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\n\n \n \n 2024\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Baseline Models for Action Recognition of Unscripted Casualty Care Dataset.\n \n \n \n \n\n\n \n Jiang, N.; Zhuo, Y.; Kirkpatrick, A., W.; Couperus, K.; Tran, O.; Beck, J.; DeVane, D.; Candelore, R.; McKee, J.; Gorbatkin, C.; Birch, E.; Colombo, C.; Duerstock, B.; and Wachs, J.\n\n\n \n\n\n\n Annual Conference on Medical Image Understanding and Analysis, pages 215-227. 2024.\n \n\n\n\n
\n\n\n\n \n \n![\"Annual](\"//bibbase.org/img/filetypes/link.svg\"\n\t "\\"Website\\"\n\t")
Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@inbook{\n type = {inbook},\n year = {2024},\n pages = {215-227},\n websites = {https://link.springer.com/10.1007/978-3-031-48593-0_16},\n id = {e97e8eaf-b934-3401-9e5b-2538d161290d},\n created = {2023-12-10T20:39:30.155Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-12-10T20:39:30.155Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inbook},\n author = {Jiang, Nina and Zhuo, Yupeng and Kirkpatrick, Andrew W. and Couperus, Kyle and Tran, Oanh and Beck, Jonah and DeVane, DeAnna and Candelore, Ross and McKee, Jessica and Gorbatkin, Chad and Birch, Eleanor and Colombo, Christopher and Duerstock, Bradley and Wachs, Juan},\n doi = {10.1007/978-3-031-48593-0_16},\n chapter = {Baseline Models for Action Recognition of Unscripted Casualty Care Dataset},\n title = {Annual Conference on Medical Image Understanding and Analysis}\n}\n\n \n\n \n \n \n \n \n \n TON-ViT: A Neuro-Symbolic AI Based on Task Oriented Network with a Vision Transformer.\n \n \n \n \n\n\n \n Zhuo, Y.; Jiang, N.; Kirkpatrick, A., W.; Couperus, K.; Tran, O.; Beck, J.; DeVane, D.; Candelore, R.; McKee, J.; Gorbatkin, C.; Birch, E.; Colombo, C.; Duerstock, B.; and Wachs, J.\n\n\n \n\n\n\n In Annual Conference on Medical Image Understanding and Analysis, pages 157-170, 2024. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {TON-ViT: A Neuro-Symbolic AI Based on Task Oriented Network with a Vision Transformer},\n type = {inproceedings},\n year = {2024},\n pages = {157-170},\n websites = {https://link.springer.com/10.1007/978-3-031-48593-0_12},\n id = {629b65ed-ba08-3bf1-9182-43c6fac3a941},\n created = {2023-12-10T20:39:30.165Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-12-10T20:39:30.165Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zhuo, Yupeng and Jiang, Nina and Kirkpatrick, Andrew W. and Couperus, Kyle and Tran, Oanh and Beck, Jonah and DeVane, DeAnna and Candelore, Ross and McKee, Jessica and Gorbatkin, Chad and Birch, Eleanor and Colombo, Christopher and Duerstock, Bradley and Wachs, Juan},\n doi = {10.1007/978-3-031-48593-0_12},\n booktitle = {Annual Conference on Medical Image Understanding and Analysis}\n}\n\n
\n\n \n \n 2023\n \n \n (4)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.\n \n \n \n \n\n\n \n Lim, C.; Barragan, J., A.; Farrow, J., M.; Wachs, J., P.; Sundaram, C., P.; and Yu, D.\n\n\n \n\n\n\n Sensors, 23(9): 4354. 4 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills},\n type = {article},\n year = {2023},\n pages = {4354},\n volume = {23},\n websites = {https://www.mdpi.com/1424-8220/23/9/4354},\n month = {4},\n day = {28},\n id = {fd94f55a-14f7-32bf-9cf3-42d9504de691},\n created = {2023-05-19T20:53:06.086Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-05-19T20:53:06.086Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.},\n bibtype = {article},\n author = {Lim, Chiho and Barragan, Juan Antonio and Farrow, Jason Michael and Wachs, Juan P. and Sundaram, Chandru P. and Yu, Denny},\n doi = {10.3390/s23094354},\n journal = {Sensors},\n number = {9}\n}\n\n Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.\n
\n\n\n\n \n\n \n \n \n \n \n \n ASAP: A Semi-Autonomous Precise System for Telesurgery During Communication Delays.\n \n \n \n \n\n\n \n Gonzalez, G.; Balakuntala, M.; Agarwal, M.; Low, T.; Knoth, B.; Kirkpatrick, A., W.; McKee, J.; Hager, G.; Aggarwal, V.; Xue, Y.; Voyles, R.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Medical Robotics and Bionics, 5(1): 66-78. 2 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n\n@article{\n title = {ASAP: A Semi-Autonomous Precise System for Telesurgery During Communication Delays},\n type = {article},\n year = {2023},\n pages = {66-78},\n volume = {5},\n websites = {https://ieeexplore.ieee.org/document/10026257/},\n month = {2},\n id = {85c83e99-5b7a-334e-acf8-76ed44f822db},\n created = {2023-05-19T20:54:53.363Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-05-19T20:54:53.363Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Gonzalez, Glebys and Balakuntala, Mythra and Agarwal, Mridul and Low, Tomas and Knoth, Bruce and Kirkpatrick, Andrew W. and McKee, Jessica and Hager, Gregory and Aggarwal, Vaneet and Xue, Yexiang and Voyles, Richard and Wachs, Juan},\n doi = {10.1109/TMRB.2023.3239674},\n journal = {IEEE Transactions on Medical Robotics and Bionics},\n number = {1}\n}\n\n \n\n \n \n \n \n \n \n Trauma THOMPSON: Clinical Decision Support for the Frontline Medic.\n \n \n \n \n\n\n \n Birch, E.; Couperus, K.; Gorbatkin, C.; Kirkpatrick, A., W.; Wachs, J.; Candelore, R.; Jiang, N.; Tran, O.; Beck, J.; Couperus, C.; McKee, J.; Curlett, T.; DeVane, D.; and Colombo, C.\n\n\n \n\n\n\n Military Medicine, 188(Supplement_6): 208-214. 11 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n\n@article{\n title = {Trauma THOMPSON: Clinical Decision Support for the Frontline Medic},\n type = {article},\n year = {2023},\n pages = {208-214},\n volume = {188},\n websites = {https://academic.oup.com/milmed/article/188/Supplement_6/208/7388257},\n month = {11},\n day = {8},\n id = {5810d74f-04ca-342f-8b59-16bf71042f41},\n created = {2023-11-17T21:26:04.454Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-11-17T21:26:04.454Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Birch, Eleanor and Couperus, Kyle and Gorbatkin, Chad and Kirkpatrick, Andrew W and Wachs, Juan and Candelore, Ross and Jiang, Nina and Tran, Oanh and Beck, Jonah and Couperus, Cody and McKee, Jessica and Curlett, Timothy and DeVane, DeAnna and Colombo, Christopher},\n doi = {10.1093/milmed/usad087},\n journal = {Military Medicine},\n number = {Supplement_6}\n}\n\n \n\n \n \n \n \n \n \n Considering human cognitive architecture in stressful medical prehospital interventions might benefit care providers.\n \n \n \n \n\n\n \n Kirkpatrick, A., W.; McKee, J., L.; Barrett, R.; Couperus, K.; and Wachs, J.\n\n\n \n\n\n\n Canadian Journal of Surgery, 66(6): E532-E534. 11 2023.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {Considering human cognitive architecture in stressful medical prehospital interventions might benefit care providers},\n type = {article},\n year = {2023},\n pages = {E532-E534},\n volume = {66},\n websites = {http://www.canjsurg.ca/lookup/doi/10.1503/cjs.015422},\n month = {11},\n day = {1},\n id = {d9455ebe-7da5-3be0-95bc-18a8b55bc935},\n created = {2023-11-17T21:31:06.140Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-11-17T21:31:06.140Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Kirkpatrick, Andrew W. and McKee, Jessica L. and Barrett, Robert and Couperus, Kyle and Wachs, Juan},\n doi = {10.1503/cjs.015422},\n journal = {Canadian Journal of Surgery},\n number = {6}\n}\n\n
\n\n \n \n 2022\n \n \n (9)\n \n \n
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\n \n \n\n \n \n \n \n \n \n To Watch Before or Listen While Doing? A Randomized Pilot of Video-Modelling versus Telementored Tube Thoracostomy.\n \n \n \n \n\n\n \n Kirkpatrick, A., W.; Tomlinson, C.; Donley, N.; McKee, J., L.; Ball, C., G.; and Wachs, J., P.\n\n\n \n\n\n\n Prehospital and Disaster Medicine, 37(1): 71-77. 2 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {To Watch Before or Listen While Doing? A Randomized Pilot of Video-Modelling versus Telementored Tube Thoracostomy},\n type = {article},\n year = {2022},\n pages = {71-77},\n volume = {37},\n websites = {https://www.cambridge.org/core/product/identifier/S1049023X22000097/type/journal_article},\n month = {2},\n day = {18},\n id = {483331f5-d0fe-3d81-b432-ba9cf31e755d},\n created = {2022-03-17T22:44:08.912Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.061Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Kirkpatrick, Andrew W. and Tomlinson, Corey and Donley, Nigel and McKee, Jessica L. and Ball, Chad G. and Wachs, Juan P.},\n doi = {10.1017/S1049023X22000097},\n journal = {Prehospital and Disaster Medicine},\n number = {1}\n}\n\n \n\n \n \n \n \n \n \n Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization.\n \n \n \n \n\n\n \n Xiao, C.; and Wachs, J.\n\n\n \n\n\n\n IEEE Robotics and Automation Letters, 7(2): 1768-1775. 4 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization},\n type = {article},\n year = {2022},\n pages = {1768-1775},\n volume = {7},\n websites = {https://ieeexplore.ieee.org/document/9676466/},\n month = {4},\n id = {5077c49e-d7e4-3f6d-94b4-6fa2f794d70f},\n created = {2022-03-17T22:46:29.828Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.083Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Xiao, Chenxi and Wachs, Juan},\n doi = {10.1109/LRA.2022.3141458},\n journal = {IEEE Robotics and Automation Letters},\n number = {2}\n}\n\n \n\n \n \n \n \n \n \n A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery.\n \n \n \n \n\n\n \n Barragan, J., A.; Yang, J.; Yu, D.; and Wachs, J., P.\n\n\n \n\n\n\n Scientific Reports, 12(1): 4504. 12 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery},\n type = {article},\n year = {2022},\n pages = {4504},\n volume = {12},\n websites = {https://www.nature.com/articles/s41598-022-08063-w},\n month = {12},\n day = {16},\n id = {9ca02f24-a79a-39a0-b5f8-db378a9bfd07},\n created = {2022-03-17T23:00:11.974Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.189Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.},\n bibtype = {article},\n author = {Barragan, Juan Antonio and Yang, Jing and Yu, Denny and Wachs, Juan P.},\n doi = {10.1038/s41598-022-08063-w},\n journal = {Scientific Reports},\n number = {1}\n}\n\n Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.\n
\n\n\n\n \n\n \n \n \n \n \n \n A randomized controlled pilot trial of video-modelling versus telementoring for improved hemorrhage control wound packing.\n \n \n \n \n\n\n \n Kirkpatrick, M., W.; McKee, J., L.; Tomlinson, M., C.; Donley, M., N.; Ball, C., G.; and Wachs, J.\n\n\n \n\n\n\n The American Journal of Surgery. 3 2022.\n \n\n\n\n
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\n\n\n\n@article{\n title = {A randomized controlled pilot trial of video-modelling versus telementoring for improved hemorrhage control wound packing},\n type = {article},\n year = {2022},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0002961022000976},\n month = {3},\n id = {9ff0e1b2-2332-3443-9d3b-26852d059142},\n created = {2022-03-21T15:16:43.891Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-04-01T19:46:21.601Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Kirkpatrick, MajorAndrew W. and McKee, Jessica L. and Tomlinson, Major Corey and Donley, MCpl Nigel and Ball, Chad G. and Wachs, Juan},\n doi = {10.1016/j.amjsurg.2022.02.039},\n journal = {The American Journal of Surgery}\n}\n\n \n\n \n \n \n \n \n \n Touchless Interfaces in the Operating Room: A Study in Gesture Preferences.\n \n \n \n \n\n\n \n Madapana, N.; Chanci, D.; Gonzalez, G.; Zhang, L.; and Wachs, J., P.\n\n\n \n\n\n\n International Journal of Human–Computer Interaction,1-11. 4 2022.\n \n\n\n\n
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\n\n\n@article{\n title = {Touchless Interfaces in the Operating Room: A Study in Gesture Preferences},\n type = {article},\n year = {2022},\n pages = {1-11},\n websites = {https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2041896},\n month = {4},\n publisher = {Taylor & Francis},\n day = {18},\n id = {935025df-684e-3858-95a5-da2b2118128d},\n created = {2022-06-03T20:18:44.392Z},\n accessed = {2022-06-03},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-06-03T20:25:03.380Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Touchless interfaces allow surgeons to control medical imaging systems autonomously while maintaining total asepsis in the Operating Room. This is specially relevant as it applies to the recent outbreak of COVID-19 disease. The choice of best gestures/commands for such interfaces is a critical step that determines the overall efficiency of surgeon-computer interaction. In this regard, usability metrics such as task completion time, memorability and error rate have a long-standing as potential entities in determining the best gestures. In addition, previous works concerned with this problem utilized qualitative measures to identify the best gestures. In this work, we hypothesize that there is a correlation between gestures' qualitative properties and their usability metrics. In this regard, we conducted a user experiment with language experts to quantify gestures' properties (v). Next, we developed a gesture-based system that facilitates surgeons to control the medical imaging software in a touchless manner. Next, a usability study was conducted with neurosurgeons and the standard usability metrics (u) were measured in a systematic manner. Lastly, multi-variate correlation analysis was used to find the relations between u and v. Statistical analysis showed that the v scores were significantly correlated with the usability metrics with an R 2 % 0:40 and p < 0.05. Once the correlation is established, we can utilize either gestures' qualitative properties or usability metrics to identify the best set of gestures.},\n bibtype = {article},\n author = {Madapana, Naveen and Chanci, Daniela and Gonzalez, Glebys and Zhang, Lingsong and Wachs, Juan P.},\n doi = {10.1080/10447318.2022.2041896},\n journal = {International Journal of Human–Computer Interaction}\n}\n\n Touchless interfaces allow surgeons to control medical imaging systems autonomously while maintaining total asepsis in the Operating Room. This is specially relevant as it applies to the recent outbreak of COVID-19 disease. The choice of best gestures/commands for such interfaces is a critical step that determines the overall efficiency of surgeon-computer interaction. In this regard, usability metrics such as task completion time, memorability and error rate have a long-standing as potential entities in determining the best gestures. In addition, previous works concerned with this problem utilized qualitative measures to identify the best gestures. In this work, we hypothesize that there is a correlation between gestures' qualitative properties and their usability metrics. In this regard, we conducted a user experiment with language experts to quantify gestures' properties (v). Next, we developed a gesture-based system that facilitates surgeons to control the medical imaging software in a touchless manner. Next, a usability study was conducted with neurosurgeons and the standard usability metrics (u) were measured in a systematic manner. Lastly, multi-variate correlation analysis was used to find the relations between u and v. Statistical analysis showed that the v scores were significantly correlated with the usability metrics with an R 2 % 0:40 and p < 0.05. Once the correlation is established, we can utilize either gestures' qualitative properties or usability metrics to identify the best set of gestures.\n
\n\n\n\n \n\n \n \n \n \n \n \n Robotically applied hemostatic clamping for care-under-fire: harnessing bomb robots for hemorrhage control.\n \n \n \n \n\n\n \n Kirkpatrick, A., W.; McKee, I., A.; Knudsen, B.; Shelton, R.; LaPorta, A., J.; Wachs, J.; and McKee, J., L.\n\n\n \n\n\n\n Canadian Journal of Surgery, 65(2): E242-E249. 4 2022.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Robotically applied hemostatic clamping for care-under-fire: harnessing bomb robots for hemorrhage control},\n type = {article},\n year = {2022},\n pages = {E242-E249},\n volume = {65},\n websites = {http://www.canjsurg.ca/lookup/doi/10.1503/cjs.009920},\n month = {4},\n publisher = {Canadian Medical Association},\n day = {1},\n id = {b640ac21-e4d4-3b15-97f9-0ca095684b1a},\n created = {2022-06-03T20:20:44.654Z},\n accessed = {2022-06-03},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-06-03T20:25:03.378Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Background: Early hemorrhage control after interpersonal violence is the most urgent requirement to preserve life and is now recognized as a responsibility of law enforcement. Although earlier entry of first responders is advocated, many shooting scenes remain unsafe for humans, necessitating first responses conducted by robots. Thus, robotic hemorrhage control warrants study as a care-under-fire treatment option. Methods: Two bomb disposal robots (Wolverine and Dragon Runner) were retro fitted with hemostatic wound clamps. The robots' ability to apply a wound clamp to a simulated extremity exsanguination while controlled by 4 experienced operators was tested. The operators were randomly assigned to perform 10 trials using 1 robot each. A third surveillance robot (Stair Climber) provided further visualization for the operators. We assessed the success rate of the application of the wound clamp to the simulated wound, the time to application of the wound clamp and the amount of fluid loss. We also assessed the opera-tors' efforts to apply the wound clamp after an initial attempt was unsuccessful or after the wound clamp was dropped. Results: Remote robotic application of a wound clamp was demonstrated to be feasible, with complete cessation of simulated bleeding in 60% of applications. This finding was consistent across all operators and both robots. There was no difference in the success rates with the 2 robots (p = 1.00). However, there were differences in fluid loss (p = 0.004) and application time (p < 0.001), with the larger (Wolverine) robot being faster and losing less fluid. Conclusion: Law enforcement tactical robots were consistently able to provide partial to complete hemorrhage control in a simulated extremity exsanguination. Consideration should be given to using this approach in care-under-fire and care-behind-the-barricade scenarios as well as further developing the technology and doctrine for robotic hemorrhage control.},\n bibtype = {article},\n author = {Kirkpatrick, Andrew W. and McKee, Ian A. and Knudsen, Brian and Shelton, Ryan and LaPorta, Anthony J. and Wachs, Juan and McKee, Jessica L.},\n doi = {10.1503/cjs.009920},\n journal = {Canadian Journal of Surgery},\n number = {2}\n}\n\n Background: Early hemorrhage control after interpersonal violence is the most urgent requirement to preserve life and is now recognized as a responsibility of law enforcement. Although earlier entry of first responders is advocated, many shooting scenes remain unsafe for humans, necessitating first responses conducted by robots. Thus, robotic hemorrhage control warrants study as a care-under-fire treatment option. Methods: Two bomb disposal robots (Wolverine and Dragon Runner) were retro fitted with hemostatic wound clamps. The robots' ability to apply a wound clamp to a simulated extremity exsanguination while controlled by 4 experienced operators was tested. The operators were randomly assigned to perform 10 trials using 1 robot each. A third surveillance robot (Stair Climber) provided further visualization for the operators. We assessed the success rate of the application of the wound clamp to the simulated wound, the time to application of the wound clamp and the amount of fluid loss. We also assessed the opera-tors' efforts to apply the wound clamp after an initial attempt was unsuccessful or after the wound clamp was dropped. Results: Remote robotic application of a wound clamp was demonstrated to be feasible, with complete cessation of simulated bleeding in 60% of applications. This finding was consistent across all operators and both robots. There was no difference in the success rates with the 2 robots (p = 1.00). However, there were differences in fluid loss (p = 0.004) and application time (p < 0.001), with the larger (Wolverine) robot being faster and losing less fluid. Conclusion: Law enforcement tactical robots were consistently able to provide partial to complete hemorrhage control in a simulated extremity exsanguination. Consideration should be given to using this approach in care-under-fire and care-behind-the-barricade scenarios as well as further developing the technology and doctrine for robotic hemorrhage control.\n
\n\n\n\n \n\n \n \n \n \n \n \n JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J., P.\n\n\n \n\n\n\n IEEE Transactions on Artificial Intelligence,1-12. 2022.\n \n\n\n\n
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\n\n\n\n@article{\n title = {JSSE: Joint Sequential Semantic Encoder for Zero-Shot Event Recognition},\n type = {article},\n year = {2022},\n pages = {1-12},\n websites = {https://ieeexplore.ieee.org/document/9900410/},\n id = {a01043d8-37aa-35d9-834a-13277dde9122},\n created = {2022-11-23T02:16:35.723Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:16:35.723Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Madapana, Naveen and Wachs, Juan P.},\n doi = {10.1109/TAI.2022.3208860},\n journal = {IEEE Transactions on Artificial Intelligence}\n}\n\n \n\n \n \n \n \n \n \n Active Multiobject Exploration and Recognition via Tactile Whiskers.\n \n \n \n \n\n\n \n Xiao, C.; Xu, S.; Wu, W.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Robotics,1-19. 2022.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Active Multiobject Exploration and Recognition via Tactile Whiskers},\n type = {article},\n year = {2022},\n pages = {1-19},\n websites = {https://ieeexplore.ieee.org/document/9813357/},\n id = {5036e6c3-83b5-380d-a57c-9583673d167c},\n created = {2022-11-23T13:45:49.820Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T13:45:49.820Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Xiao, Chenxi and Xu, Shujia and Wu, Wenzhuo and Wachs, Juan},\n doi = {10.1109/TRO.2022.3182487},\n journal = {IEEE Transactions on Robotics}\n}\n\n \n\n \n \n \n \n \n \n An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing—Demonstration of a Semi-autonomous Suction Tool.\n \n \n \n \n\n\n \n Yang, J.; Barragan, J., A.; Farrow, J., M.; Sundaram, C., P.; Wachs, J., P.; and Yu, D.\n\n\n \n\n\n\n Human Factors: The Journal of the Human Factors and Ergonomics Society,001872082211299. 11 2022.\n \n\n\n\n
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\n\n\n\n@article{\n title = {An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing—Demonstration of a Semi-autonomous Suction Tool},\n type = {article},\n year = {2022},\n pages = {001872082211299},\n websites = {http://journals.sagepub.com/doi/10.1177/00187208221129940},\n month = {11},\n day = {11},\n id = {e6583947-f13b-33d5-84de-618274462786},\n created = {2023-05-19T21:00:28.598Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-05-19T21:00:28.598Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Yang, Jing and Barragan, Juan Antonio and Farrow, Jason Michael and Sundaram, Chandru P. and Wachs, Juan P. and Yu, Denny},\n doi = {10.1177/00187208221129940},\n journal = {Human Factors: The Journal of the Human Factors and Ergonomics Society}\n}\n\n
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\n \n \n\n \n \n \n \n \n \n Triangle-Net: Towards Robustness in Point Cloud Learning.\n \n \n \n \n\n\n \n Xiao, C.; and Wachs, J.\n\n\n \n\n\n\n In The IEEE Winter Conference on Applications of Computer Vision, pages 826-835, 2021. \n \n\n\n\n
\n\n\n\n \n \n![\"Triangle-Net:](\"//bibbase.org/img/filetypes/pdf.svg\"\n\t "\\"Paper\\"\n\t")
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\n\n\n@inproceedings{\n title = {Triangle-Net: Towards Robustness in Point Cloud Learning},\n type = {inproceedings},\n year = {2021},\n pages = {826-835},\n id = {5a622f45-e5d7-3c5d-9840-6e45ad7263cf},\n created = {2020-11-17T19:56:54.244Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T17:49:32.642Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Three dimensional (3D) object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust to various sampling resolutions, noisy measurements, and unconstrained pose configurations. Previous research has shown that points' sparsity, rotation and positional inherent variance can lead to a significant drop in the performance of point cloud based classification techniques. However, neither of them is sufficiently robust to multifactorial variance and significant sparsity. In this regard, we propose a novel approach for 3D classification that can simultaneously achieve invariance towards rotation, positional shift, scaling, and is robust to point sparsity. To this end, we introduce a new feature that utilizes graph structure of point clouds, which can be learned end-to-end with our proposed neural network to acquire a robust latent representation of the 3D object. We show that such latent representations can significantly improve the performance of object classification and retrieval tasks when points are sparse. Further, we show that our approach outperforms PointNet and 3DmFV by 35.0% and 28.1% respectively in ModelNet 40 classification tasks using sparse point clouds of only 16 points under arbitrary SO(3) rotation.},\n bibtype = {inproceedings},\n author = {Xiao, C. and Wachs, JP},\n booktitle = {The IEEE Winter Conference on Applications of Computer Vision}\n}\n\n Three dimensional (3D) object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust to various sampling resolutions, noisy measurements, and unconstrained pose configurations. Previous research has shown that points' sparsity, rotation and positional inherent variance can lead to a significant drop in the performance of point cloud based classification techniques. However, neither of them is sufficiently robust to multifactorial variance and significant sparsity. In this regard, we propose a novel approach for 3D classification that can simultaneously achieve invariance towards rotation, positional shift, scaling, and is robust to point sparsity. To this end, we introduce a new feature that utilizes graph structure of point clouds, which can be learned end-to-end with our proposed neural network to acquire a robust latent representation of the 3D object. We show that such latent representations can significantly improve the performance of object classification and retrieval tasks when points are sparse. Further, we show that our approach outperforms PointNet and 3DmFV by 35.0% and 28.1% respectively in ModelNet 40 classification tasks using sparse point clouds of only 16 points under arbitrary SO(3) rotation.\n
\n\n\n\n \n\n \n \n \n \n \n \n From the Dexterous Surgical Skill to the Battlefield—A Robotics Exploratory Study.\n \n \n \n \n\n\n \n Gonzalez, G., T.; Kaur, U.; Rahman, M.; Venkatesh, V.; Sanchez, N.; Hager, G.; Xue, Y.; Voyles, R.; and Wachs, J.\n\n\n \n\n\n\n Military Medicine, 186(Supplement_1): 288-294. 1 2021.\n \n\n\n\n
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Paper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {From the Dexterous Surgical Skill to the Battlefield—A Robotics Exploratory Study},\n type = {article},\n year = {2021},\n pages = {288-294},\n volume = {186},\n websites = {https://academic.oup.com/milmed/article/186/Supplement_1/288/6119495},\n month = {1},\n day = {25},\n id = {be891970-9ce1-343d-8ffd-e184ba2c3504},\n created = {2021-02-14T23:59:00.000Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-15T21:08:21.039Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {true},\n abstract = {© The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. INTRODUCTION: Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. METHODS: A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. RESULTS: Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. CONCLUSIONS: The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas.},\n bibtype = {article},\n author = {Gonzalez, Glebys T and Kaur, Upinder and Rahman, Masudur and Venkatesh, Vishnunandan and Sanchez, Natalia and Hager, Gregory and Xue, Yexiang and Voyles, Richard and Wachs, Juan},\n doi = {10.1093/milmed/usaa253},\n journal = {Military Medicine},\n number = {Supplement_1}\n}\n\n © The Association of Military Surgeons of the United States 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. INTRODUCTION: Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. METHODS: A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. RESULTS: Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. CONCLUSIONS: The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas.\n
\n\n\n\n \n\n \n \n \n \n \n \n Assessing Collaborative Physical Tasks Via Gestural Analysis.\n \n \n \n \n\n\n \n Rojas-Munoz, E.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Human-Machine Systems, 51(2): 152-161. 4 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Assessing Collaborative Physical Tasks Via Gestural Analysis},\n type = {article},\n year = {2021},\n keywords = {Collaboration,gestures,knowledge representa-tion,task understanding},\n pages = {152-161},\n volume = {51},\n websites = {https://ieeexplore.ieee.org/document/9350223/},\n month = {4},\n id = {975ef13f-684a-352e-afde-f44590ca595a},\n created = {2021-06-04T19:36:51.163Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.627Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Recent studies have shown that gestures are useful indicators of understanding, learning, and memory retention. However, and specially in collaborative settings, current metrics that estimate task understanding often neglect the information expressed through gestures. This work introduces the physical instruction assimilation (PIA) metric, a novel approach to estimate task understanding by analyzing the way in which collaborators use gestures to convey, assimilate, and execute physical instructions. PIA estimates task understanding by inspecting the number of necessary gestures required to complete a shared task. PIA is calculated based on the multiagent gestural instruction comparer (MAGIC) architecture, a previously proposed framework to represent, assess, and compare gestures. To evaluate our metric, we collected gestures from collaborators remotely completing the following three tasks: block assembly, origami, and ultrasound training. The PIA scores of these individuals are compared against two other metrics used to estimate task understanding: number of errors and amount of idle time during the task. Statistically significant correlations between PIA and these metrics are found. Additionally, a Taguchi design is used to evaluate PIA's sensitivity to changes in the MAGIC architecture. The factors evaluated the effect of changes in time, order, and motion trajectories of the collaborators' gestures. PIA is shown to be robust to these changes, having an average mean change of 0.45. These results hint that gestures, in the form of the assimilation of physical instructions, can reveal insights of task understanding and complement other commonly used metrics.},\n bibtype = {article},\n author = {Rojas-Munoz, Edgar and Wachs, Juan},\n doi = {10.1109/THMS.2021.3051305},\n journal = {IEEE Transactions on Human-Machine Systems},\n number = {2}\n}\n\n Recent studies have shown that gestures are useful indicators of understanding, learning, and memory retention. However, and specially in collaborative settings, current metrics that estimate task understanding often neglect the information expressed through gestures. This work introduces the physical instruction assimilation (PIA) metric, a novel approach to estimate task understanding by analyzing the way in which collaborators use gestures to convey, assimilate, and execute physical instructions. PIA estimates task understanding by inspecting the number of necessary gestures required to complete a shared task. PIA is calculated based on the multiagent gestural instruction comparer (MAGIC) architecture, a previously proposed framework to represent, assess, and compare gestures. To evaluate our metric, we collected gestures from collaborators remotely completing the following three tasks: block assembly, origami, and ultrasound training. The PIA scores of these individuals are compared against two other metrics used to estimate task understanding: number of errors and amount of idle time during the task. Statistically significant correlations between PIA and these metrics are found. Additionally, a Taguchi design is used to evaluate PIA's sensitivity to changes in the MAGIC architecture. The factors evaluated the effect of changes in time, order, and motion trajectories of the collaborators' gestures. PIA is shown to be robust to these changes, having an average mean change of 0.45. These results hint that gestures, in the form of the assimilation of physical instructions, can reveal insights of task understanding and complement other commonly used metrics.\n
\n\n\n\n \n\n \n \n \n \n \n \n Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives.\n \n \n \n \n\n\n \n Wachs, J., P.; Kirkpatrick, A., W.; and Tisherman, S., A.\n\n\n \n\n\n\n Annual Review of Biomedical Engineering, 23(1): 115-139. 7 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives},\n type = {article},\n year = {2021},\n pages = {115-139},\n volume = {23},\n websites = {https://www.annualreviews.org/doi/10.1146/annurev-bioeng-083120-023315},\n month = {7},\n day = {13},\n id = {20ab6786-cbd5-3452-ae0e-aae2d3fdb28d},\n created = {2021-06-04T19:36:51.164Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.227Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telemedicine is perhaps the most rapidly growing area in health care. Approximately 15 million Americans receive medical assistance remotely every year. Yet rural communities face significant challenges in securing subspecialist care. In the United States, 25% of the population resides in rural areas, where less than 15% of physicians work. Current surgery residency programs do not adequately prepare surgeons for rural practice. Telementoring, wherein a remote expert guides a less experienced caregiver, has been proposed to address this challenge. Nonetheless, existing mentoring technologies are not widely available to rural communities, due to a lack of infrastructure and mentor availability. For this reason, some clinicians prefer simpler and more reliable technologies. This article presents past and current telementoring systems, with a focus on rural settings, and proposes aset of requirements for such systems. We conclude with a perspective on the future of telementoring systems and the integration of artificial intelligence within those systems.},\n bibtype = {article},\n author = {Wachs, Juan P. and Kirkpatrick, Andrew W. and Tisherman, Samuel A.},\n doi = {10.1146/annurev-bioeng-083120-023315},\n journal = {Annual Review of Biomedical Engineering},\n number = {1}\n}\n\n Telemedicine is perhaps the most rapidly growing area in health care. Approximately 15 million Americans receive medical assistance remotely every year. Yet rural communities face significant challenges in securing subspecialist care. In the United States, 25% of the population resides in rural areas, where less than 15% of physicians work. Current surgery residency programs do not adequately prepare surgeons for rural practice. Telementoring, wherein a remote expert guides a less experienced caregiver, has been proposed to address this challenge. Nonetheless, existing mentoring technologies are not widely available to rural communities, due to a lack of infrastructure and mentor availability. For this reason, some clinicians prefer simpler and more reliable technologies. This article presents past and current telementoring systems, with a focus on rural settings, and proposes aset of requirements for such systems. We conclude with a perspective on the future of telementoring systems and the integration of artificial intelligence within those systems.\n
\n\n\n\n \n\n \n \n \n \n \n \n Fingers See Things Differently (FIST-D): An Object Aware Visualization and Manipulation Framework Based on Tactile Observations.\n \n \n \n \n\n\n \n Xiao, C.; Madapana, N.; and Wachs, J.\n\n\n \n\n\n\n IEEE Robotics and Automation Letters, 6(3): 4249-4256. 7 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Fingers See Things Differently (FIST-D): An Object Aware Visualization and Manipulation Framework Based on Tactile Observations},\n type = {article},\n year = {2021},\n keywords = {Deep learning in grasping and manipulation,force and tactile sensing,perception for grasping and manipulation},\n pages = {4249-4256},\n volume = {6},\n websites = {https://ieeexplore.ieee.org/document/9372831/},\n month = {7},\n id = {2badbc94-3ecb-31ba-9be4-9094f1d7cfe2},\n created = {2021-06-04T19:36:51.379Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T13:55:44.298Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Planning object manipulation policies based on tactile observations alone is a challenging task due to the multi-factorial variances in the measured point cloud (e.g. sparsity, missing regions, rotation, etc.) and the limited sensory information available through tactile sensing. Nevertheless, the mainstream grasp planners are designed for well-structured point cloud data, and lack the crucial ability to plan grasps in unexplored regions that are common during tactile sampling. Hence, it is crucial to detect the grasp regions from incomplete and unstructured tactile point cloud data. To address this limitation, we propose a novel framework that utilizes a support set of CAD models to augment the tactile observations, and thereby facilitate object recognition, visualization, and developing manipulation policies solely using tactile samples. To cope with the noise and sparsity of tactile observations, we propose uGPIS, a surface reconstruction method that utilizes the occupancy possibility function and the Gaussian Process Regression to recover the underlying surface from tactile point clouds. Then, we complete the partially observed tactile point cloud using the prior knowledge obtained from the support set of full CAD models. This prior information will provide the enriched geometric information that is crucial to determine the grasp regions. Our experimental results on a physical simulation show that our method can successfully combine the prior knowledge from the database to enhance the grasp success rate.},\n bibtype = {article},\n author = {Xiao, Chenxi and Madapana, Naveen and Wachs, Juan},\n doi = {10.1109/LRA.2021.3064211},\n journal = {IEEE Robotics and Automation Letters},\n number = {3}\n}\n\n Planning object manipulation policies based on tactile observations alone is a challenging task due to the multi-factorial variances in the measured point cloud (e.g. sparsity, missing regions, rotation, etc.) and the limited sensory information available through tactile sensing. Nevertheless, the mainstream grasp planners are designed for well-structured point cloud data, and lack the crucial ability to plan grasps in unexplored regions that are common during tactile sampling. Hence, it is crucial to detect the grasp regions from incomplete and unstructured tactile point cloud data. To address this limitation, we propose a novel framework that utilizes a support set of CAD models to augment the tactile observations, and thereby facilitate object recognition, visualization, and developing manipulation policies solely using tactile samples. To cope with the noise and sparsity of tactile observations, we propose uGPIS, a surface reconstruction method that utilizes the occupancy possibility function and the Gaussian Process Regression to recover the underlying surface from tactile point clouds. Then, we complete the partially observed tactile point cloud using the prior knowledge obtained from the support set of full CAD models. This prior information will provide the enriched geometric information that is crucial to determine the grasp regions. Our experimental results on a physical simulation show that our method can successfully combine the prior knowledge from the database to enhance the grasp success rate.\n
\n\n\n\n \n\n \n \n \n \n \n \n Pose-based Sign Language Recognition using GCN and BERT.\n \n \n \n \n\n\n \n Tunga, A.; Nuthalapati, S., V.; and Wachs, J.\n\n\n \n\n\n\n In 2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW), pages 31-40, 1 2021. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Pose-based Sign Language Recognition using GCN and BERT},\n type = {inproceedings},\n year = {2021},\n pages = {31-40},\n websites = {https://ieeexplore.ieee.org/document/9407595/},\n month = {1},\n publisher = {IEEE},\n id = {0699961b-213b-397d-8ac7-112c9768a2f3},\n created = {2021-06-04T19:36:51.508Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-15T21:08:18.590Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Sign language recognition (SLR) plays a crucial role in bridging the communication gap between the hearing and vocally impaired community and the rest of the society. Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. However, recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations and other movements. Recent pose-based architectures for WSLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information.We tackle the problem of WSLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion. Our proposed architecture explicitly captures the spatial interactions in the video using a Graph Convolutional Network (GCN). The temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT). Experimental results on WLASL, a standard word-level sign language recognition dataset show that our model significantly outperforms the state-of-the-art on pose-based methods by achieving an improvement in the prediction accuracy by up to 5%.},\n bibtype = {inproceedings},\n author = {Tunga, Anirudh and Nuthalapati, Sai Vidyaranya and Wachs, Juan},\n doi = {10.1109/WACVW52041.2021.00008},\n booktitle = {2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)}\n}\n\n Sign language recognition (SLR) plays a crucial role in bridging the communication gap between the hearing and vocally impaired community and the rest of the society. Word-level sign language recognition (WSLR) is the first important step towards understanding and interpreting sign language. However, recognizing signs from videos is a challenging task as the meaning of a word depends on a combination of subtle body motions, hand configurations and other movements. Recent pose-based architectures for WSLR either model both the spatial and temporal dependencies among the poses in different frames simultaneously or only model the temporal information without fully utilizing the spatial information.We tackle the problem of WSLR using a novel pose-based approach, which captures spatial and temporal information separately and performs late fusion. Our proposed architecture explicitly captures the spatial interactions in the video using a Graph Convolutional Network (GCN). The temporal dependencies between the frames are captured using Bidirectional Encoder Representations from Transformers (BERT). Experimental results on WLASL, a standard word-level sign language recognition dataset show that our model significantly outperforms the state-of-the-art on pose-based methods by achieving an improvement in the prediction accuracy by up to 5%.\n
\n\n\n\n \n\n \n \n \n \n \n \n One-Shot Image Recognition Using Prototypical Encoders with Reduced Hubness.\n \n \n \n \n\n\n \n Xiao, C.; Madapana, N.; and Wachs, J., P.\n\n\n \n\n\n\n In The IEEE Winter Conference on Applications of Computer Vision., pages 2252-2261, 2021. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {One-Shot Image Recognition Using Prototypical Encoders with Reduced Hubness.},\n type = {inproceedings},\n year = {2021},\n pages = {2252-2261},\n id = {b8e317bc-15c3-30a8-80cb-220c1f16b430},\n created = {2021-06-04T19:36:51.704Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-15T20:40:59.774Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Xiao2021},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Humans have the innate ability to recognize new objects just by looking at sketches of them (also referred as to prototype images). Similarly, prototypical images can be used as an effective visual representations of unseen classes to tackle few-shot learning (FSL) tasks. Our main goal is to recognize unseen hand signs (gestures) traffic-signs, and corporate-logos, by having their iconographic images or prototypes. Previous works proposed to utilize variational prototypical-encoders (VPE) to address FSL problems. While VPE learns an image-to-image translation task efficiently, we discovered that its performance is significantly hampered by the so-called hubness problem and it fails to regulate the representations in the latent space. Hence, we propose a new model (VPE++) that inherently reduces hubness and incorporates contrastive and multi-task losses to increase the discrimina-tive ability of FSL models. Results show that the VPE++ approach can generalize better to the unseen classes and can achieve superior accuracies on logos, traffic signs, and hand gestures datasets as compared to the state-of-the-art.},\n bibtype = {inproceedings},\n author = {Xiao, Chenxi and Madapana, Naveen and Wachs, Juan Pablo},\n booktitle = {The IEEE Winter Conference on Applications of Computer Vision.}\n}\n\n Humans have the innate ability to recognize new objects just by looking at sketches of them (also referred as to prototype images). Similarly, prototypical images can be used as an effective visual representations of unseen classes to tackle few-shot learning (FSL) tasks. Our main goal is to recognize unseen hand signs (gestures) traffic-signs, and corporate-logos, by having their iconographic images or prototypes. Previous works proposed to utilize variational prototypical-encoders (VPE) to address FSL problems. While VPE learns an image-to-image translation task efficiently, we discovered that its performance is significantly hampered by the so-called hubness problem and it fails to regulate the representations in the latent space. Hence, we propose a new model (VPE++) that inherently reduces hubness and incorporates contrastive and multi-task losses to increase the discrimina-tive ability of FSL models. Results show that the VPE++ approach can generalize better to the unseen classes and can achieve superior accuracies on logos, traffic signs, and hand gestures datasets as compared to the state-of-the-art.\n
\n\n\n\n \n\n \n \n \n \n \n \n Sensor-based indicators of performance changes between sessions during robotic surgery training.\n \n \n \n \n\n\n \n Wu, C.; Cha, J.; Sulek, J.; Sundaram, C., P.; Wachs, J.; Proctor, R., W.; and Yu, D.\n\n\n \n\n\n\n Applied Ergonomics, 90: 103251. 1 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Sensor-based indicators of performance changes between sessions during robotic surgery training},\n type = {article},\n year = {2021},\n keywords = {Electroencephalogram,Eye tracking,Performance,Robotic surgery,Simulated training},\n pages = {103251},\n volume = {90},\n websites = {https://linkinghub.elsevier.com/retrieve/pii/S0003687020302015},\n month = {1},\n id = {676bebf3-c442-3e19-b47e-ce79ef943775},\n created = {2021-06-04T19:36:51.908Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-16T19:52:00.662Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm=−.25,p<.001) and gaze entropy (rrm=−.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.},\n bibtype = {article},\n author = {Wu, Chuhao and Cha, Jackie and Sulek, Jay and Sundaram, Chandru P. and Wachs, Juan and Proctor, Robert W. and Yu, Denny},\n doi = {10.1016/j.apergo.2020.103251},\n journal = {Applied Ergonomics}\n}\n\n Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm=−.25,p<.001) and gaze entropy (rrm=−.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.\n
\n\n\n\n \n\n \n \n \n \n \n \n JSE: Joint Semantic Encoder for zero-shot gesture learning.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J.\n\n\n \n\n\n\n Pattern Analysis and Applications. 6 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {JSE: Joint Semantic Encoder for zero-shot gesture learning},\n type = {article},\n year = {2021},\n websites = {https://link.springer.com/10.1007/s10044-021-00992-y},\n month = {6},\n day = {11},\n id = {2d1a9797-585e-3c9b-aa29-871713bf2cac},\n created = {2021-06-14T13:23:38.553Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-14T22:34:20.061Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Zero-shot learning (ZSL) is a transfer learning paradigm that aims to recognize unseen categories just by having a high-level description of them. While deep learning has greatly pushed the limits of ZSL for object classification, ZSL for gesture recognition (ZSGL) remains largely unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL. It is indisputable that deep learning has obviated the need for feature engineering for problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (velocity, heuristical, and latent features) on the performance of ZSGL. In addition, we propose a bi-linear autoencoder approach, referred to as Joint Semantic Encoder (JSE), for ZSGL that jointly minimizes the reconstruction, semantic and classification losses.We conducted extensive experiments to compare and contrast the feature extraction techniques and to evaluate the performance of JSE with respect to existing ZSL methods. For attribute-based classification scenario, irrespective of the feature type, results showed that JSE outperforms other approaches by 5% (p < 0:01) on an average. When JSE is trained with heuristical features in across-category condition, we showed that JSE significantly outperforms other methods by approximately 5% (p < 0:01).},\n bibtype = {article},\n author = {Madapana, Naveen and Wachs, Juan},\n doi = {10.1007/s10044-021-00992-y},\n journal = {Pattern Analysis and Applications}\n}\n\n Zero-shot learning (ZSL) is a transfer learning paradigm that aims to recognize unseen categories just by having a high-level description of them. While deep learning has greatly pushed the limits of ZSL for object classification, ZSL for gesture recognition (ZSGL) remains largely unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL. It is indisputable that deep learning has obviated the need for feature engineering for problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (velocity, heuristical, and latent features) on the performance of ZSGL. In addition, we propose a bi-linear autoencoder approach, referred to as Joint Semantic Encoder (JSE), for ZSGL that jointly minimizes the reconstruction, semantic and classification losses.We conducted extensive experiments to compare and contrast the feature extraction techniques and to evaluate the performance of JSE with respect to existing ZSL methods. For attribute-based classification scenario, irrespective of the feature type, results showed that JSE outperforms other approaches by 5% (p < 0:01) on an average. When JSE is trained with heuristical features in across-category condition, we showed that JSE significantly outperforms other methods by approximately 5% (p < 0:01).\n
\n\n\n\n \n\n \n \n \n \n \n \n DESERTS: DElay-tolerant SEmi-autonomous Robot Teleoperation for Surgery.\n \n \n \n \n\n\n \n Gonzalez, G.; Agarwal, M.; Balakuntala, M., V.; Masudur Rahman, M.; Kaur, U.; Voyles, R., M.; Aggarwal, V.; Xue, Y.; and Wachs, J.\n\n\n \n\n\n\n In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 12693-12700, 5 2021. IEEE\n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {DESERTS: DElay-tolerant SEmi-autonomous Robot Teleoperation for Surgery},\n type = {inproceedings},\n year = {2021},\n pages = {12693-12700},\n websites = {https://ieeexplore.ieee.org/document/9561399/},\n month = {5},\n publisher = {IEEE},\n day = {30},\n id = {1d675ffd-8d1f-3abe-b0ed-6335acdbb9c1},\n created = {2022-03-17T12:12:09.789Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T12:12:09.789Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Gonzalez, Glebys and Agarwal, Mridul and Balakuntala, Mythra V. and Masudur Rahman, Md and Kaur, Upinder and Voyles, Richard M. and Aggarwal, Vaneet and Xue, Yexiang and Wachs, Juan},\n doi = {10.1109/ICRA48506.2021.9561399},\n booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}\n}\n\n \n\n \n \n \n \n \n \n Fast and Robust UAV to UAV Detection and Tracking from Video.\n \n \n \n \n\n\n \n Li, J.; Ye, D., H.; Kolsch, M.; Wachs, J., P.; and Bouman, C., A.\n\n\n \n\n\n\n IEEE Transactions on Emerging Topics in Computing,1-1. 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Fast and Robust UAV to UAV Detection and Tracking from Video},\n type = {article},\n year = {2021},\n keywords = {Cameras,Detectors,Optical detectors,Optical imaging,Radar tracking,Target tracking,Unmanned aerial vehicles},\n pages = {1-1},\n websites = {https://ieeexplore.ieee.org/document/9519550/},\n id = {63e95c53-0275-34a7-8fa2-83f2cbb2f3b0},\n created = {2022-03-17T22:37:35.227Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.646Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Unmanned Aerial Vehicle (UAV) technology is being increasingly used in a wide variety of applications ranging from remote sensing, to delivery and security. As the number of UAVs increases, there is a growing need for UAV to UAV detection and tracking systems for both collision avoidance and coordination. Among possible solutions, autonomous see-and-avoid systems based on low-cost high-resolution video cameras offer important advantages in terms of light weight and low power consumption. However, in order to be effective, camera based see-and-avoid systems require sensitive, robust, and computationally efficient algorithms for autonomous detection and tracking of UAVs from a moving camera. In this paper, we propose a general architecture for a highly accurate and computationally efficient UAV to UAV detection and tracking (U2U-D&T) algorithm from a camera mounted on a moving UAV platform. The system is based on a computationally efficient pipeline consisting of a moving target detector, followed by a target tracker. The algorithm is validated using video data collected from multiple fixed-wing UAVs that is manually ground-truthed and is publicly available. Results indicate that the proposed algorithm can be implemented on commodity hardware and robustly achieves highly accurate detection and tracking of even distant and faint UAVs.},\n bibtype = {article},\n author = {Li, Jing and Ye, Dong Hye and Kolsch, Mathias and Wachs, Juan P. and Bouman, Charles A},\n doi = {10.1109/TETC.2021.3104555},\n journal = {IEEE Transactions on Emerging Topics in Computing}\n}\n\n Unmanned Aerial Vehicle (UAV) technology is being increasingly used in a wide variety of applications ranging from remote sensing, to delivery and security. As the number of UAVs increases, there is a growing need for UAV to UAV detection and tracking systems for both collision avoidance and coordination. Among possible solutions, autonomous see-and-avoid systems based on low-cost high-resolution video cameras offer important advantages in terms of light weight and low power consumption. However, in order to be effective, camera based see-and-avoid systems require sensitive, robust, and computationally efficient algorithms for autonomous detection and tracking of UAVs from a moving camera. In this paper, we propose a general architecture for a highly accurate and computationally efficient UAV to UAV detection and tracking (U2U-D&T) algorithm from a camera mounted on a moving UAV platform. The system is based on a computationally efficient pipeline consisting of a moving target detector, followed by a target tracker. The algorithm is validated using video data collected from multiple fixed-wing UAVs that is manually ground-truthed and is publicly available. Results indicate that the proposed algorithm can be implemented on commodity hardware and robustly achieves highly accurate detection and tracking of even distant and faint UAVs.\n
\n\n\n\n \n\n \n \n \n \n \n \n Assessing task understanding in remote ultrasound diagnosis via gesture analysis.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; and Wachs, J., P.\n\n\n \n\n\n\n Pattern Analysis and Applications, 24(4): 1489-1500. 11 2021.\n \n\n\n\n
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\n\n\n@article{\n title = {Assessing task understanding in remote ultrasound diagnosis via gesture analysis},\n type = {article},\n year = {2021},\n keywords = {Gestures,Human collaboration,Task understanding,Ultrasound training},\n pages = {1489-1500},\n volume = {24},\n websites = {https://link.springer.com/10.1007/s10044-021-01027-2},\n month = {11},\n day = {13},\n id = {40834a12-e9e2-3c4b-a763-138203353506},\n created = {2022-03-17T22:42:38.261Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.024Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This work presents a gesture-based approach to estimate task understanding and performance during remote ultrasound tasks. Our approach is comprised of two main components. The first component uses the Multi-Agent Gestural Instruction Comparer (MAGIC) framework to represent and compare the gestures performed by collaborators. Through MAGIC, gestures can be compared based in their morphology, semantics, and pragmatics. The second component computes the Physical Instructions Assimilation (PIA) metric, a score representing how well are gestures being used to communicate and execute physical instructions. To evaluate our hypothesis, 20 participants performed a remote ultrasound task consisting of three subtasks: vessel detection, blood extraction, and foreign body detection. MAGIC’s gesture comparison approaches were compared against two other approaches based on how well they replicated human-annotated gestures matchings. Our approach outperformed the others, agreeing with the human baseline over 76% of the times. Subsequently, a correlation analysis was performed to compare PIA’s task understanding insights with those of three other metrics: error rate, idle time rate, and task completion percentage. Significant correlations (p≤0.04) were found between PIA and all the other metrics, positioning PIA as an effective metric for task understanding estimation. Finally, post-experiment questionnaires were used to subjectively evaluate the participants’ perceived understanding. The PIA score was found to be significantly correlated with the participants’ overall task understanding (p≤ 0.05), hinting to the relation between the assimilation of physical instructions and self-perceived understanding. These results demonstrate that gestures an be used to estimate task understanding in remote ultrasound tasks, which can improve how these tasks are performed and assessed.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Wachs, Juan P.},\n doi = {10.1007/s10044-021-01027-2},\n journal = {Pattern Analysis and Applications},\n number = {4}\n}\n\n This work presents a gesture-based approach to estimate task understanding and performance during remote ultrasound tasks. Our approach is comprised of two main components. The first component uses the Multi-Agent Gestural Instruction Comparer (MAGIC) framework to represent and compare the gestures performed by collaborators. Through MAGIC, gestures can be compared based in their morphology, semantics, and pragmatics. The second component computes the Physical Instructions Assimilation (PIA) metric, a score representing how well are gestures being used to communicate and execute physical instructions. To evaluate our hypothesis, 20 participants performed a remote ultrasound task consisting of three subtasks: vessel detection, blood extraction, and foreign body detection. MAGIC’s gesture comparison approaches were compared against two other approaches based on how well they replicated human-annotated gestures matchings. Our approach outperformed the others, agreeing with the human baseline over 76% of the times. Subsequently, a correlation analysis was performed to compare PIA’s task understanding insights with those of three other metrics: error rate, idle time rate, and task completion percentage. Significant correlations (p≤0.04) were found between PIA and all the other metrics, positioning PIA as an effective metric for task understanding estimation. Finally, post-experiment questionnaires were used to subjectively evaluate the participants’ perceived understanding. The PIA score was found to be significantly correlated with the participants’ overall task understanding (p≤ 0.05), hinting to the relation between the assimilation of physical instructions and self-perceived understanding. These results demonstrate that gestures an be used to estimate task understanding in remote ultrasound tasks, which can improve how these tasks are performed and assessed.\n
\n\n\n\n \n\n \n \n \n \n \n \n Sequential Prediction with Logic Constraints for Surgical Robotic Activity Recognition.\n \n \n \n \n\n\n \n Rahman, M., M.; Voyles, R., M.; Wachs, J.; and Xue, Y.\n\n\n \n\n\n\n In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pages 468-475, 8 2021. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Sequential Prediction with Logic Constraints for Surgical Robotic Activity Recognition},\n type = {inproceedings},\n year = {2021},\n pages = {468-475},\n websites = {https://ieeexplore.ieee.org/document/9515358/},\n month = {8},\n publisher = {IEEE},\n day = {8},\n id = {b9c4e60c-8c61-3368-a864-ff16419b15e2},\n created = {2022-03-17T22:48:35.298Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.813Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Many real-world time-sensitive and high-stake applications (e.g., surgical, rescue, and recovery robotics) exhibit sequential nature; thus, applying Recurrent Neural Network (RNN)-based sequential models is an attractive approach to detect robotic activity. One limitation of such approaches is data scarcity. As a result, limited training samples may lead to over-fitting, producing incorrect predictions during deployment. Nevertheless, abundant domain knowledge may still be available, which may help formulate logic constraints. In this paper, we propose a novel way to integrate domain knowledge into RNN-based sequential prediction. We build a Markov Logic Network (MLN)-based classifier that automatically learns constraint weights from data. We propose two methods to incorporate this MLN-based prediction: (i) PriorLayer, in which the values of the hidden layer of the RNN are combined with weights learned from logic constraints in an additional neural network layer, and (ii) Conflation, in which class probabilities from RNN predictions and constraint weights are combined based on the conflation of class probabilities. We evaluate robotic activity classification methods on a simulated OpenAI Gym environment and a real-world DESK dataset for surgical robotics. We observe that our proposed MLN-based approaches boost the performance of LSTM-based networks. In particular, MLN boosts the accuracy of LSTM from 71% to 84% on the Gym dataset and from 68% to 72% on the Taurus robot dataset. Furthermore, MLN (i.e., PriorLayer) shows regularization capability where it improves accuracy in initial LSTM training while avoiding over-fitting early, thus improves the final classification accuracy on unseen data. The code is available at https://github.com/masud99r/prediction-with-logic-constraints.},\n bibtype = {inproceedings},\n author = {Rahman, Md Masudur and Voyles, Richard M. and Wachs, Juan and Xue, Yexiang},\n doi = {10.1109/RO-MAN50785.2021.9515358},\n booktitle = {2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)}\n}\n\n Many real-world time-sensitive and high-stake applications (e.g., surgical, rescue, and recovery robotics) exhibit sequential nature; thus, applying Recurrent Neural Network (RNN)-based sequential models is an attractive approach to detect robotic activity. One limitation of such approaches is data scarcity. As a result, limited training samples may lead to over-fitting, producing incorrect predictions during deployment. Nevertheless, abundant domain knowledge may still be available, which may help formulate logic constraints. In this paper, we propose a novel way to integrate domain knowledge into RNN-based sequential prediction. We build a Markov Logic Network (MLN)-based classifier that automatically learns constraint weights from data. We propose two methods to incorporate this MLN-based prediction: (i) PriorLayer, in which the values of the hidden layer of the RNN are combined with weights learned from logic constraints in an additional neural network layer, and (ii) Conflation, in which class probabilities from RNN predictions and constraint weights are combined based on the conflation of class probabilities. We evaluate robotic activity classification methods on a simulated OpenAI Gym environment and a real-world DESK dataset for surgical robotics. We observe that our proposed MLN-based approaches boost the performance of LSTM-based networks. In particular, MLN boosts the accuracy of LSTM from 71% to 84% on the Gym dataset and from 68% to 72% on the Taurus robot dataset. Furthermore, MLN (i.e., PriorLayer) shows regularization capability where it improves accuracy in initial LSTM training while avoiding over-fitting early, thus improves the final classification accuracy on unseen data. The code is available at https://github.com/masud99r/prediction-with-logic-constraints.\n
\n\n\n\n \n\n \n \n \n \n \n \n ICONS: Imitation CONStraints for Robot Collaboration.\n \n \n \n \n\n\n \n Gonzalez, G.; and Wachs, J.\n\n\n \n\n\n\n In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), pages 147-154, 8 2021. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {ICONS: Imitation CONStraints for Robot Collaboration},\n type = {inproceedings},\n year = {2021},\n pages = {147-154},\n websites = {https://ieeexplore.ieee.org/document/9515490/},\n month = {8},\n publisher = {IEEE},\n day = {8},\n id = {950bfd1f-6740-318f-9f8d-68f78f9664e9},\n created = {2022-03-17T22:49:39.504Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.970Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Skill imitation has been an important ability in human-robot collaboration since it allows expeditious robot teaching of new tasks never seen before. To mimic the human pose, inverse kinematic solvers have been used to endow kinematic structures with human-like motion. Nevertheless, these solutions tend to be formulated for a specific robot or task. To address this generalization issue, this work presents the ICONS framework for imitation constraints, which proposes a general formulation for pose imitation, paired with a computationally efficient solver, inspired by the FABRIK algorithm. Three versions of the solver were developed to optimize the presented constraints. To assess the performance of ICONS, two tasks were evaluated, an incision task, and an assembly task. Fifty demonstrations were collected for each task. We compared the performance of our method, using pose accuracy and occlusion, against the numerical solver baseline (FABRIK). Notably, the ICONS framework improved the pose accuracy by 58% and reduced the environment occlusion by 38%. Moreover, the computational efficiency of the ICONS framework was assessed. Results show that the proposed algorithm maintains the efficiency of the baseline, finding the target solution under 10 iterations.},\n bibtype = {inproceedings},\n author = {Gonzalez, Glebys and Wachs, Juan},\n doi = {10.1109/RO-MAN50785.2021.9515490},\n booktitle = {2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)}\n}\n\n Skill imitation has been an important ability in human-robot collaboration since it allows expeditious robot teaching of new tasks never seen before. To mimic the human pose, inverse kinematic solvers have been used to endow kinematic structures with human-like motion. Nevertheless, these solutions tend to be formulated for a specific robot or task. To address this generalization issue, this work presents the ICONS framework for imitation constraints, which proposes a general formulation for pose imitation, paired with a computationally efficient solver, inspired by the FABRIK algorithm. Three versions of the solver were developed to optimize the presented constraints. To assess the performance of ICONS, two tasks were evaluated, an incision task, and an assembly task. Fifty demonstrations were collected for each task. We compared the performance of our method, using pose accuracy and occlusion, against the numerical solver baseline (FABRIK). Notably, the ICONS framework improved the pose accuracy by 58% and reduced the environment occlusion by 38%. Moreover, the computational efficiency of the ICONS framework was assessed. Results show that the proposed algorithm maintains the efficiency of the baseline, finding the target solution under 10 iterations.\n
\n\n\n\n \n\n \n \n \n \n \n \n SACHETS: Semi-autonomous cognitive hybrid emergency teleoperated suction.\n \n \n \n \n\n\n \n Barragan, J., A.; Chanci, D.; Yu, D.; and Wachs, J., P.\n\n\n \n\n\n\n In 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021, pages 1243-1248, 8 2021. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {SACHETS: Semi-autonomous cognitive hybrid emergency teleoperated suction},\n type = {inproceedings},\n year = {2021},\n keywords = {Cognitive workload,Human robotic interaction,Robotic surgery,Semi-autonomous assistant},\n pages = {1243-1248},\n websites = {https://ieeexplore.ieee.org/document/9515517/},\n month = {8},\n publisher = {IEEE},\n day = {8},\n id = {0257d56c-3ae3-353a-bb40-fd0aa675ccc8},\n created = {2022-03-17T22:50:26.315Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.857Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Blood suction and irrigation are among the most critical support tasks in robotic-assisted minimally invasive surgery (RMIS). Usually, suction/irrigation tools are controlled by a surgical assistant to maintain a clear view of the surgical field. Thus, the assistant's contribution to other emergency support tasks is limited. Similarly, when the surgical assistant is not available to perform the blood suction, the leading surgeon must take over this task, which in a complex surgical procedure can result in an unnecessary increment in the cognitive load. To alleviate this problem, we have developed a semi-autonomous robotic suction assistant, which was integrated with a Da Vinci Research Kit (DVRK). At the heart of the algorithm, there is an autonomous control based on a deep learning model to segment and identify the location of blood accumulations. This system provides automatic suction allowing the leading surgeon to focus exclusively on the main task through the control of key instruments of the robot. We conducted a user study to evaluate the user's workload demands and performance while doing a surgical task under two modalities: (1) autonomous suction action and (2) a surgeon-controlled-suction. Our results indicate that users working with the autonomous system completed the task 161 seconds faster than in the surgeon-controlled-suction modality. Furthermore, the autonomous modality led to a lower percentage of bleeding in the surgical field and workload demands on the users (p-value<0.05). These results show how leveraging state-of-the-art AI algorithms can reduce cognitive demands and enhance performance.},\n bibtype = {inproceedings},\n author = {Barragan, Juan Antonio and Chanci, Daniela and Yu, Denny and Wachs, Juan P.},\n doi = {10.1109/RO-MAN50785.2021.9515517},\n booktitle = {2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021}\n}\n\n Blood suction and irrigation are among the most critical support tasks in robotic-assisted minimally invasive surgery (RMIS). Usually, suction/irrigation tools are controlled by a surgical assistant to maintain a clear view of the surgical field. Thus, the assistant's contribution to other emergency support tasks is limited. Similarly, when the surgical assistant is not available to perform the blood suction, the leading surgeon must take over this task, which in a complex surgical procedure can result in an unnecessary increment in the cognitive load. To alleviate this problem, we have developed a semi-autonomous robotic suction assistant, which was integrated with a Da Vinci Research Kit (DVRK). At the heart of the algorithm, there is an autonomous control based on a deep learning model to segment and identify the location of blood accumulations. This system provides automatic suction allowing the leading surgeon to focus exclusively on the main task through the control of key instruments of the robot. We conducted a user study to evaluate the user's workload demands and performance while doing a surgical task under two modalities: (1) autonomous suction action and (2) a surgeon-controlled-suction. Our results indicate that users working with the autonomous system completed the task 161 seconds faster than in the surgeon-controlled-suction modality. Furthermore, the autonomous modality led to a lower percentage of bleeding in the surgical field and workload demands on the users (p-value<0.05). These results show how leveraging state-of-the-art AI algorithms can reduce cognitive demands and enhance performance.\n
\n\n\n\n \n\n \n \n \n \n \n \n Dexterous skill transfer between surgical procedures for teleoperated robotic surgery.\n \n \n \n \n\n\n \n Agarwal, M.; Gonzalez, G.; Balakuntala, M., V.; Masudur Rahman, M.; Aggarwal, V.; Voyles, R., M.; Xue, Y.; and Wachs, J.\n\n\n \n\n\n\n In 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021, pages 1236-1242, 8 2021. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Dexterous skill transfer between surgical procedures for teleoperated robotic surgery},\n type = {inproceedings},\n year = {2021},\n pages = {1236-1242},\n websites = {https://ieeexplore.ieee.org/document/9515453/},\n month = {8},\n publisher = {IEEE},\n day = {8},\n id = {4e9ccdb2-9b84-3ae0-8b38-15439a8fca73},\n created = {2022-03-17T22:51:14.423Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:22.971Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as 'surgemes') instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this paper, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition. This is demonstrated by using a network pre-trained with peg-transfer data from Yumi robot to learn classification on debridement on data from Taurus robot. Using a pre-trained network improves the classification accuracy achieves a classification accuracy of 76% with only 8 sequences in target domain, which is 22.5% better than no-transfer scenario. Additionally, ablations on transfer learning indicate that transfer learning requires 40% less data compared to no-transfer to achieve same classification accuracy. Further, the convergence rate of the transfer learning setup is significantly higher than the no-transfer setup trained only on the target domain.},\n bibtype = {inproceedings},\n author = {Agarwal, Mridul and Gonzalez, Glebys and Balakuntala, Mythra V. and Masudur Rahman, Md and Aggarwal, Vaneet and Voyles, Richard M. and Xue, Yexiang and Wachs, Juan},\n doi = {10.1109/RO-MAN50785.2021.9515453},\n booktitle = {2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021}\n}\n\n In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as 'surgemes') instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this paper, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition. This is demonstrated by using a network pre-trained with peg-transfer data from Yumi robot to learn classification on debridement on data from Taurus robot. Using a pre-trained network improves the classification accuracy achieves a classification accuracy of 76% with only 8 sequences in target domain, which is 22.5% better than no-transfer scenario. Additionally, ablations on transfer learning indicate that transfer learning requires 40% less data compared to no-transfer to achieve same classification accuracy. Further, the convergence rate of the transfer learning setup is significantly higher than the no-transfer setup trained only on the target domain.\n
\n\n\n\n \n\n \n \n \n \n \n \n Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering.\n \n \n \n \n\n\n \n Balakuntala, M., V.; Kaur, U.; Ma, X.; Wachs, J.; and Voyles, R., M.\n\n\n \n\n\n\n In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 4679-4685, 5 2021. IEEE\n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering},\n type = {inproceedings},\n year = {2021},\n pages = {4679-4685},\n websites = {https://ieeexplore.ieee.org/document/9561734/},\n month = {5},\n publisher = {IEEE},\n day = {30},\n id = {11af3c29-2f8a-3c39-a949-d0f5ebc87285},\n created = {2022-03-17T22:54:45.349Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.041Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Balakuntala, Mythra V. and Kaur, Upinder and Ma, Xin and Wachs, Juan and Voyles, Richard M.},\n doi = {10.1109/ICRA48506.2021.9561734},\n booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}\n}\n\n \n\n \n \n \n \n \n \n ZF-SSE: A Unified Sequential Semantic Encoder for Zero-Few-Shot Learning.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J.\n\n\n \n\n\n\n In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pages 1-8, 12 2021. IEEE\n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {ZF-SSE: A Unified Sequential Semantic Encoder for Zero-Few-Shot Learning},\n type = {inproceedings},\n year = {2021},\n pages = {1-8},\n websites = {https://ieeexplore.ieee.org/document/9667025/},\n month = {12},\n publisher = {IEEE},\n day = {15},\n id = {fd444c97-1ef6-3a49-a83d-cfe93624c036},\n created = {2022-03-17T22:56:54.793Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T23:08:23.208Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, Juan},\n doi = {10.1109/FG52635.2021.9667025},\n booktitle = {2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)}\n}\n\n
\n\n \n \n 2020\n \n \n (23)\n \n \n
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\n \n \n\n \n \n \n \n \n \n The AI-Medic: an artificial intelligent mentor for trauma surgery.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Couperus, K.; and Wachs, J., P.\n\n\n \n\n\n\n Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization,1-9. 11 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {The AI-Medic: an artificial intelligent mentor for trauma surgery},\n type = {article},\n year = {2020},\n pages = {1-9},\n websites = {https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1835548},\n month = {11},\n day = {23},\n id = {aee3307f-4a7d-34f8-b58c-45776472ac7e},\n created = {2020-11-16T16:49:16.592Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T18:05:37.911Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not available. However, unreliable network conditions, poor infras- tructure, and lack of remote mentors availability can signi cantly hinder remote in- tervention. To guide medical practitioners when mentors are unavailable, we present the AI-Medic, the initial steps towards an intelligent arti cial system for autonomous medical mentoring. A Deep Learning model is used to predict medical instructions from images of surgical procedures. The model was trained using the Dataset for AI Surgical Instruction (DAISI), a dataset including images and instructions providing step-by-step demonstrations of surgical procedures. The dataset includes one repe- tition of 290 diferent procedures from 20 medical disciplines, for a total of 17,339 color images and their associated text descriptions. Both images and text descrip- tions were compiled via input of expert surgeons from ve medical facilities and from academic textbooks. DAISI was used to train an encoder-decoder neural net- work to predict medical instructions given a view of the surgery. Afterwards, the predicted instructions were evaluated using cumulative BLEU scores and input from expert physicians. The evaluation was performed under two settings: with and with- out providing the model with prior information from test set procedures. According to the BLEU scores, the predicted and ground truth instructions were as high as 86+-1% similar. Additionally, expert physicians subjectively assessed the algorithm using via Likert scale questions, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Couperus, Kyle and Wachs, Juan P.},\n doi = {10.1080/21681163.2020.1835548},\n journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization}\n}\n\n Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not available. However, unreliable network conditions, poor infras- tructure, and lack of remote mentors availability can signi cantly hinder remote in- tervention. To guide medical practitioners when mentors are unavailable, we present the AI-Medic, the initial steps towards an intelligent arti cial system for autonomous medical mentoring. A Deep Learning model is used to predict medical instructions from images of surgical procedures. The model was trained using the Dataset for AI Surgical Instruction (DAISI), a dataset including images and instructions providing step-by-step demonstrations of surgical procedures. The dataset includes one repe- tition of 290 diferent procedures from 20 medical disciplines, for a total of 17,339 color images and their associated text descriptions. Both images and text descrip- tions were compiled via input of expert surgeons from ve medical facilities and from academic textbooks. DAISI was used to train an encoder-decoder neural net- work to predict medical instructions given a view of the surgery. Afterwards, the predicted instructions were evaluated using cumulative BLEU scores and input from expert physicians. The evaluation was performed under two settings: with and with- out providing the model with prior information from test set procedures. According to the BLEU scores, the predicted and ground truth instructions were as high as 86+-1% similar. Additionally, expert physicians subjectively assessed the algorithm using via Likert scale questions, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.\n
\n\n\n\n \n\n \n \n \n \n \n Message from the General and Program Chairs FG 2020.\n \n \n \n\n\n \n Wachs, J.; Escalera, S.; Cohn, J.; Salah, A., A.; and Ross, A.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
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\n\n\n\n@misc{\n title = {Message from the General and Program Chairs FG 2020},\n type = {misc},\n year = {2020},\n source = {Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020},\n pages = {xxi},\n id = {b9406e4b-f201-3f07-8aad-755e08fc47da},\n created = {2021-06-04T19:22:34.267Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.984Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {misc},\n author = {Wachs, Juan and Escalera, Sergio and Cohn, Jeffrey and Salah, Albert Ali and Ross, Arun},\n doi = {10.1109/FG47880.2020.00150}\n}\n\n \n\n \n \n \n \n \n Gesture Agreement Assessment Using Description Vectors.\n \n \n \n\n\n \n Madapana, N.; Gonzalez, G.; and Wachs, J.\n\n\n \n\n\n\n In Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020, pages 40-44, 2020. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Gesture Agreement Assessment Using Description Vectors},\n type = {inproceedings},\n year = {2020},\n keywords = {agreement analysis,gestures,semantic descriptors},\n pages = {40-44},\n id = {38ce65c9-c876-3d63-a5a1-968be5748a15},\n created = {2021-06-04T19:36:47.471Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.450Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Participatory design is a popular design technique that involves the end-users in the early stages of the design process to obtain user-friendly gestural interfaces. Guessability studies followed by agreement analyses are often used to elicit and comprehend the preferences (gestures/proposals) of the participants. Previous approaches to assess agreement, grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this work, we represent the gestures using binary description vectors to allow them to be partially similar. In this context, we introduce a new metric referred to as a soft agreement rate (SAR) to quantify the level of consensus between the participants. In addition, we performed computational experiments to study the behavior of our partial agreement formula and mathematically show that existing agreement metrics are a special case of our approach. Our methodology was evaluated through a gesture elicitation study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by SAR metric is 2.64 times higher than the existing metrics. In addition to the most agreed gesture, SAR formulation also provides the mostly agreed descriptors which can potentially help the designers to come up with a final gesture set.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Gonzalez, Glebys and Wachs, Juan},\n doi = {10.1109/FG47880.2020.00043},\n booktitle = {Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020}\n}\n\n Participatory design is a popular design technique that involves the end-users in the early stages of the design process to obtain user-friendly gestural interfaces. Guessability studies followed by agreement analyses are often used to elicit and comprehend the preferences (gestures/proposals) of the participants. Previous approaches to assess agreement, grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this work, we represent the gestures using binary description vectors to allow them to be partially similar. In this context, we introduce a new metric referred to as a soft agreement rate (SAR) to quantify the level of consensus between the participants. In addition, we performed computational experiments to study the behavior of our partial agreement formula and mathematically show that existing agreement metrics are a special case of our approach. Our methodology was evaluated through a gesture elicitation study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by SAR metric is 2.64 times higher than the existing metrics. In addition to the most agreed gesture, SAR formulation also provides the mostly agreed descriptors which can potentially help the designers to come up with a final gesture set.\n
\n\n\n\n \n\n \n \n \n \n \n \n The MAGIC of E-Health: A Gesture-Based Approach to Estimate Understanding and Performance in Remote Ultrasound Tasks.\n \n \n \n \n\n\n \n Rojas-Munoz, E.; and Wachs, J., P.\n\n\n \n\n\n\n In 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pages 723-727, 11 2020. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {The MAGIC of E-Health: A Gesture-Based Approach to Estimate Understanding and Performance in Remote Ultrasound Tasks},\n type = {inproceedings},\n year = {2020},\n keywords = {E Health,Gesture Modeling,Gesture Understanding,Human Computer Interaction,Ultrasound Training},\n pages = {723-727},\n websites = {https://ieeexplore.ieee.org/document/9320170/},\n month = {11},\n publisher = {IEEE},\n id = {a6ba8f6e-53da-3884-b882-b6ca83a18f62},\n created = {2021-06-04T19:36:47.529Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T18:02:30.783Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This work presents an approach to estimate task understanding and performance during a remote ultrasound training task via gestures. These task understanding insights are obtained through the PIA metric, a score that represents how well are gestures being used to complete a shared task. To evaluate our hypothesis, 20 participants performed a remote ultrasound training task consisting of three subtasks: vessel detection, blood extraction, and foreign body detection. Afterwards, their task understanding and performance was estimated using our PIA metric and three other metrics: error rate, idle time rate, and task completion percentage. After performing a correlation analysis, we found significant correlations between the PIA metric and all the other metrics for task understanding estimation. In addition, the insights generated from our PIA score explained inconsistencies in the participants' scores that were not expressed using the other metrics. Finally, we used two post-experiment questionnaires to subjectively evaluate the participants' perceived understanding and performance, and found that the PIA score was significantly correlated with the participants' overall task understanding. All these results indicate that a gesture-based metric can be used to estimate task understanding, which can have a positive impact in the way remote ultrasound tasks are performed and assessed.},\n bibtype = {inproceedings},\n author = {Rojas-Munoz, Edgar and Wachs, Juan P.},\n doi = {10.1109/FG47880.2020.00047},\n booktitle = {2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)}\n}\n\n This work presents an approach to estimate task understanding and performance during a remote ultrasound training task via gestures. These task understanding insights are obtained through the PIA metric, a score that represents how well are gestures being used to complete a shared task. To evaluate our hypothesis, 20 participants performed a remote ultrasound training task consisting of three subtasks: vessel detection, blood extraction, and foreign body detection. Afterwards, their task understanding and performance was estimated using our PIA metric and three other metrics: error rate, idle time rate, and task completion percentage. After performing a correlation analysis, we found significant correlations between the PIA metric and all the other metrics for task understanding estimation. In addition, the insights generated from our PIA score explained inconsistencies in the participants' scores that were not expressed using the other metrics. Finally, we used two post-experiment questionnaires to subjectively evaluate the participants' perceived understanding and performance, and found that the PIA score was significantly correlated with the participants' overall task understanding. All these results indicate that a gesture-based metric can be used to estimate task understanding, which can have a positive impact in the way remote ultrasound tasks are performed and assessed.\n
\n\n\n\n \n\n \n \n \n \n \n \n The AI-Medic: An Artificial Intelligent Mentor for Trauma Surgery.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Couperus, K.; and Wachs, J., P.\n\n\n \n\n\n\n In In 14thAugmented Environments for Computer Assisted Interventions Workshop (AE-CAI), part of 23rd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI '20), 2020. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {The AI-Medic: An Artificial Intelligent Mentor for Trauma Surgery},\n type = {inproceedings},\n year = {2020},\n keywords = {Datasets,neural networks,surgery,telementoring},\n id = {15420391-73b1-3271-859a-540ff40c1cb5},\n created = {2021-06-04T19:36:47.866Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T17:58:03.642Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not available. However, unreliable network conditions, poor infrastructure, and lack of remote mentors availability can significantly hinder remote intervention. To guide medical practitioners when mentors are unavailable, we present the AI-Medic, the initial steps towards an intelligent artificial system for autonomous medical mentoring. A Deep Learning model is used to predict medical instructions from images of surgical procedures. An encoder-decoder model was trained to predict medical instructions given a view of a surgery. The training was done using the Dataset for AI Surgical Instruction (DAISI), a dataset including images and instructions providing step-by-step demonstrations of 290 different surgical procedures from 20 medical disciplines. The predicted instructions were evaluated using cumulative BLEU scores and input from expert physicians. The evaluation was performed under two settings: with and without providing the model with prior information from test set procedures. According to the BLEU scores, the predicted and ground truth instructions were as high as (Formula presented.) % similar. Additionally, expert physicians subjectively assessed the algorithm subjetively and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms assisting in autonomous medical mentoring.},\n bibtype = {inproceedings},\n author = {Rojas-Muñoz, Edgar and Couperus, Kyle and Wachs, Juan P.},\n doi = {10.1080/21681163.2020.1835548},\n booktitle = {In 14thAugmented Environments for Computer Assisted Interventions Workshop (AE-CAI), part of 23rd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI '20)}\n}\n\n Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not available. However, unreliable network conditions, poor infrastructure, and lack of remote mentors availability can significantly hinder remote intervention. To guide medical practitioners when mentors are unavailable, we present the AI-Medic, the initial steps towards an intelligent artificial system for autonomous medical mentoring. A Deep Learning model is used to predict medical instructions from images of surgical procedures. An encoder-decoder model was trained to predict medical instructions given a view of a surgery. The training was done using the Dataset for AI Surgical Instruction (DAISI), a dataset including images and instructions providing step-by-step demonstrations of 290 different surgical procedures from 20 medical disciplines. The predicted instructions were evaluated using cumulative BLEU scores and input from expert physicians. The evaluation was performed under two settings: with and without providing the model with prior information from test set procedures. According to the BLEU scores, the predicted and ground truth instructions were as high as (Formula presented.) % similar. Additionally, expert physicians subjectively assessed the algorithm subjetively and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms assisting in autonomous medical mentoring.\n
\n\n\n\n \n\n \n \n \n \n \n \n Electrophysiological indicators of gesture perception.\n \n \n \n \n\n\n \n Cabrera, M., E.; Novak, K.; Foti, D.; Voyles, R.; and Wachs, J., P.\n\n\n \n\n\n\n Experimental Brain Research, 238(3): 537-550. 1 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Electrophysiological indicators of gesture perception},\n type = {article},\n year = {2020},\n keywords = {EEG,Gesture processing,Mirror neuron,Mu},\n pages = {537-550},\n volume = {238},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/31974755,https://doi.org/10.1007/s00221-020-05724-y,http://link.springer.com/10.1007/s00221-020-05724-y},\n month = {1},\n publisher = {Springer},\n day = {23},\n id = {08cacbcf-a498-3922-b711-e68a6f38b752},\n created = {2021-06-04T19:36:49.276Z},\n accessed = {2020-02-19},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T18:47:20.204Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2020},\n source_type = {JOUR},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Electroencephalography (EEG) activity in the mu frequency band (8–13 Hz) is suppressed during both gesture performance and observation. However, it is not clear if or how particular characteristics within the kinematic execution of gestures map onto dynamic changes in mu activity. Mapping the time course of gesture kinematics onto that of mu activity could help understand which aspects of gestures capture attention and aid in the classification of communicative intent. In this work, we test whether the timing of inflection points within gesture kinematics predicts the occurrence of oscillatory mu activity during passive gesture observation. The timing for salient features of performed gestures in video stimuli was determined by isolating inflection points in the hands’ motion trajectories. Participants passively viewed the gesture videos while continuous EEG data was collected. We used wavelet analysis to extract mu oscillations at 11 Hz and at central electrodes and occipital electrodes. We used linear regression to test for associations between the timing of inflection points in motion trajectories and mu oscillations that generalized across gesture stimuli. Separately, we also tested whether inflection point occurrences evoked mu/alpha responses that generalized across participants. Across all gestures and inflection points, and pooled across participants, peaks in 11 Hz EEG waveforms were detected 465 and 535 ms after inflection points at occipital and central electrodes, respectively. A regression model showed that inflection points in the motion trajectories strongly predicted subsequent mu oscillations (R2= 0.921 , p<0.01); effects were weaker and non-significant for low (17 Hz) and high (21 Hz) beta activity. When segmented by inflection point occurrence rather than stimulus onset and testing participants as a random effect, inflection points evoked mu and beta activity from 308 to 364 ms at central electrodes, and broad activity from 226 to 800 ms at occipital electrodes. The results suggest that inflection points in gesture trajectories elicit coordinated activity in the visual and motor cortices, with prominent activity in the mu/alpha frequency band and extending into the beta frequency band. The time course of activity indicates that visual processing drives subsequent activity in the motor cortex during gesture processing, with a lag of approximately 80 ms.},\n bibtype = {article},\n author = {Cabrera, Maria E. and Novak, Keisha and Foti, Dan and Voyles, Richard and Wachs, Juan P.},\n doi = {10.1007/s00221-020-05724-y},\n journal = {Experimental Brain Research},\n number = {3}\n}\n\n Electroencephalography (EEG) activity in the mu frequency band (8–13 Hz) is suppressed during both gesture performance and observation. However, it is not clear if or how particular characteristics within the kinematic execution of gestures map onto dynamic changes in mu activity. Mapping the time course of gesture kinematics onto that of mu activity could help understand which aspects of gestures capture attention and aid in the classification of communicative intent. In this work, we test whether the timing of inflection points within gesture kinematics predicts the occurrence of oscillatory mu activity during passive gesture observation. The timing for salient features of performed gestures in video stimuli was determined by isolating inflection points in the hands’ motion trajectories. Participants passively viewed the gesture videos while continuous EEG data was collected. We used wavelet analysis to extract mu oscillations at 11 Hz and at central electrodes and occipital electrodes. We used linear regression to test for associations between the timing of inflection points in motion trajectories and mu oscillations that generalized across gesture stimuli. Separately, we also tested whether inflection point occurrences evoked mu/alpha responses that generalized across participants. Across all gestures and inflection points, and pooled across participants, peaks in 11 Hz EEG waveforms were detected 465 and 535 ms after inflection points at occipital and central electrodes, respectively. A regression model showed that inflection points in the motion trajectories strongly predicted subsequent mu oscillations (R2= 0.921 , p<0.01); effects were weaker and non-significant for low (17 Hz) and high (21 Hz) beta activity. When segmented by inflection point occurrence rather than stimulus onset and testing participants as a random effect, inflection points evoked mu and beta activity from 308 to 364 ms at central electrodes, and broad activity from 226 to 800 ms at occipital electrodes. The results suggest that inflection points in gesture trajectories elicit coordinated activity in the visual and motor cortices, with prominent activity in the mu/alpha frequency band and extending into the beta frequency band. The time course of activity indicates that visual processing drives subsequent activity in the motor cortex during gesture processing, with a lag of approximately 80 ms.\n
\n\n\n\n \n\n \n \n \n \n \n \n The System for Telementoring with Augmented Reality (STAR): A head-mounted display to improve surgical coaching and confidence in remote areas.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Cabrera, M., M., E.; Lin, C.; Andersen, D.; Popescu, V.; Anderson, K.; Zarzaur, B., B., L.; Mullis, B.; and Wachs, J., J., P.\n\n\n \n\n\n\n Surgery (United States), 167(4): 724-731. 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {The System for Telementoring with Augmented Reality (STAR): A head-mounted display to improve surgical coaching and confidence in remote areas},\n type = {article},\n year = {2020},\n pages = {724-731},\n volume = {167},\n id = {c1385aa0-1f29-313b-aa23-5be30c98a29f},\n created = {2021-06-04T19:36:49.292Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T20:19:17.272Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Background: The surgical workforce particularly in rural regions needs novel approaches to reinforce the skills and confidence of health practitioners. Although conventional telementoring systems have proven beneficial to address this gap, the benefits of platforms of augmented reality-based telementoring in the coaching and confidence of medical personnel are yet to be evaluated. Methods: A total of 20 participants were guided by remote expert surgeons to perform leg fasciotomies on cadavers under one of two conditions: (1) telementoring (with our System for Telementoring with Augmented Reality) or (2) independently reviewing the procedure beforehand. Using the Individual Performance Score and the Weighted Individual Performance Score, two on-site, expert surgeons evaluated the participants. Postexperiment metrics included number of errors, procedure completion time, and self-reported confidence scores. A total of six objective measurements were obtained to describe the self-reported confidence scores and the overall quality of the coaching. Additional analyses were performed based on the participants’ expertise level. Results: Participants using the System for Telementoring with Augmented Reality received 10% greater Weighted Individual Performance Score (P = .03) and performed 67% fewer errors (P = .04). Moreover, participants with lower surgical expertise that used the System for Telementoring with Augmented Reality received 17% greater Individual Performance Score (P = .04), 32% greater Weighted Individual Performance Score (P < .01) and performed 92% fewer errors (P < .001). In addition, participants using the System for Telementoring with Augmented Reality reported 25% more confidence in all evaluated aspects (P < .03). On average, participants using the System for Telementoring with Augmented Reality received augmented reality guidance 19 times on average and received guidance for 47% of their total task completion time. Conclusion: Participants using the System for Telementoring with Augmented Reality performed leg fasciotomies with fewer errors and received better performance scores. In addition, participants using the System for Telementoring with Augmented Reality reported being more confident when performing fasciotomies under telementoring. Augmented Reality Head-Mounted Display–based telementoring successfully provided confidence and coaching to medical personnel.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Cabrera, M.E. Maria E. and Lin, Chengyuan and Andersen, Daniel and Popescu, Voicu and Anderson, Kathryn and Zarzaur, B.L. Ben L. and Mullis, Brian and Wachs, J.P. Juan P.},\n doi = {10.1016/j.surg.2019.11.008},\n journal = {Surgery (United States)},\n number = {4}\n}\n\n Background: The surgical workforce particularly in rural regions needs novel approaches to reinforce the skills and confidence of health practitioners. Although conventional telementoring systems have proven beneficial to address this gap, the benefits of platforms of augmented reality-based telementoring in the coaching and confidence of medical personnel are yet to be evaluated. Methods: A total of 20 participants were guided by remote expert surgeons to perform leg fasciotomies on cadavers under one of two conditions: (1) telementoring (with our System for Telementoring with Augmented Reality) or (2) independently reviewing the procedure beforehand. Using the Individual Performance Score and the Weighted Individual Performance Score, two on-site, expert surgeons evaluated the participants. Postexperiment metrics included number of errors, procedure completion time, and self-reported confidence scores. A total of six objective measurements were obtained to describe the self-reported confidence scores and the overall quality of the coaching. Additional analyses were performed based on the participants’ expertise level. Results: Participants using the System for Telementoring with Augmented Reality received 10% greater Weighted Individual Performance Score (P = .03) and performed 67% fewer errors (P = .04). Moreover, participants with lower surgical expertise that used the System for Telementoring with Augmented Reality received 17% greater Individual Performance Score (P = .04), 32% greater Weighted Individual Performance Score (P < .01) and performed 92% fewer errors (P < .001). In addition, participants using the System for Telementoring with Augmented Reality reported 25% more confidence in all evaluated aspects (P < .03). On average, participants using the System for Telementoring with Augmented Reality received augmented reality guidance 19 times on average and received guidance for 47% of their total task completion time. Conclusion: Participants using the System for Telementoring with Augmented Reality performed leg fasciotomies with fewer errors and received better performance scores. In addition, participants using the System for Telementoring with Augmented Reality reported being more confident when performing fasciotomies under telementoring. Augmented Reality Head-Mounted Display–based telementoring successfully provided confidence and coaching to medical personnel.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Randomized Trial of Mentored vs Nonmentored Military Medics Compared in the Application of a Wound Clamp Without Prior Training: When to Shut Up and Just Watch!.\n \n \n \n \n\n\n \n Kirkpatrick, A., W.; McKee, J., L.; Netzer, I.; Mckee, I., A.; McBeth, P.; Wachs, J., J., P.; Ball, C., G.; and Glassberg, E.\n\n\n \n\n\n\n Military Medicine, 185(Suppl 1): 67-72. 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {A Randomized Trial of Mentored vs Nonmentored Military Medics Compared in the Application of a Wound Clamp Without Prior Training: When to Shut Up and Just Watch!},\n type = {article},\n year = {2020},\n pages = {67-72},\n volume = {185},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/32074324},\n day = {7},\n id = {73bee043-263a-304d-bd67-5b06b7d13b81},\n created = {2021-06-04T19:36:50.054Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.222Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {0f5aa25e-250b-450c-a560-2626eae40317,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Introduction: Hemorrhage control is a basic task required of first responders and typically requires technical interventions during stressful circumstances. Remote telementoring (RTM) utilizes information technology to guide inexperienced providers, but when this is useful remains undefined. Methods: Military medics were randomized to mentoring or not from an experienced subject matter expert during the application of a wound clamp (WC) to a simulated bleed. Inexperienced, nonmentored medics were given a 30-second safety briefing; mentored medics were not. Objective outcomes were time to task completion and success in arresting simulated bleeding. Results: Thirty-three medics participated (16 mentored and 17 nonmentored). All (100%) successfully applies the WC to arrest the simulated hemorrhage. RTM significantly slowed hemorrhage control (P = 0.000) between the mentored (40.4 ± 12.0 seconds) and nonmentored (15.2 ± 10.3 seconds) groups. On posttask questionnaire, all medics subjectively rated the difficulty of the wound clamping as 1.7/10 (10 being extremely hard). Discussion: WC application appeared to be an easily acquired technique that was effective in controlling simulated extremity exsanguination, such that RTM while feasible did not improve outcomes. Limitations were the lack of true stress and using simulation for the task. Future research should focus on determining when RTM is useful and when it is not required.},\n bibtype = {article},\n author = {Kirkpatrick, Andrew W. and McKee, Jessica L. and Netzer, Itamar and Mckee, Ian A. and McBeth, Paul and Wachs, JP Juan P. and Ball, Chad G. and Glassberg, Elon},\n doi = {10.1093/milmed/usz251},\n journal = {Military Medicine},\n number = {Suppl 1}\n}\n\n Introduction: Hemorrhage control is a basic task required of first responders and typically requires technical interventions during stressful circumstances. Remote telementoring (RTM) utilizes information technology to guide inexperienced providers, but when this is useful remains undefined. Methods: Military medics were randomized to mentoring or not from an experienced subject matter expert during the application of a wound clamp (WC) to a simulated bleed. Inexperienced, nonmentored medics were given a 30-second safety briefing; mentored medics were not. Objective outcomes were time to task completion and success in arresting simulated bleeding. Results: Thirty-three medics participated (16 mentored and 17 nonmentored). All (100%) successfully applies the WC to arrest the simulated hemorrhage. RTM significantly slowed hemorrhage control (P = 0.000) between the mentored (40.4 ± 12.0 seconds) and nonmentored (15.2 ± 10.3 seconds) groups. On posttask questionnaire, all medics subjectively rated the difficulty of the wound clamping as 1.7/10 (10 being extremely hard). Discussion: WC application appeared to be an easily acquired technique that was effective in controlling simulated extremity exsanguination, such that RTM while feasible did not improve outcomes. Limitations were the lack of true stress and using simulation for the task. Future research should focus on determining when RTM is useful and when it is not required.\n
\n\n\n\n \n\n \n \n \n \n \n \n Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training.\n \n \n \n \n\n\n \n Wu, C.; Cha, J.; Sulek, J.; Zhou, T.; Sundaram, C., C., P.; Wachs, J.; and Yu, D.\n\n\n \n\n\n\n Human Factors, 62(8): 1365-1386. 12 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training},\n type = {article},\n year = {2020},\n keywords = {eye movements,perceived workload,robotics and telesurgery,simulation training,statistics and data analysis},\n pages = {1365-1386},\n volume = {62},\n websites = {http://journals.sagepub.com/doi/10.1177/0018720819874544},\n month = {12},\n day = {27},\n id = {e8085d0c-44c6-32c9-b2a4-c91e92089efb},\n created = {2021-06-04T19:36:50.077Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T18:00:33.006Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective: The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background: Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains. Methods: Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features. Results: Gaze entropy increased as perceived workload increased, with a correlation of.51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels. Conclusion: Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training. Application: Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.},\n bibtype = {article},\n author = {Wu, Chuhao and Cha, Jackie and Sulek, Jay and Zhou, Tian and Sundaram, C.P. Chandru P. and Wachs, Juan and Yu, Denny},\n doi = {10.1177/0018720819874544},\n journal = {Human Factors},\n number = {8}\n}\n\n Objective: The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background: Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains. Methods: Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features. Results: Gaze entropy increased as perceived workload increased, with a correlation of.51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels. Conclusion: Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training. Application: Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.\n
\n\n\n\n \n\n \n \n \n \n \n \n Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery.\n \n \n \n \n\n\n \n Zhou, T.; Cha, J., S.; Gonzalez, G.; Wachs, J., P.; Sundaram, C., P.; and Yu, D.\n\n\n \n\n\n\n ACM Transactions on Human-Robot Interaction, 9(2): 1-26. 2 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery},\n type = {article},\n year = {2020},\n pages = {1-26},\n volume = {9},\n websites = {https://doi.org/10.1145/3368589,https://dl.acm.org/doi/10.1145/3368589},\n month = {2},\n day = {7},\n id = {27c4abd8-4b3c-3a20-9580-4929f653a5ed},\n created = {2021-06-04T19:36:50.236Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:27:12.581Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {RN1828},\n source_type = {article},\n user_context = {Journal Article},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab},\n private_publication = {false},\n abstract = {Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons’ cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks.},\n bibtype = {article},\n author = {Zhou, Tian and Cha, Jackie S. and Gonzalez, Glebys and Wachs, Juan P. and Sundaram, Chandru P. and Yu, Denny},\n doi = {10.1145/3368589},\n journal = {ACM Transactions on Human-Robot Interaction},\n number = {2}\n}\n\n Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons’ cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks.\n
\n\n\n\n \n\n \n \n \n \n \n \n Beyond MAGIC: Matching Collaborative Gestures using an optimization-based Approach.\n \n \n \n \n\n\n \n Rojas-Munoz, E.; and Wachs, J., P.\n\n\n \n\n\n\n In 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pages 457-464, 11 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Beyond MAGIC: Matching Collaborative Gestures using an optimization-based Approach},\n type = {inproceedings},\n year = {2020},\n keywords = {Collaboration,Gesture Modeling,Gesture Understanding,Human Computer Interaction},\n pages = {457-464},\n websites = {https://pdfs.semanticscholar.org/88e1/8059b5e53a557b643a170c015d0475038b53.pdf,https://ieeexplore.ieee.org/document/9320287/},\n month = {11},\n publisher = {IEEE},\n id = {393e58b2-3f3c-3709-8773-2b5477442b79},\n created = {2021-06-04T19:36:50.798Z},\n accessed = {2020-11-15},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:22:48.991Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Gestures are a key aspect of communication during collaboration: through gestures we can express ideas, inquires and formalize instructions as we collaborate. Nevertheless, gesture analysis is not currently used to assess quality of task collaboration. One possible reason for this is that there is no consensus on how to represent and compare gestures from the semantic standpoint. To address this, this paper introduces three novel approaches to compare gestures performed by individuals as they collaborate to complete a physical task. Our approach relies on solving three variations of an integer optimization assignment problem, i.e. based on gesture similarity, based on temporal synchrony, and based on a combination of both. We collected the gestures of 40 participants (divided into 20 pairs) as they performed two collaborative tasks, and generated a human baseline that compared and matched their gestures. Afterwards, our gesture comparison approach was evaluated against other gestures comparison approaches based on how well they replicated the human baseline. Our approach outperformed the other approaches, agreeing with the human baseline over 85% of the times. Thus, the obtained results support the proposed technique for gesture comparison. This in turn can lead to the development of better methods to evaluate collaborative physical tasks.},\n bibtype = {inproceedings},\n author = {Rojas-Munoz, Edgar and Wachs, Juan P.},\n doi = {10.1109/FG47880.2020.00044},\n booktitle = {2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)}\n}\n\n Gestures are a key aspect of communication during collaboration: through gestures we can express ideas, inquires and formalize instructions as we collaborate. Nevertheless, gesture analysis is not currently used to assess quality of task collaboration. One possible reason for this is that there is no consensus on how to represent and compare gestures from the semantic standpoint. To address this, this paper introduces three novel approaches to compare gestures performed by individuals as they collaborate to complete a physical task. Our approach relies on solving three variations of an integer optimization assignment problem, i.e. based on gesture similarity, based on temporal synchrony, and based on a combination of both. We collected the gestures of 40 participants (divided into 20 pairs) as they performed two collaborative tasks, and generated a human baseline that compared and matched their gestures. Afterwards, our gesture comparison approach was evaluated against other gestures comparison approaches based on how well they replicated the human baseline. Our approach outperformed the other approaches, agreeing with the human baseline over 85% of the times. Thus, the obtained results support the proposed technique for gesture comparison. This in turn can lead to the development of better methods to evaluate collaborative physical tasks.\n
\n\n\n\n \n\n \n \n \n \n \n \n Physiological Measurements of Situation Awareness: A Systematic Review.\n \n \n \n \n\n\n \n Zhang, T.; Yang, J.; Liang, N.; Pitts, B., J.; Prakah-Asante, K., O.; Curry, R.; Duerstock, B., S.; Wachs, J., P.; and Yu, D.\n\n\n \n\n\n\n Human Factors,0018720820969071. 11 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {Physiological Measurements of Situation Awareness: A Systematic Review},\n type = {article},\n year = {2020},\n keywords = {driver behavior,physiological measurement,physiology,situation awareness,wearable devices},\n pages = {0018720820969071},\n websites = {https://doi.org/10.1177/0018720820969071,http://journals.sagepub.com/doi/10.1177/0018720820969071},\n month = {11},\n publisher = {SAGE PublicationsSage CA: Los Angeles, CA},\n day = {26},\n id = {6e3db8ab-4ae1-37ec-80b4-ce2d948bc97d},\n created = {2021-06-04T19:36:50.806Z},\n accessed = {2020-11-30},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.885Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {RN2750},\n source_type = {article},\n user_context = {Journal Article},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective: The goal of this review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background: Assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SA global assessment technique (SAGAT), situation present assessment method (SPAM), and/or SA rating technique (SART). However, research suggests that physiological sensing methods may also be capable of inferring SA. Method: Seven databases were searched. Eligibility criteria included human–subject experiments that used at least one direct SA assessment technique as well as at least one physiological measurement. Information extracted from each article were the physiological metric(s), direct SA measurement(s), correlation between these two metrics, and experimental task(s). Results: Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA was mixed. Only three electroencephalography (EEG) studies were identified, and their results suggest that EEG was sensitive to changes in SA. Overall, medium correlations were observed among the studies that reported a correlation coefficient between physiological and direct SA measures. Conclusion: Reviewed studies observed relationships between a wide range of physiological measurements and direct assessments of SA. However, further investigations are needed to methodically collect more evidence. Application: This review provides researchers and practitioners a summary of observed methods to indirectly assess SA with sensors and highlights research gaps to be addressed in future work.},\n bibtype = {article},\n author = {Zhang, Ting and Yang, Jing and Liang, Nade and Pitts, Brandon J. and Prakah-Asante, Kwaku O. and Curry, Reates and Duerstock, Bradley S. and Wachs, Juan P. and Yu, Denny},\n doi = {10.1177/0018720820969071},\n journal = {Human Factors}\n}\n\n Objective: The goal of this review is to investigate the relationship between indirect physiological measurements and direct measures of situation awareness (SA). Background: Assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SA global assessment technique (SAGAT), situation present assessment method (SPAM), and/or SA rating technique (SART). However, research suggests that physiological sensing methods may also be capable of inferring SA. Method: Seven databases were searched. Eligibility criteria included human–subject experiments that used at least one direct SA assessment technique as well as at least one physiological measurement. Information extracted from each article were the physiological metric(s), direct SA measurement(s), correlation between these two metrics, and experimental task(s). Results: Twenty-five articles were included in this review. Eye tracking techniques were the most commonly used physiological measures, and correlations between conscious aspects of eye movement measures and direct SA scores were observed. Evidence for cardiovascular predictors of SA was mixed. Only three electroencephalography (EEG) studies were identified, and their results suggest that EEG was sensitive to changes in SA. Overall, medium correlations were observed among the studies that reported a correlation coefficient between physiological and direct SA measures. Conclusion: Reviewed studies observed relationships between a wide range of physiological measurements and direct assessments of SA. However, further investigations are needed to methodically collect more evidence. Application: This review provides researchers and practitioners a summary of observed methods to indirectly assess SA with sensors and highlights research gaps to be addressed in future work.\n
\n\n\n\n \n\n \n \n \n \n \n \n Feature Selection for Zero-Shot Gesture Recognition.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J.\n\n\n \n\n\n\n In 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pages 683-687, 11 2020. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Feature Selection for Zero-Shot Gesture Recognition},\n type = {inproceedings},\n year = {2020},\n keywords = {and spontaneous gestures,attribute,attribute learning,gesture recognition,zero shot learning},\n pages = {683-687},\n websites = {https://ieeexplore.ieee.org/document/9320217/},\n month = {11},\n publisher = {IEEE},\n id = {97a45c06-3904-36d7-809d-9dca89e7222e},\n created = {2021-06-04T19:36:50.966Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:21:50.732Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2020a},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Existing classification techniques assign a predetermined categorical label to each sample and cannot recognize the new categories that might appear after the training stage. This limitation has led to the advent of new paradigms in machine learning such as zero-shot learning (ZSL). ZSL aims to recognize unseen categories by having a high-level description of them. While deep learning has pushed the limits of ZSL for object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (ZSGL) remain unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes, attribute-based datasets, and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL problems. It is indisputable that deep learning has obviated the need for feature engineering for the problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (raw features, engineered features, and deep learning features) on the performance of ZSGL. Next, we propose a new approach for ZSGL that jointly minimizes the reconstruction loss, semantic and classification losses. Our methodology yields an unseen class accuracy of (38%) which parallels the accuracies obtained through state-of-the-art approaches.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, Juan},\n doi = {10.1109/FG47880.2020.00046},\n booktitle = {2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)}\n}\n\n Existing classification techniques assign a predetermined categorical label to each sample and cannot recognize the new categories that might appear after the training stage. This limitation has led to the advent of new paradigms in machine learning such as zero-shot learning (ZSL). ZSL aims to recognize unseen categories by having a high-level description of them. While deep learning has pushed the limits of ZSL for object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (ZSGL) remain unexplored. Previous attempts to address ZSGL were focused on the creation of gesture attributes, attribute-based datasets, and algorithmic improvements, and there is little or no research concerned with feature selection for ZSGL problems. It is indisputable that deep learning has obviated the need for feature engineering for the problems with large datasets. However, when the data is scarce, it is critical to leverage the domain information to create discriminative input features. The main goal of this work is to study the effect of three different feature extraction techniques (raw features, engineered features, and deep learning features) on the performance of ZSGL. Next, we propose a new approach for ZSGL that jointly minimizes the reconstruction loss, semantic and classification losses. Our methodology yields an unseen class accuracy of (38%) which parallels the accuracies obtained through state-of-the-art approaches.\n
\n\n\n\n \n\n \n \n \n \n \n \n DAISI: Database for AI Surgical Instruction.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Couperus, K.; and Wachs, J.\n\n\n \n\n\n\n arXiv. 3 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {DAISI: Database for AI Surgical Instruction},\n type = {article},\n year = {2020},\n keywords = {Autonomous,Database,Images ·,Medical,Mentoring ·},\n websites = {http://arxiv.org/abs/2004.02809},\n month = {3},\n day = {22},\n id = {569da35d-32f9-38a5-ab2a-4d2d0ea50803},\n created = {2021-06-04T19:36:51.145Z},\n accessed = {2020-11-16},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.356Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {rojasmuoz2020daisi},\n source_type = {misc},\n folder_uuids = {0f5aa25e-250b-450c-a560-2626eae40317,03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the development of an intelligent artificial system for autonomous medical mentoring. Specifically, we present the first Database for AI Surgical Instruction (DAISI). DAISI leverages on images and instructions to provide step-by-step demonstrations of how to perform procedures from various medical disciplines. The dataset was acquired from real surgical procedures and data from academic textbooks. We used DAISI to train an encoder-decoder neural network capable of predicting medical instructions given a current view of the surgery. Afterwards, the instructions predicted by the network were evaluated using cumulative BLEU scores and input from expert physicians. According to the BLEU scores, the predicted and ground truth instructions were as high as 67% similar. Additionally, expert physicians subjectively assessed the algorithm using Likert scale, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Couperus, Kyle and Wachs, Juan},\n journal = {arXiv}\n}\n\n Telementoring surgeons as they perform surgery can be essential in the treatment of patients when in situ expertise is not available. Nonetheless, expert mentors are often unavailable to provide trainees with real-time medical guidance. When mentors are unavailable, a fallback autonomous mechanism should provide medical practitioners with the required guidance. However, AI/autonomous mentoring in medicine has been limited by the availability of generalizable prediction models, and surgical procedures datasets to train those models with. This work presents the initial steps towards the development of an intelligent artificial system for autonomous medical mentoring. Specifically, we present the first Database for AI Surgical Instruction (DAISI). DAISI leverages on images and instructions to provide step-by-step demonstrations of how to perform procedures from various medical disciplines. The dataset was acquired from real surgical procedures and data from academic textbooks. We used DAISI to train an encoder-decoder neural network capable of predicting medical instructions given a current view of the surgery. Afterwards, the instructions predicted by the network were evaluated using cumulative BLEU scores and input from expert physicians. According to the BLEU scores, the predicted and ground truth instructions were as high as 67% similar. Additionally, expert physicians subjectively assessed the algorithm using Likert scale, and considered that the predicted descriptions were related to the images. This work provides a baseline for AI algorithms to assist in autonomous medical mentoring.\n
\n\n\n\n \n\n \n \n \n \n \n \n How About the Mentor? Effective Workspace Visualization in AR Telementoring.\n \n \n \n \n\n\n \n Lin, C.; Rojas-Munoz, E.; Cabrera, M., E.; Sanchez-Tamayo, N.; Andersen, D.; Popescu, V.; Barragan Noguera, J., A.; Zarzaur, B.; Murphy, P.; Anderson, K.; Douglas, T.; Griffis, C.; and Wachs, J.\n\n\n \n\n\n\n In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 212-220, 3 2020. IEEE\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {How About the Mentor? Effective Workspace Visualization in AR Telementoring},\n type = {inproceedings},\n year = {2020},\n keywords = {Graphics systems and interfaces,Human-centered computing,Mixed/augmented reality,Visualization},\n pages = {212-220},\n websites = {https://ieeexplore.ieee.org/document/9089635/},\n month = {3},\n publisher = {IEEE},\n id = {e0dd61ca-0042-32c4-8984-73f4b3ac004f},\n created = {2021-06-04T19:36:51.189Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:15:57.360Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Augmented Reality (AR) benefits telementoring by enhancing the communication between the mentee and the remote mentor with mentor authored graphical annotations that are directly integrated into the mentee's view of the workspace. An important problem is conveying the workspace to the mentor effectively, such that they can provide adequate guidance. AR headsets now incorporate a frontfacing video camera, which can be used to acquire the workspace. However, simply providing to the mentor this video acquired from the mentee's first-person view is inadequate. As the mentee moves their head, the mentor's visualization of the workspace changes frequently, unexpectedly, and substantially. This paper presents a method for robust high-level stabilization of a mentee first-person video to provide effective workspace visualization to a remote mentor. The visualization is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's typical viewpoint, as needed to best inform the mentor of the current state of the workspace. In one study, the stabilized visualization had significant advantages over unstabilized visualization, in the context of three number matching tasks. In a second study, stabilization showed good results, in the context of surgical telementoring, specifically for cricothyroidotomy training in austere settings.},\n bibtype = {inproceedings},\n author = {Lin, Chengyuan and Rojas-Munoz, Edgar and Cabrera, Maria Eugenia and Sanchez-Tamayo, Natalia and Andersen, Daniel and Popescu, Voicu and Barragan Noguera, Juan Antonio and Zarzaur, Ben and Murphy, Pat and Anderson, Kathryn and Douglas, Thomas and Griffis, Clare and Wachs, Juan},\n doi = {10.1109/VR46266.2020.1580934001692},\n booktitle = {2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}\n}\n\n Augmented Reality (AR) benefits telementoring by enhancing the communication between the mentee and the remote mentor with mentor authored graphical annotations that are directly integrated into the mentee's view of the workspace. An important problem is conveying the workspace to the mentor effectively, such that they can provide adequate guidance. AR headsets now incorporate a frontfacing video camera, which can be used to acquire the workspace. However, simply providing to the mentor this video acquired from the mentee's first-person view is inadequate. As the mentee moves their head, the mentor's visualization of the workspace changes frequently, unexpectedly, and substantially. This paper presents a method for robust high-level stabilization of a mentee first-person video to provide effective workspace visualization to a remote mentor. The visualization is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's typical viewpoint, as needed to best inform the mentor of the current state of the workspace. In one study, the stabilized visualization had significant advantages over unstabilized visualization, in the context of three number matching tasks. In a second study, stabilization showed good results, in the context of surgical telementoring, specifically for cricothyroidotomy training in austere settings.\n
\n\n\n\n \n\n \n \n \n \n \n \n Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks.\n \n \n \n \n\n\n \n Zhang, T.; Duerstock, B., S.; and Wachs, J., P.\n\n\n \n\n\n\n IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(4): 1032-1041. 4 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Classification of Blind Users’ Image Exploratory Behaviors Using Spiking Neural Networks},\n type = {article},\n year = {2020},\n keywords = {Blind or visually impaired,Dempster-Shafer theory,Exploration procedures,Image perception,Spatio-temporal data,Spiking neural networks},\n pages = {1032-1041},\n volume = {28},\n websites = {https://ieeexplore.ieee.org/document/8932577/},\n month = {4},\n id = {bba5d5dc-899e-3bbc-a861-e4d28c585c95},\n created = {2021-06-04T19:36:51.294Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:14:15.893Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Individuals who are blind adopt multiple procedures to tactually explore images. Automatically recognizing and classifying users' exploration behaviors is the first step towards the development of an intelligent system that could assist users to explore images more efficiently. In this paper, a computational framework was developed to classify different procedures used by blind users during image exploration. Translation-, rotation- and scale-invariant features were extracted from the trajectories of users' movements. These features were divided as numerical and logical features and were fed into neural networks. More specifically, we trained spiking neural networks (SNNs) to further encode the numerical features as model strings. The proposed framework employed a distance-based classification scheme to determine the final class/label of the exploratory procedures. Dempster-Shafter Theory (DST) was applied to integrate the distances obtained from all the features. Through the experiments of different dynamics of spiking neurons, the proposed framework achieved a good performance with 95.89% classification accuracy. It is extremely effective in encoding and classifying spatio-temporal data, as compared to Dynamic Time Warping and Hidden Markov Model with 61.30% and 28.70% accuracy. The proposed framework serves as the fundamental block for the development of intelligent interfaces, enhancing the image exploration experience for the blind.},\n bibtype = {article},\n author = {Zhang, Ting and Duerstock, Bradley S. and Wachs, Juan P.},\n doi = {10.1109/TNSRE.2019.2959555},\n journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},\n number = {4}\n}\n\n Individuals who are blind adopt multiple procedures to tactually explore images. Automatically recognizing and classifying users' exploration behaviors is the first step towards the development of an intelligent system that could assist users to explore images more efficiently. In this paper, a computational framework was developed to classify different procedures used by blind users during image exploration. Translation-, rotation- and scale-invariant features were extracted from the trajectories of users' movements. These features were divided as numerical and logical features and were fed into neural networks. More specifically, we trained spiking neural networks (SNNs) to further encode the numerical features as model strings. The proposed framework employed a distance-based classification scheme to determine the final class/label of the exploratory procedures. Dempster-Shafter Theory (DST) was applied to integrate the distances obtained from all the features. Through the experiments of different dynamics of spiking neurons, the proposed framework achieved a good performance with 95.89% classification accuracy. It is extremely effective in encoding and classifying spatio-temporal data, as compared to Dynamic Time Warping and Hidden Markov Model with 61.30% and 28.70% accuracy. The proposed framework serves as the fundamental block for the development of intelligent interfaces, enhancing the image exploration experience for the blind.\n
\n\n\n\n \n\n \n \n \n \n \n \n The AI-Medic: A Multimodal Artificial Intelligent Mentor for Trauma Surgery.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Couperus, K.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings of the 2020 International Conference on Multimodal Interaction, pages 766-767, 10 2020. ACM\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {The AI-Medic: A Multimodal Artificial Intelligent Mentor for Trauma Surgery},\n type = {inproceedings},\n year = {2020},\n keywords = {datasets,neural networks,surgery,telementoring},\n pages = {766-767},\n websites = {https://dl.acm.org/doi/10.1145/3382507.3421167},\n month = {10},\n publisher = {ACM},\n day = {21},\n city = {New York, NY, USA},\n id = {30688ef6-69c0-3cfc-b353-3d4b35c130d1},\n created = {2021-06-04T19:36:51.312Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T15:11:57.147Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not readily available. However, adverse cyber-attacks, unreliable network conditions, and remote mentors' predisposition can significantly jeopardize the remote intervention. To provide medical practitioners with guidance when mentors are unavailable, we present the AI-Medic, the initial steps towards the development of a multimodal intelligent artificial system for autonomous medical mentoring. The system uses a tablet device to acquire the view of an operating field. This imagery is provided to an encoder-decoder neural network trained to predict medical instructions from the current view of a surgery. The network was training using DAISI, a dataset including images and instructions providing step-by-step demonstrations of surgical procedures. The predicted medical instructions are conveyed to the user via visual and auditory modalities.},\n bibtype = {inproceedings},\n author = {Rojas-Muñoz, Edgar and Couperus, Kyle and Wachs, Juan P.},\n doi = {10.1145/3382507.3421167},\n booktitle = {Proceedings of the 2020 International Conference on Multimodal Interaction}\n}\n\n Telementoring generalist surgeons as they treat patients can be essential when in situ expertise is not readily available. However, adverse cyber-attacks, unreliable network conditions, and remote mentors' predisposition can significantly jeopardize the remote intervention. To provide medical practitioners with guidance when mentors are unavailable, we present the AI-Medic, the initial steps towards the development of a multimodal intelligent artificial system for autonomous medical mentoring. The system uses a tablet device to acquire the view of an operating field. This imagery is provided to an encoder-decoder neural network trained to predict medical instructions from the current view of a surgery. The network was training using DAISI, a dataset including images and instructions providing step-by-step demonstrations of surgical procedures. The predicted medical instructions are conveyed to the user via visual and auditory modalities.\n
\n\n\n\n \n\n \n \n \n \n \n \n Telementoring in Leg Fasciotomies via Mixed-Reality: Clinical Evaluation of the STAR Platform.\n \n \n \n \n\n\n \n Rojas-Munõz, E.; Cabrera, M., E.; Lin, C.; Sánchez-Tamayo, N.; Andersen, D.; Popescu, V.; Anderson, K.; Zarzaur, B.; Mullis, B.; and Wachs, J., P.\n\n\n \n\n\n\n In Military Medicine, volume 185, pages 513-520, 2020. \n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Telementoring in Leg Fasciotomies via Mixed-Reality: Clinical Evaluation of the STAR Platform},\n type = {inproceedings},\n year = {2020},\n pages = {513-520},\n volume = {185},\n id = {1c352d79-9397-3c0f-ae8e-e022d497c1a2},\n created = {2021-06-04T19:36:51.313Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T14:50:36.974Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Introduction: Point-of-injury (POI) care requires immediate specialized assistance but delays and expertise lapses can lead to complications. In such scenarios, telementoring can benefit health practitioners by transmitting guidance from remote specialists. However, current telementoring systems are not appropriate for POI care. This article clinically evaluates our System for Telementoring with Augmented Reality (STAR), a novel telementoring system based on an augmented reality head-mounted display. The system is portable, self-contained, and displays virtual surgical guidance onto the operating field. These capabilities can facilitate telementoring in POI scenarios while mitigating limitations of conventional telementoring systems. Methods: Twenty participants performed leg fasciotomies on cadaveric specimens under either one of two experimental conditions: Telementoring using STAR; or without telementoring but reviewing the procedure beforehand. An expert surgeon evaluated the participants' performance in terms of completion time, number of errors, and procedure-related scores. Additional metrics included a self-reported confidence score and postexperiment questionnaires. Results: STAR effectively delivered surgical guidance to nonspecialist health practitioners: Participants using STAR performed fewer errors and obtained higher procedure-related scores. Conclusions: This work validates STAR as a viable surgical telementoring platform, which could be further explored to aid in scenarios where life-saving care must be delivered in a prehospital setting.},\n bibtype = {inproceedings},\n author = {Rojas-Munõz, Edgar and Cabrera, Maria Eugenia and Lin, Chengyuan and Sánchez-Tamayo, Natalia and Andersen, Dan and Popescu, Voicu and Anderson, Kathryn and Zarzaur, Ben and Mullis, Brian and Wachs, Juan P.},\n doi = {10.1093/milmed/usz234},\n booktitle = {Military Medicine}\n}\n\n Introduction: Point-of-injury (POI) care requires immediate specialized assistance but delays and expertise lapses can lead to complications. In such scenarios, telementoring can benefit health practitioners by transmitting guidance from remote specialists. However, current telementoring systems are not appropriate for POI care. This article clinically evaluates our System for Telementoring with Augmented Reality (STAR), a novel telementoring system based on an augmented reality head-mounted display. The system is portable, self-contained, and displays virtual surgical guidance onto the operating field. These capabilities can facilitate telementoring in POI scenarios while mitigating limitations of conventional telementoring systems. Methods: Twenty participants performed leg fasciotomies on cadaveric specimens under either one of two experimental conditions: Telementoring using STAR; or without telementoring but reviewing the procedure beforehand. An expert surgeon evaluated the participants' performance in terms of completion time, number of errors, and procedure-related scores. Additional metrics included a self-reported confidence score and postexperiment questionnaires. Results: STAR effectively delivered surgical guidance to nonspecialist health practitioners: Participants using STAR performed fewer errors and obtained higher procedure-related scores. Conclusions: This work validates STAR as a viable surgical telementoring platform, which could be further explored to aid in scenarios where life-saving care must be delivered in a prehospital setting.\n
\n\n\n\n \n\n \n \n \n \n \n \n Correlation Between Gestures’ Qualitative Properties and Usability metrics.\n \n \n \n \n\n\n \n Chanci, D.; Madapana, N.; Gonzalez, G.; and Wachs, J.\n\n\n \n\n\n\n Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1): 726-730. 2020.\n \n\n\n\n
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\n\n\n@article{\n title = {Correlation Between Gestures’ Qualitative Properties and Usability metrics},\n type = {article},\n year = {2020},\n pages = {726-730},\n volume = {64},\n id = {bfd3cfbb-3b48-352b-84e9-ae841eb205a4},\n created = {2021-06-04T19:36:51.489Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T14:40:42.605Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The choice of best gestures and commands for touchless interfaces is a critical step that determines the user- satisfaction and overall efficiency of surgeon computer interaction. In this regard, usability metrics such as task completion time, error rate, and memorability have a long-standing as potential entities in determining the best gesture vocabulary. In addition, some previous works concerned with this problem have utilized qualitative measures to identify the best gesture. In this work, we hypothesize that there is a correlation between the qualitative properties of gestures (v) and their usability metrics (u). Therefore, we conducted an experiment with linguists to quantify the properties of the gestures. Next, a user study was conducted with surgeons, and the usability metrics were measured. Lastly, linear and non-linear regression techniques were used to find the correlations between u and v. Results show that usability metrics are correlated with the gestures’ qualitative properties ( R 2 = 0.4).},\n bibtype = {article},\n author = {Chanci, Daniela and Madapana, Naveen and Gonzalez, Glebys and Wachs, Juan},\n doi = {10.1177/1071181320641168},\n journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},\n number = {1}\n}\n\n The choice of best gestures and commands for touchless interfaces is a critical step that determines the user- satisfaction and overall efficiency of surgeon computer interaction. In this regard, usability metrics such as task completion time, error rate, and memorability have a long-standing as potential entities in determining the best gesture vocabulary. In addition, some previous works concerned with this problem have utilized qualitative measures to identify the best gesture. In this work, we hypothesize that there is a correlation between the qualitative properties of gestures (v) and their usability metrics (u). Therefore, we conducted an experiment with linguists to quantify the properties of the gestures. Next, a user study was conducted with surgeons, and the usability metrics were measured. Lastly, linear and non-linear regression techniques were used to find the correlations between u and v. Results show that usability metrics are correlated with the gestures’ qualitative properties ( R 2 = 0.4).\n
\n\n\n\n \n\n \n \n \n \n \n \n Agreement Study Using Gesture Description Analysis.\n \n \n \n \n\n\n \n Madapana, N.; Gonzalez, G.; Zhang, L.; Rodgers, R.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Human-Machine Systems, 50(5): 434-443. 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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Paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Agreement Study Using Gesture Description Analysis},\n type = {article},\n year = {2020},\n keywords = {Gesture recognition,agreement analysis,gesture descriptors,human computer interaction,participatory design,user centered design},\n pages = {434-443},\n volume = {50},\n id = {bf243675-7a2d-3103-b1ad-6963cfa70a4e},\n created = {2021-06-04T19:36:51.566Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-16T20:19:52.118Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human-computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based, or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this article, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis. In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate ($\\mathcal SAR$) to measure the level of agreement and provide a mathematical justification for this metric. Furthermore, we perform computational experiments to study the behavior of $\\mathcal SAR$ and demonstrate that existing agreement metrics are a special case of our approach. Our method is evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by $\\mathcal SAR$ is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.},\n bibtype = {article},\n author = {Madapana, Naveen and Gonzalez, Glebys and Zhang, Lingsong and Rodgers, Richard and Wachs, Juan},\n doi = {10.1109/THMS.2020.2992216},\n journal = {IEEE Transactions on Human-Machine Systems},\n number = {5}\n}\n\n Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human-computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based, or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this article, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis. In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate ($\\mathcal SAR$) to measure the level of agreement and provide a mathematical justification for this metric. Furthermore, we perform computational experiments to study the behavior of $\\mathcal SAR$ and demonstrate that existing agreement metrics are a special case of our approach. Our method is evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by $\\mathcal SAR$ is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.\n
\n\n\n\n \n\n \n \n \n \n \n \n Evaluation of an augmented reality platform for austere surgical telementoring: a randomized controlled crossover study in cricothyroidotomies.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Lin, C.; Sanchez-Tamayo, N.; Cabrera, M., E.; Andersen, D.; Popescu, V.; Barragan, J., A.; Zarzaur, B.; Murphy, P.; Anderson, K.; Douglas, T.; Griffis, C.; McKee, J.; Kirkpatrick, A., W.; and Wachs, J., P.\n\n\n \n\n\n\n npj Digital Medicine, 3(1): 75. 12 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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Paper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Evaluation of an augmented reality platform for austere surgical telementoring: a randomized controlled crossover study in cricothyroidotomies},\n type = {article},\n year = {2020},\n pages = {75},\n volume = {3},\n websites = {http://www.nature.com/articles/s41746-020-0284-9},\n month = {12},\n day = {21},\n id = {cce58d36-b5b2-30ee-87e4-de2f064df741},\n created = {2021-06-04T19:36:51.678Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-16T20:14:36.858Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring platforms can help transfer surgical expertise remotely. However, most telementoring platforms are not designed to assist in austere, pre-hospital settings. This paper evaluates the system for telementoring with augmented reality (STAR), a portable and self-contained telementoring platform based on an augmented reality head-mounted display (ARHMD). The system is designed to assist in austere scenarios: a stabilized first-person view of the operating field is sent to a remote expert, who creates surgical instructions that a local first responder wearing the ARHMD can visualize as three-dimensional models projected onto the patient’s body. Our hypothesis evaluated whether remote guidance with STAR could lead to performing a surgical procedure better, as opposed to remote audio-only guidance. Remote expert surgeons guided first responders through training cricothyroidotomies in a simulated austere scenario, and on-site surgeons evaluated the participants using standardized evaluation tools. The evaluation comprehended completion time and technique performance of specific cricothyroidotomy steps. The analyses were also performed considering the participants’ years of experience as first responders, and their experience performing cricothyroidotomies. A linear mixed model analysis showed that using STAR was associated with higher procedural and non-procedural scores, and overall better performance. Additionally, a binary logistic regression analysis showed that using STAR was associated to safer and more successful executions of cricothyroidotomies. This work demonstrates that remote mentors can use STAR to provide first responders with guidance and surgical knowledge, and represents a first step towards the adoption of ARHMDs to convey clinical expertise remotely in austere scenarios.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Lin, Chengyuan and Sanchez-Tamayo, Natalia and Cabrera, Maria Eugenia and Andersen, Daniel and Popescu, Voicu and Barragan, Juan Antonio and Zarzaur, Ben and Murphy, Patrick and Anderson, Kathryn and Douglas, Thomas and Griffis, Clare and McKee, Jessica and Kirkpatrick, Andrew W. and Wachs, Juan P.},\n doi = {10.1038/s41746-020-0284-9},\n journal = {npj Digital Medicine},\n number = {1}\n}\n\n Telementoring platforms can help transfer surgical expertise remotely. However, most telementoring platforms are not designed to assist in austere, pre-hospital settings. This paper evaluates the system for telementoring with augmented reality (STAR), a portable and self-contained telementoring platform based on an augmented reality head-mounted display (ARHMD). The system is designed to assist in austere scenarios: a stabilized first-person view of the operating field is sent to a remote expert, who creates surgical instructions that a local first responder wearing the ARHMD can visualize as three-dimensional models projected onto the patient’s body. Our hypothesis evaluated whether remote guidance with STAR could lead to performing a surgical procedure better, as opposed to remote audio-only guidance. Remote expert surgeons guided first responders through training cricothyroidotomies in a simulated austere scenario, and on-site surgeons evaluated the participants using standardized evaluation tools. The evaluation comprehended completion time and technique performance of specific cricothyroidotomy steps. The analyses were also performed considering the participants’ years of experience as first responders, and their experience performing cricothyroidotomies. A linear mixed model analysis showed that using STAR was associated with higher procedural and non-procedural scores, and overall better performance. Additionally, a binary logistic regression analysis showed that using STAR was associated to safer and more successful executions of cricothyroidotomies. This work demonstrates that remote mentors can use STAR to provide first responders with guidance and surgical knowledge, and represents a first step towards the adoption of ARHMDs to convey clinical expertise remotely in austere scenarios.\n
\n\n\n\n \n\n \n \n \n \n \n \n SARTRES: a semi-autonomous robot teleoperation environment for surgery.\n \n \n \n \n\n\n \n Rahman, M., M.; Balakuntala, M., V.; Gonzalez, G.; Agarwal, M.; Kaur, U.; Venkatesh, V., L., N.; Sanchez-Tamayo, N.; Xue, Y.; Voyles, R., M.; Aggarwal, V.; and Wachs, J.\n\n\n \n\n\n\n Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization,1-8. 11 2020.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {SARTRES: a semi-autonomous robot teleoperation environment for surgery},\n type = {article},\n year = {2020},\n keywords = {Teleoperated Robotic Surgery,surgical activity recognition,surgical vision and perception},\n pages = {1-8},\n websites = {https://www.tandfonline.com/doi/abs/10.1080/21681163.2020.1834878,https://www.tandfonline.com/doi/full/10.1080/21681163.2020.1834878},\n month = {11},\n publisher = {Taylor and Francis Ltd.},\n day = {5},\n id = {11ae94ac-8a51-30b6-a9a0-cfe8cc1a57ed},\n created = {2021-06-04T19:36:51.740Z},\n accessed = {2020-11-22},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-16T20:11:20.795Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Teleoperated surgical robots can provide immediate medical assistance in austere and hostile environments. However, such scenarios are time-sensitive and require high-bandwidth and low-latency communication links that might be unavailable. The system presented in this paper has a standard surgical teleoperation interface, which provides surgeons with an environment in which they are trained. In our semi-autonomous robotic framework, high-level instructions are inferred from the surgeon’s actions and then executed semi-autonomously on the robot. The framework consists of two main modules: (i) Recognition Module–which recognises atomic sub-tasks (i.e., surgemes) performed at the operator end, and (ii) Execution Module–which executes the identified surgemes at the robot end using task contextual information. The peg transfer task was selected for this paper due to its importance in laparoscopic surgical training. The experiments were performed on the DESK surgical dataset to show our framework’s effectiveness using two metrics: user intervention (in the degree of autonomy) and success rate of surgeme execution. We achieved an average accuracy of 91.5% for surgeme recognition and 86% success during surgeme execution. Furthermore, we obtained an average success rate of 53.9% for the overall task, using a model-based approach with a degree of autonomy of 99.33%.},\n bibtype = {article},\n author = {Rahman, Md Masudur and Balakuntala, Mythra V. and Gonzalez, Glebys and Agarwal, Mridul and Kaur, Upinder and Venkatesh, Vishnunandan L.N. N. and Sanchez-Tamayo, Natalia and Xue, Yexiang and Voyles, Richard M. and Aggarwal, Vaneet and Wachs, Juan},\n doi = {10.1080/21681163.2020.1834878},\n journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization}\n}\n\n Teleoperated surgical robots can provide immediate medical assistance in austere and hostile environments. However, such scenarios are time-sensitive and require high-bandwidth and low-latency communication links that might be unavailable. The system presented in this paper has a standard surgical teleoperation interface, which provides surgeons with an environment in which they are trained. In our semi-autonomous robotic framework, high-level instructions are inferred from the surgeon’s actions and then executed semi-autonomously on the robot. The framework consists of two main modules: (i) Recognition Module–which recognises atomic sub-tasks (i.e., surgemes) performed at the operator end, and (ii) Execution Module–which executes the identified surgemes at the robot end using task contextual information. The peg transfer task was selected for this paper due to its importance in laparoscopic surgical training. The experiments were performed on the DESK surgical dataset to show our framework’s effectiveness using two metrics: user intervention (in the degree of autonomy) and success rate of surgeme execution. We achieved an average accuracy of 91.5% for surgeme recognition and 86% success during surgeme execution. Furthermore, we obtained an average success rate of 53.9% for the overall task, using a model-based approach with a degree of autonomy of 99.33%.\n
\n\n\n\n \n\n \n \n \n \n \n \n OBJECTIVE METRICS OF HIGH COGNITIVE WORKLOAD DURING ROBOTIC PARTIAL NEPHRECTOMY: A PILOT STUDY.\n \n \n \n \n\n\n \n Cha, J.; Steward*, J.; Sulek, J.; Sundaram, C.; Wachs, J.; and Yu, D.\n\n\n \n\n\n\n Journal of Urology, 203(Supplement 4). 4 2020.\n \n\n\n\n
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\n\n\n\n@article{\n title = {OBJECTIVE METRICS OF HIGH COGNITIVE WORKLOAD DURING ROBOTIC PARTIAL NEPHRECTOMY: A PILOT STUDY},\n type = {article},\n year = {2020},\n volume = {203},\n websites = {http://www.jurology.com/doi/10.1097/JU.0000000000000902.014},\n month = {4},\n id = {d6c7b115-f235-3711-94be-380964d2df76},\n created = {2021-06-04T19:36:51.824Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T14:40:39.591Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Cha, Jackie and Steward*, James and Sulek, Jay and Sundaram, Chandru and Wachs, Juan and Yu, Denny},\n doi = {10.1097/JU.0000000000000902.014},\n journal = {Journal of Urology},\n number = {Supplement 4}\n}\n\n
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\n \n \n\n \n \n \n \n \n Classification of Blind Users' Image Exploratory Behaviors Using Spike-timing Neural Network.\n \n \n \n\n\n \n Zhang, T.; Duerstock, B.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Neural Systems and Rehabilitation Engineering, Forthcomin. 2019.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Classification of Blind Users' Image Exploratory Behaviors Using Spike-timing Neural Network},\n type = {article},\n year = {2019},\n volume = {Forthcomin},\n id = {c0038ffb-b06f-3f68-8e46-9eaff4461649},\n created = {2019-09-26T21:58:25.024Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:57.852Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Zhang2019},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Zhang, T. and Duerstock, B.S. and Wachs, J.P.},\n journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering}\n}\n\n \n\n \n \n \n \n \n Multimodal Physiological Signals for Workload Prediction in Robot-Assisted Surgery.\n \n \n \n\n\n \n Zhou, T.; Gonzales, H.; Chu, J.; Yu, D.; and Wachs, J.\n\n\n \n\n\n\n Transactions on Human-Robot Interaction. 2019.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Multimodal Physiological Signals for Workload Prediction in Robot-Assisted Surgery},\n type = {article},\n year = {2019},\n id = {1a20e40c-0599-38f3-b764-99182de5f302},\n created = {2019-12-19T03:17:34.822Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:57.679Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Zhou2019a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Zhou, T. and Gonzales, H. and Chu, J. and Yu, D. and Wachs, J.},\n journal = {Transactions on Human-Robot Interaction}\n}\n\n \n\n \n \n \n \n \n Augmented reality as a medium for improved telementoring.\n \n \n \n\n\n \n Rojas-Munõz, E.; Andersen, D.; Cabrera, M.; Popescu, V.; Marley, S.; Zarzaur, B.; Mullis, B.; and Wachs, J.\n\n\n \n\n\n\n Military Medicine, 184. 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Augmented reality as a medium for improved telementoring},\n type = {article},\n year = {2019},\n keywords = {augmented reality,surgical telementoring,telemedicine},\n volume = {184},\n id = {6bd9ce21-a78c-37cc-8b4d-789f3fb039c1},\n created = {2019-04-09T23:59:00.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-07-06T18:38:49.084Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2019 Association of Military Surgeons of the United States. All rights reserved. Combat trauma injuries require urgent and specialized care. When patient evacuation is infeasible, critical life-saving care must be given at the point of injury in real-time and under austere conditions associated to forward operating bases. Surgical telementoring allows local generalists to receive remote instruction from specialists thousands of miles away. However, current telementoring systems have limited annotation capabilities and lack of direct visualization of the future result of the surgical actions by the specialist. The System for Telementoring with Augmented Reality (STAR) is a surgical telementoring platform that improves the transfer of medical expertise by integrating a full-size interaction table for mentors to create graphical annotations, with augmented reality (AR) devices to display surgical annotations directly onto the generalist's field of view. Along with the explanation of the system's features, this paper provides results of user studies that validate STAR as a comprehensive AR surgical telementoring platform. In addition, potential future applications of STAR are discussed, which are desired features that state-of-the-art AR medical telementoring platforms should have when combat trauma scenarios are in the spotlight of such technologies.},\n bibtype = {article},\n author = {Rojas-Munõz, E. and Andersen, D. and Cabrera, M.E. and Popescu, V. and Marley, S. and Zarzaur, B. and Mullis, B. and Wachs, J.P.},\n doi = {10.1093/milmed/usy300},\n journal = {Military Medicine}\n}\n\n © 2019 Association of Military Surgeons of the United States. All rights reserved. Combat trauma injuries require urgent and specialized care. When patient evacuation is infeasible, critical life-saving care must be given at the point of injury in real-time and under austere conditions associated to forward operating bases. Surgical telementoring allows local generalists to receive remote instruction from specialists thousands of miles away. However, current telementoring systems have limited annotation capabilities and lack of direct visualization of the future result of the surgical actions by the specialist. The System for Telementoring with Augmented Reality (STAR) is a surgical telementoring platform that improves the transfer of medical expertise by integrating a full-size interaction table for mentors to create graphical annotations, with augmented reality (AR) devices to display surgical annotations directly onto the generalist's field of view. Along with the explanation of the system's features, this paper provides results of user studies that validate STAR as a comprehensive AR surgical telementoring platform. In addition, potential future applications of STAR are discussed, which are desired features that state-of-the-art AR medical telementoring platforms should have when combat trauma scenarios are in the spotlight of such technologies.\n
\n\n\n\n \n\n \n \n \n \n \n A “human-proof pointy-end”: A robotically applied hemostatic clamp for care-under-fire.\n \n \n \n\n\n \n McKee, I.; McKee, J.; Knudsen, B.; Shelton, R.; LaPorta, T.; Wachs, J.; and Kirkpatrick, A.\n\n\n \n\n\n\n Canadian Journal of Surgery, 62(6). 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {A “human-proof pointy-end”: A robotically applied hemostatic clamp for care-under-fire},\n type = {article},\n year = {2019},\n volume = {62},\n id = {d09037a5-2dd4-3fbc-8919-9386adb69c74},\n created = {2019-12-12T23:59:00.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:08.596Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {true},\n abstract = {© 2019 Joule Inc. or its licensors. Providing the earliest hemorrhage control is now recognized as a shared responsibility of all members of society, including both the lay public and professionals, consistent with the Stop the Bleed campaign. However, providing early hemorrhage control in a hostile environment, such as the scene of a mass shooting, is extremely challenging. In such settings, the first access to a bleeding victim may be robotic. An all-purpose bomb robot was thus retrofitted with a commercial, off-the-shelf wound clamp and successfully applied to an extremity exsanguination simulator as a demonstration of remote robotic hemorrhage control. As this method can potentially control extremity hemorrhage, further development of the techniques, equipment and, most importantly, the guidelines and rules of engagement should continue. We suggest that in order to minimize the loss of life during an active shooter incident, the armamentarium of prehospital medical resources may be extended to include law-enforcement robots.},\n bibtype = {article},\n author = {McKee, I.A. and McKee, J.L. and Knudsen, B.E. and Shelton, R. and LaPorta, T. and Wachs, J. and Kirkpatrick, A.W.},\n doi = {10.1503/cjs.019318},\n journal = {Canadian Journal of Surgery},\n number = {6}\n}\n\n © 2019 Joule Inc. or its licensors. Providing the earliest hemorrhage control is now recognized as a shared responsibility of all members of society, including both the lay public and professionals, consistent with the Stop the Bleed campaign. However, providing early hemorrhage control in a hostile environment, such as the scene of a mass shooting, is extremely challenging. In such settings, the first access to a bleeding victim may be robotic. An all-purpose bomb robot was thus retrofitted with a commercial, off-the-shelf wound clamp and successfully applied to an extremity exsanguination simulator as a demonstration of remote robotic hemorrhage control. As this method can potentially control extremity hemorrhage, further development of the techniques, equipment and, most importantly, the guidelines and rules of engagement should continue. We suggest that in order to minimize the loss of life during an active shooter incident, the armamentarium of prehospital medical resources may be extended to include law-enforcement robots.\n
\n\n\n\n \n\n \n \n \n \n \n A Conceptual Model For Tool Handling In The Operation Room.\n \n \n \n\n\n \n Wachs, J.; and Dori, D.\n\n\n \n\n\n\n In 2019. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {A Conceptual Model For Tool Handling In The Operation Room},\n type = {inproceedings},\n year = {2019},\n id = {06da5961-a47e-37f1-9833-a51be595ee7c},\n created = {2021-06-04T19:22:34.290Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.013Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Wachs, Juan and Dori, Dov},\n doi = {10.5339/qfarc.2014.itpp0604}\n}\n\n \n\n \n \n \n \n \n Creating a physical tele-mentored ultrasound supported medical interventions (TMUSMI) Box.\n \n \n \n\n\n \n McKee, J., J., L.; McBeth, P., B.; Wachs, J.; Hamilton, D.; Ball, C., G., C.; Gillman, L.; Kirkpatrick, A., A., W.; McKee, J., J., L.; McBeth, P., B.; Hamilton, D.; Wachs, J.; Ball, C., G., C.; and Gillman, L.\n\n\n \n\n\n\n In Presented in Oral format at the 2019 Annual Scientific Meeting of the Trauma Association of Canada, volume 62, pages 18-19, 2019. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Creating a physical tele-mentored ultrasound supported medical interventions (TMUSMI) Box},\n type = {inproceedings},\n year = {2019},\n pages = {18-19},\n volume = {62},\n city = {Calgary, Alberta, Canada},\n id = {77935587-6f1e-3a70-b753-ad9cfff4c956},\n created = {2021-06-04T19:36:47.484Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.358Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Kirkpatrick},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {McKee, JL Jessica L. and McBeth, Paul B. and Wachs, JP and Hamilton, D and Ball, Chad G. CG and Gillman, L and Kirkpatrick, AW Andrew W. and McKee, JL Jessica L. and McBeth, Paul B. and Hamilton, D and Wachs, JP and Ball, Chad G. CG and Gillman, L},\n booktitle = {Presented in Oral format at the 2019 Annual Scientific Meeting of the Trauma Association of Canada}\n}\n\n \n\n \n \n \n \n \n \n DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots.\n \n \n \n \n\n\n \n Madapana, N.; Low, T.; Voyles, R., M.; Xue, Y.; Wachs, J., J.; Rahman, M., M., M., M.; Sanchez-Tamayo, N.; Balakuntala, M., V., M., M.; Gonzalez, G.; Bindu, J., P., J., J.; Vishnunandan Venkatesh, L., N.; Zhang, X.; Noguera, J., B., J., J.; Low, T.; Voyles, R., M.; Xue, Y.; Wachs, J., J.; Venkatesh, L., N., V.; Zhang, X.; Noguera, J., B., J., J.; Low, T.; Voyles, R., M.; Wachs, J., J.; Xue, Y.; and Wachs, J., J.\n\n\n \n\n\n\n In International Conference on Intelligent Robots and Systems (IROS)., pages 6928-6934, 3 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots},\n type = {inproceedings},\n year = {2019},\n pages = {6928-6934},\n websites = {http://arxiv.org/abs/1903.00959},\n month = {3},\n day = {3},\n id = {7d988764-e0f6-3387-897a-83aae62a7739},\n created = {2021-06-04T19:36:47.679Z},\n accessed = {2019-03-04},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.407Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2019a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semi-autonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (DExterous Surgical SKills) dataset. It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset was used to test the idea of transferring knowledge across different domains (e.g. from Taurus to YuMi robot) for a surgical gesture classification task with seven gestures/surgemes. We explored two different scenarios: 1) No transfer and 2) Domain transfer (simulated Taurus to real Taurus and YuMi robots). We conducted extensive experiments with three supervised learning models and provided baselines in each of these scenarios. Results show that using simulation data during training enhances the performance on the real robots, where limited real data is available. In particular, we obtained an accuracy of 55% on the real Taurus data using a model that is trained only on the simulator data, but that accuracy improved to 82% when the ratio of real to simulated data was increased to 0.18 in the training set.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Low, Thomas and Voyles, Richard M. and Xue, Yexiang and Wachs, JP Juan and Rahman, M.M. Md Masudur MM and Sanchez-Tamayo, Natalia and Balakuntala, Mythra V. MV M.V. and Gonzalez, Glebys and Bindu, Jyothsna Padmakumar JP J.P. and Vishnunandan Venkatesh, L.N. N. and Zhang, Xingguang and Noguera, Juan Barragan J.B. JB and Low, Thomas and Voyles, Richard M. and Xue, Yexiang and Wachs, JP Juan and Venkatesh, LN N. Vishnunandan and Zhang, Xingguang and Noguera, Juan Barragan J.B. JB and Low, Thomas and Voyles, Richard M. and Wachs, JP Juan and Xue, Yexiang and Wachs, JP Juan},\n doi = {10.1109/IROS40897.2019.8967760},\n booktitle = {International Conference on Intelligent Robots and Systems (IROS).}\n}\n\n Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semi-autonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (DExterous Surgical SKills) dataset. It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset was used to test the idea of transferring knowledge across different domains (e.g. from Taurus to YuMi robot) for a surgical gesture classification task with seven gestures/surgemes. We explored two different scenarios: 1) No transfer and 2) Domain transfer (simulated Taurus to real Taurus and YuMi robots). We conducted extensive experiments with three supervised learning models and provided baselines in each of these scenarios. Results show that using simulation data during training enhances the performance on the real robots, where limited real data is available. In particular, we obtained an accuracy of 55% on the real Taurus data using a model that is trained only on the simulator data, but that accuracy improved to 82% when the ratio of real to simulated data was increased to 0.18 in the training set.\n
\n\n\n\n \n\n \n \n \n \n \n \n Extending Policy from One-Shot Learning through Coaching.\n \n \n \n \n\n\n \n Balakuntala, M., V., M.; Venkatesh, V., L., V., V., L., N.; Bindu, J., P., J.; Voyles, R., M., R.; and Wachs, J.\n\n\n \n\n\n\n In 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019, pages 1-7, 5 2019. Institute of Electrical and Electronics Engineers Inc.\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Extending Policy from One-Shot Learning through Coaching},\n type = {inproceedings},\n year = {2019},\n pages = {1-7},\n websites = {http://arxiv.org/abs/1905.04841,https://ieeexplore.ieee.org/document/8956364/},\n month = {5},\n publisher = {Institute of Electrical and Electronics Engineers Inc.},\n day = {12},\n id = {e0021c17-79d1-3483-8168-fcba4e3f5e4e},\n created = {2021-06-04T19:36:47.901Z},\n accessed = {2020-11-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.814Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations, often followed by episodes of coaching that tune and refine the execution during practice. Adopting a similar framework for teaching robots through demonstrations makes teaching tasks highly intuitive and imitating the refinement of complex tasks through coaching improves the efficacy. Unlike traditional Learning from Demonstration (LfD) approaches which rely on multiple demonstrations to train a task, we present a novel one-shot learning from demonstration approach, augmented by coaching, to transfer the task from task expert to robot. The demonstration is automatically segmented into a sequence of a priori skills (the task policy) parametrized to match task goals. During practice, the robotic skills self-evaluate their performances and refine the task policy to locally optimize cumulative performance. Then, human coaching further refines the task policy to explore and globally optimize the net performance. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. The proposed approach is evaluated using the task of scooping and unscooping granular media. The self-evaluator of the scooping skill uses the realtime force signature and resistive force theory to minimize scooping resistance similar to how humans scoop. Coaching feedback focuses modifications to sub-domains of the action space, using RL to converge to desired performance. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching achieving a success rate of ≈90%.},\n bibtype = {inproceedings},\n author = {Balakuntala, Mythra V. M.V. and Venkatesh, Vishnunandan L.N. V.L.N. Vishnunandan L. N. and Bindu, Jyothsna Padmakumar J.P. and Voyles, Richard M. R.M. and Wachs, Juan},\n doi = {10.1109/RO-MAN46459.2019.8956364},\n booktitle = {2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019}\n}\n\n Humans generally teach their fellow collaborators to perform tasks through a small number of demonstrations, often followed by episodes of coaching that tune and refine the execution during practice. Adopting a similar framework for teaching robots through demonstrations makes teaching tasks highly intuitive and imitating the refinement of complex tasks through coaching improves the efficacy. Unlike traditional Learning from Demonstration (LfD) approaches which rely on multiple demonstrations to train a task, we present a novel one-shot learning from demonstration approach, augmented by coaching, to transfer the task from task expert to robot. The demonstration is automatically segmented into a sequence of a priori skills (the task policy) parametrized to match task goals. During practice, the robotic skills self-evaluate their performances and refine the task policy to locally optimize cumulative performance. Then, human coaching further refines the task policy to explore and globally optimize the net performance. Both the self-evaluation and coaching are implemented using reinforcement learning (RL) methods. The proposed approach is evaluated using the task of scooping and unscooping granular media. The self-evaluator of the scooping skill uses the realtime force signature and resistive force theory to minimize scooping resistance similar to how humans scoop. Coaching feedback focuses modifications to sub-domains of the action space, using RL to converge to desired performance. Thus, the proposed method provides a framework for learning tasks from one demonstration and generalizing it using human feedback through coaching achieving a success rate of ≈90%.\n
\n\n\n\n \n\n \n \n \n \n \n \n MAGIC: A fundamental framework for gesture representation, comparison and assessment.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Wachs, J., J., P.; Rojas-Munoz, E.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, pages 1-8, 5 2019. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {MAGIC: A fundamental framework for gesture representation, comparison and assessment},\n type = {inproceedings},\n year = {2019},\n pages = {1-8},\n websites = {https://ieeexplore.ieee.org/document/8756534/},\n month = {5},\n publisher = {IEEE},\n id = {b8403992-fd12-3e0c-82df-841cafc2dd23},\n created = {2021-06-04T19:36:48.006Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.842Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {03b70259-4210-4c36-8d9b-3f13198ce1ab,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Gestures play a fundamental role in instructional processes between agents. However, effectively transferring this non-verbal information becomes complex when the agents are not physically co-located. Recently, remote collaboration systems that transfer gestural information have been developed. Nonetheless, these systems relegate gestures to an illustrative role: only a representation of the gesture is transmitted. We argue that further comparisons between the gestures can provide information of how well the tasks are being understood and performed. While gesture comparison frameworks exist, they only rely on gesture's appearance, leaving semantics and pragmatical aspects aside. This work introduces the Multi-Agent Gestural Instructions Comparer (MAGIC), an architecture that represents and compares gestures at the morphological, semantical and pragmatical levels. MAGIC abstracts gestures via a three-stage pipeline based on a taxonomy classification, a dynamic semantics framework and a constituency parsing; and utilizes a comparison scheme based on subtrees intersections to describe gesture similarity. This work shows the feasibility of the framework by assessing MAGIC's gesture matching accuracy against other gesture comparison frameworks during a mentor-mentee remote collaborative physical task scenario.},\n bibtype = {inproceedings},\n author = {Rojas-Muñoz, Edgar and Wachs, J.P. Juan P. and Rojas-Munoz, Edgar and Wachs, J.P. Juan P.},\n doi = {10.1109/FG.2019.8756534},\n booktitle = {Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019}\n}\n\n Gestures play a fundamental role in instructional processes between agents. However, effectively transferring this non-verbal information becomes complex when the agents are not physically co-located. Recently, remote collaboration systems that transfer gestural information have been developed. Nonetheless, these systems relegate gestures to an illustrative role: only a representation of the gesture is transmitted. We argue that further comparisons between the gestures can provide information of how well the tasks are being understood and performed. While gesture comparison frameworks exist, they only rely on gesture's appearance, leaving semantics and pragmatical aspects aside. This work introduces the Multi-Agent Gestural Instructions Comparer (MAGIC), an architecture that represents and compares gestures at the morphological, semantical and pragmatical levels. MAGIC abstracts gestures via a three-stage pipeline based on a taxonomy classification, a dynamic semantics framework and a constituency parsing; and utilizes a comparison scheme based on subtrees intersections to describe gesture similarity. This work shows the feasibility of the framework by assessing MAGIC's gesture matching accuracy against other gesture comparison frameworks during a mentor-mentee remote collaborative physical task scenario.\n
\n\n\n\n \n\n \n \n \n \n \n Spiking Neural Networks for early prediction in human–robot collaboration.\n \n \n \n\n\n \n Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n International Journal of Robotics Research, 38(14): 1619-1643. 9 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Spiking Neural Networks for early prediction in human–robot collaboration},\n type = {article},\n year = {2019},\n keywords = {Cognitive human–robot interaction,cognitive robotics,gesture,human-centered and life-like robotics,learning and adaptive systems,medical robots and systems,posture,social spaces and facial expressions},\n pages = {1619-1643},\n volume = {38},\n month = {9},\n id = {0bd3acc4-f88c-3242-9869-a272dbe8f05d},\n created = {2021-06-04T19:36:48.296Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.284Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2019},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This article introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about a human or agent’s intentions. The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon’s turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet’s performance in early turn-taking prediction. It was found to reach an (Formula presented.) score of 0.683 given 10% of completed action, and an (Formula presented.) score of 0.852 at 50% and 0.894 at 100% of the completed action. This performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (<40%). Such early turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.},\n bibtype = {article},\n author = {Zhou, Tian and Wachs, J.P. Juan P.},\n doi = {10.1177/0278364919872252},\n journal = {International Journal of Robotics Research},\n number = {14}\n}\n\n This article introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about a human or agent’s intentions. The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon’s turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet’s performance in early turn-taking prediction. It was found to reach an (Formula presented.) score of 0.683 given 10% of completed action, and an (Formula presented.) score of 0.852 at 50% and 0.894 at 100% of the completed action. This performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (<40%). Such early turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.\n
\n\n\n\n \n\n \n \n \n \n \n Augmented reality as a medium for improved telementoring.\n \n \n \n\n\n \n Rojas-Munõz, E.; Andersen, D.; Cabrera, M., M., E.; Popescu, V.; Marley, S.; Zarzaur, B.; Mullis, B.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Military Medicine, volume 184, pages 57-64, 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Augmented reality as a medium for improved telementoring},\n type = {inproceedings},\n year = {2019},\n keywords = {augmented reality,surgical telementoring,telemedicine},\n pages = {57-64},\n volume = {184},\n id = {79d4e10f-433b-36b4-8d0d-3f146ba80be5},\n created = {2021-06-04T19:36:48.798Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.876Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Combat trauma injuries require urgent and specialized care. When patient evacuation is infeasible, critical life-saving care must be given at the point of injury in real-time and under austere conditions associated to forward operating bases. Surgical telementoring allows local generalists to receive remote instruction from specialists thousands of miles away. However, current telementoring systems have limited annotation capabilities and lack of direct visualization of the future result of the surgical actions by the specialist. The System for Telementoring with Augmented Reality (STAR) is a surgical telementoring platform that improves the transfer of medical expertise by integrating a full-size interaction table for mentors to create graphical annotations, with augmented reality (AR) devices to display surgical annotations directly onto the generalist's field of view. Along with the explanation of the system's features, this paper provides results of user studies that validate STAR as a comprehensive AR surgical telementoring platform. In addition, potential future applications of STAR are discussed, which are desired features that state-of-the-art AR medical telementoring platforms should have when combat trauma scenarios are in the spotlight of such technologies.},\n bibtype = {inproceedings},\n author = {Rojas-Munõz, Edgar and Andersen, Dan and Cabrera, M.E. Maria Eugenia and Popescu, Voicu and Marley, Sherri and Zarzaur, Ben and Mullis, Brian and Wachs, J.P. Juan P.},\n doi = {10.1093/milmed/usy300},\n booktitle = {Military Medicine}\n}\n\n Combat trauma injuries require urgent and specialized care. When patient evacuation is infeasible, critical life-saving care must be given at the point of injury in real-time and under austere conditions associated to forward operating bases. Surgical telementoring allows local generalists to receive remote instruction from specialists thousands of miles away. However, current telementoring systems have limited annotation capabilities and lack of direct visualization of the future result of the surgical actions by the specialist. The System for Telementoring with Augmented Reality (STAR) is a surgical telementoring platform that improves the transfer of medical expertise by integrating a full-size interaction table for mentors to create graphical annotations, with augmented reality (AR) devices to display surgical annotations directly onto the generalist's field of view. Along with the explanation of the system's features, this paper provides results of user studies that validate STAR as a comprehensive AR surgical telementoring platform. In addition, potential future applications of STAR are discussed, which are desired features that state-of-the-art AR medical telementoring platforms should have when combat trauma scenarios are in the spotlight of such technologies.\n
\n\n\n\n \n\n \n \n \n \n \n \n Transferring Dexterous Surgical Skill Knowledge between Robots for Semi-autonomous Teleoperation.\n \n \n \n \n\n\n \n Rahman, M., M., M.; Sanchez-Tamayo, N.; Gonzalez, G.; Agarwal, M.; Aggarwal, V.; Voyles, R., M., R.; Xue, Y.; and Wachs, J.\n\n\n \n\n\n\n In 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019, 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Transferring Dexterous Surgical Skill Knowledge between Robots for Semi-autonomous Teleoperation},\n type = {inproceedings},\n year = {2019},\n id = {ad9bc67f-067b-3028-b939-dbf1de7ed2e9},\n created = {2021-06-04T19:36:49.017Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-21T19:01:30.744Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In the future, deployable, teleoperated surgical robots can save the lives of critically injured patients in battlefield environments. These robotic systems will need to have autonomous capabilities to take over during communication delays and unexpected environmental conditions during critical phases of the procedure. Understanding and predicting the next surgical actions (referred as 'surgemes') is essential for autonomous surgery. Most approaches for surgeme recognition cannot cope with the high variability associated with austere environments and thereby cannot 'transfer' well to field robotics. We propose a methodology that uses compact image representations with kinematic features for surgeme recognition in the DESK dataset. This dataset offers samples for surgical procedures over different robotic platforms with a high variability in the setup. We performed surgeme classification in two setups: 1) No transfer, 2) Transfer from a simulated scenario to two real deployable robots. Then, the results were compared with recognition accuracies using only kinematic data with the same experimental setup. The results show that our approach improves the recognition performance over kinematic data across different domains. The proposed approach produced a transfer accuracy gain up to 20% between the simulated and the real robot, and up to 31% between the simulated robot and a different robot. A transfer accuracy gain was observed for all cases, even those already above 90%.},\n bibtype = {inproceedings},\n author = {Rahman, M.M. Md Masudur and Sanchez-Tamayo, Natalia and Gonzalez, Glebys and Agarwal, Mridul and Aggarwal, Vaneet and Voyles, Richard M. R.M. and Xue, Yexiang and Wachs, Juan},\n doi = {10.1109/RO-MAN46459.2019.8956396},\n booktitle = {2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019}\n}\n\n In the future, deployable, teleoperated surgical robots can save the lives of critically injured patients in battlefield environments. These robotic systems will need to have autonomous capabilities to take over during communication delays and unexpected environmental conditions during critical phases of the procedure. Understanding and predicting the next surgical actions (referred as 'surgemes') is essential for autonomous surgery. Most approaches for surgeme recognition cannot cope with the high variability associated with austere environments and thereby cannot 'transfer' well to field robotics. We propose a methodology that uses compact image representations with kinematic features for surgeme recognition in the DESK dataset. This dataset offers samples for surgical procedures over different robotic platforms with a high variability in the setup. We performed surgeme classification in two setups: 1) No transfer, 2) Transfer from a simulated scenario to two real deployable robots. Then, the results were compared with recognition accuracies using only kinematic data with the same experimental setup. The results show that our approach improves the recognition performance over kinematic data across different domains. The proposed approach produced a transfer accuracy gain up to 20% between the simulated and the real robot, and up to 31% between the simulated robot and a different robot. A transfer accuracy gain was observed for all cases, even those already above 90%.\n
\n\n\n\n \n\n \n \n \n \n \n \n Augmented Reality Future Step Visualization for Robust Surgical Telementoring.\n \n \n \n \n\n\n \n Andersen, D., D., S.; Cabrera, M., M., E.; Rojas-Muñoz, E., E., J.; Popescu, V., S., V.; Gonzalez, G., G., T.; Mullis, B.; Marley, S.; Zarzaur, B., B., L.; and Wachs, J., J., P.\n\n\n \n\n\n\n Simulation in Healthcare, 14(1): 59-66. 2 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Augmented Reality Future Step Visualization for Robust Surgical Telementoring},\n type = {article},\n year = {2019},\n keywords = {Augmented reality,simulator training,telemedicine,telementoring},\n pages = {59-66},\n volume = {14},\n websites = {http://journals.lww.com/01266021-201902000-00008,http://insights.ovid.com/crossref?an=01266021-201902000-00008},\n month = {2},\n id = {55c64f85-ca2e-3bb8-a0b0-746f6fcee23e},\n created = {2021-06-04T19:36:50.053Z},\n accessed = {2019-09-26},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.191Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Andersen2019},\n folder_uuids = {0f5aa25e-250b-450c-a560-2626eae40317,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Introduction Surgical telementoring connects expert mentors with trainees performing urgent care in austere environments. However, such environments impose unreliable network quality, with significant latency and low bandwidth. We have developed an augmented reality telementoring system that includes future step visualization of the medical procedure. Pregenerated video instructions of the procedure are dynamically overlaid onto the trainee's view of the operating field when the network connection with a mentor is unreliable. Methods Our future step visualization uses a tablet suspended above the patient's body, through which the trainee views the operating field. Before trainee use, an expert records a "future library" of step-by-step video footage of the operation. Videos are displayed to the trainee as semitransparent graphical overlays. We conducted a study where participants completed a cricothyroidotomy under telementored guidance. Participants used one of two telementoring conditions: conventional telestrator or our system with future step visualization. During the operation, the connection between trainee and mentor was bandwidth throttled. Recorded metrics were idle time ratio, recall error, and task performance. Results Participants in the future step visualization condition had 48% smaller idle time ratio (14.5% vs. 27.9%, P < 0.001), 26% less recall error (119 vs. 161, P = 0.042), and 10% higher task performance scores (rater 1 = 90.83 vs. 81.88, P = 0.008; rater 2 = 88.54 vs. 79.17, P = 0.042) than participants in the telestrator condition. Conclusions Future step visualization in surgical telementoring is an important fallback mechanism when trainee/mentor network connection is poor, and it is a key step towards semiautonomous and then completely mentor-free medical assistance systems.},\n bibtype = {article},\n author = {Andersen, D.S. Daniel S. and Cabrera, M.E. Maria E. and Rojas-Muñoz, E.J. Edgar J. and Popescu, Voicu S. V.S. and Gonzalez, G.T. Glebys T. and Mullis, Brian and Marley, Sherri and Zarzaur, B.L. Ben L. and Wachs, J.P. Juan P.},\n doi = {10.1097/SIH.0000000000000334},\n journal = {Simulation in Healthcare},\n number = {1}\n}\n\n Introduction Surgical telementoring connects expert mentors with trainees performing urgent care in austere environments. However, such environments impose unreliable network quality, with significant latency and low bandwidth. We have developed an augmented reality telementoring system that includes future step visualization of the medical procedure. Pregenerated video instructions of the procedure are dynamically overlaid onto the trainee's view of the operating field when the network connection with a mentor is unreliable. Methods Our future step visualization uses a tablet suspended above the patient's body, through which the trainee views the operating field. Before trainee use, an expert records a \"future library\" of step-by-step video footage of the operation. Videos are displayed to the trainee as semitransparent graphical overlays. We conducted a study where participants completed a cricothyroidotomy under telementored guidance. Participants used one of two telementoring conditions: conventional telestrator or our system with future step visualization. During the operation, the connection between trainee and mentor was bandwidth throttled. Recorded metrics were idle time ratio, recall error, and task performance. Results Participants in the future step visualization condition had 48% smaller idle time ratio (14.5% vs. 27.9%, P < 0.001), 26% less recall error (119 vs. 161, P = 0.042), and 10% higher task performance scores (rater 1 = 90.83 vs. 81.88, P = 0.008; rater 2 = 88.54 vs. 79.17, P = 0.042) than participants in the telestrator condition. Conclusions Future step visualization in surgical telementoring is an important fallback mechanism when trainee/mentor network connection is poor, and it is a key step towards semiautonomous and then completely mentor-free medical assistance systems.\n
\n\n\n\n \n\n \n \n \n \n \n \n Robust high-level video stabilization for effective AR telementoring.\n \n \n \n \n\n\n \n Lin, C.; Rojas-Munoz, E.; Cabrera, M., M., E.; Sanchez-Tamayo, N.; Andersen, D.; Popescu, V.; Noguera, J., J., A., B.; Zarzaur, B.; Murphy, P.; Anderson, K.; Douglas, T.; Griffis, C.; and Wachs, J.\n\n\n \n\n\n\n In 26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings, pages 1038-1039, 3 2019. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Robust high-level video stabilization for effective AR telementoring},\n type = {inproceedings},\n year = {2019},\n keywords = {Centered computing,Collaborative interaction Human,Human,Human computer interaction (HCI),Interaction paradigms,Mixed / augmented reality,Visualization,Visualization design and evaluation methods Human},\n pages = {1038-1039},\n websites = {https://ieeexplore.ieee.org/document/8798331/},\n month = {3},\n publisher = {IEEE},\n id = {3e608804-86fb-341b-8d31-61d40ebb9d68},\n created = {2021-06-04T19:36:50.250Z},\n accessed = {2019-09-26},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.437Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lin2019},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This poster presents the design, implementation, and evaluation of a method for robust high-level stabilization of mentees first-person video in augmented reality (AR) telementoring. This video is captured by the front-facing built-in camera of an AR headset and stabilized by rendering from a stationary view a planar proxy of the workspace projectively texture mapped with the video feed. The result is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's default viewpoint. The stabilization method was evaluated in two user studies, in the context of number matching and for cricothyroidotomy training, respectively. Both showed a significant advantage of our method compared with unstabilized visualization.},\n bibtype = {inproceedings},\n author = {Lin, Chengyuan and Rojas-Munoz, Edgar and Cabrera, M.E. Maria Eugenia and Sanchez-Tamayo, Natalia and Andersen, Daniel and Popescu, Voicu and Noguera, J.A.B. Juan Antonio Barragan and Zarzaur, Ben and Murphy, Pat and Anderson, Kathryn and Douglas, Thomas and Griffis, Clare and Wachs, Juan},\n doi = {10.1109/VR.2019.8798331},\n booktitle = {26th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019 - Proceedings}\n}\n\n This poster presents the design, implementation, and evaluation of a method for robust high-level stabilization of mentees first-person video in augmented reality (AR) telementoring. This video is captured by the front-facing built-in camera of an AR headset and stabilized by rendering from a stationary view a planar proxy of the workspace projectively texture mapped with the video feed. The result is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's default viewpoint. The stabilization method was evaluated in two user studies, in the context of number matching and for cricothyroidotomy training, respectively. Both showed a significant advantage of our method compared with unstabilized visualization.\n
\n\n\n\n \n\n \n \n \n \n \n Preference elicitation: Obtaining gestural guidelines for PACS in neurosurgery.\n \n \n \n\n\n \n Madapana, N.; Gonzalez, G.; Taneja, R.; Rodgers, R.; Zhang, L.; and Wachs, J.\n\n\n \n\n\n\n International Journal of Medical Informatics, 130. 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Preference elicitation: Obtaining gestural guidelines for PACS in neurosurgery},\n type = {article},\n year = {2019},\n keywords = {Gestures,MRI scans,Neurosurgery,PACS,Radiology},\n volume = {130},\n id = {e062530c-f923-3378-b766-7817d6b36899},\n created = {2021-06-04T19:36:50.398Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.611Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2019},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective: Accessing medical records is an integral part of neurosurgical procedures in the Operating Room (OR). Gestural interfaces can help reduce the risks for infections by allowing the surgical staff to browse Picture Archiving and Communication Systems (PACS) without touch. The main objectives of this work are to: a) Elicit gestures from neurosurgeons to analyze their preferences, b) Develop heuristics for gestural interfaces, and c) Produce a lexicon that maximizes surgeons’ preferences. Materials and methods: A gesture elicitation study was conducted with nine neurosurgeons. Initially, subjects were asked to outline the gestures on a drawing board for each of the PACS commands. Next, the subjects performed one of three imaging tasks using gestures instead of the keyboard and mouse. Each gesture was annotated with respect to the presence/absence of gesture descriptors. Next, K-nearest neighbor approach was used to obtain the final lexicon that complies with the preferred/popular descriptors. Results: The elicitation study resulted in nine gesture lexicons, each comprised of 28 gestures. A paired t-test between the popularity of the overall gesture and the top three descriptors showed that the latter is significantly higher than the former (89.5%-59.7% vs 19.4%, p < 0.001), meaning more than half of the subjects agreed on these descriptors. Next, the gesture heuristics were generated for each command using the popular descriptors. Lastly, we developed a lexicon that complies with surgeons’ preferences. Conclusions: Neurosurgeons do agree on fundamental characteristics of gestures to perform image manipulation tasks. The proposed heuristics could potentially guide the development of future gesture-based interaction of PACS for the OR.},\n bibtype = {article},\n author = {Madapana, Naveen and Gonzalez, Glebys and Taneja, Rahul and Rodgers, Richard and Zhang, Lingsong and Wachs, Juan},\n doi = {10.1016/j.ijmedinf.2019.07.013},\n journal = {International Journal of Medical Informatics}\n}\n\n Objective: Accessing medical records is an integral part of neurosurgical procedures in the Operating Room (OR). Gestural interfaces can help reduce the risks for infections by allowing the surgical staff to browse Picture Archiving and Communication Systems (PACS) without touch. The main objectives of this work are to: a) Elicit gestures from neurosurgeons to analyze their preferences, b) Develop heuristics for gestural interfaces, and c) Produce a lexicon that maximizes surgeons’ preferences. Materials and methods: A gesture elicitation study was conducted with nine neurosurgeons. Initially, subjects were asked to outline the gestures on a drawing board for each of the PACS commands. Next, the subjects performed one of three imaging tasks using gestures instead of the keyboard and mouse. Each gesture was annotated with respect to the presence/absence of gesture descriptors. Next, K-nearest neighbor approach was used to obtain the final lexicon that complies with the preferred/popular descriptors. Results: The elicitation study resulted in nine gesture lexicons, each comprised of 28 gestures. A paired t-test between the popularity of the overall gesture and the top three descriptors showed that the latter is significantly higher than the former (89.5%-59.7% vs 19.4%, p < 0.001), meaning more than half of the subjects agreed on these descriptors. Next, the gesture heuristics were generated for each command using the popular descriptors. Lastly, we developed a lexicon that complies with surgeons’ preferences. Conclusions: Neurosurgeons do agree on fundamental characteristics of gestures to perform image manipulation tasks. The proposed heuristics could potentially guide the development of future gesture-based interaction of PACS for the OR.\n
\n\n\n\n \n\n \n \n \n \n \n Surgical Telementoring Without Encumbrance.\n \n \n \n\n\n \n Rojas-Muñoz, E.; Cabrera, M., E.; Andersen, D.; Popescu, V.; Marley, S.; Mullis, B.; Zarzaur, B.; and Wachs, J.\n\n\n \n\n\n\n Annals of Surgery, 270(2): 384-389. 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Surgical Telementoring Without Encumbrance},\n type = {article},\n year = {2019},\n pages = {384-389},\n volume = {270},\n id = {2f44430d-b202-362e-b0a4-3f7df415c632},\n created = {2021-06-04T19:36:50.824Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.972Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Rojas-Munoz2019},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {OBJECTIVE This study investigates the benefits of a surgical telementoring system based on an augmented reality head-mounted display (ARHMD) that overlays surgical instructions directly onto the surgeon's view of the operating field, without workspace obstruction. SUMMARY BACKGROUND DATA In conventional telestrator-based telementoring, the surgeon views annotations of the surgical field by shifting focus to a nearby monitor, which substantially increases cognitive load. As an alternative, tablets have been used between the surgeon and the patient to display instructions; however, tablets impose additional obstructions of surgeon's motions. METHODS Twenty medical students performed anatomical marking (Task1) and abdominal incision (Task2) on a patient simulator, in 1 of 2 telementoring conditions: ARHMD and telestrator. The dependent variables were placement error, number of focus shifts, and completion time. Furthermore, workspace efficiency was quantified as the number and duration of potential surgeon-tablet collisions avoided by the ARHMD. RESULTS The ARHMD condition yielded smaller placement errors (Task1: 45%, P < 0.001; Task2: 14%, P = 0.01), fewer focus shifts (Task1: 93%, P < 0.001; Task2: 88%, P = 0.0039), and longer completion times (Task1: 31%, P < 0.001; Task2: 24%, P = 0.013). Furthermore, the ARHMD avoided potential tablet collisions (4.8 for 3.2 seconds in Task1; 3.8 for 1.3 seconds in Task2). CONCLUSION The ARHMD system promises to improve accuracy and to eliminate focus shifts in surgical telementoring. Because ARHMD participants were able to refine their execution of instructions, task completion time increased. Unlike a tablet system, the ARHMD does not require modifying natural motions to avoid collisions.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Cabrera, Maria Eugenia and Andersen, Daniel and Popescu, Voicu and Marley, Sherri and Mullis, Brian and Zarzaur, Ben and Wachs, Juan},\n doi = {10.1097/sla.0000000000002764},\n journal = {Annals of Surgery},\n number = {2}\n}\n\n OBJECTIVE This study investigates the benefits of a surgical telementoring system based on an augmented reality head-mounted display (ARHMD) that overlays surgical instructions directly onto the surgeon's view of the operating field, without workspace obstruction. SUMMARY BACKGROUND DATA In conventional telestrator-based telementoring, the surgeon views annotations of the surgical field by shifting focus to a nearby monitor, which substantially increases cognitive load. As an alternative, tablets have been used between the surgeon and the patient to display instructions; however, tablets impose additional obstructions of surgeon's motions. METHODS Twenty medical students performed anatomical marking (Task1) and abdominal incision (Task2) on a patient simulator, in 1 of 2 telementoring conditions: ARHMD and telestrator. The dependent variables were placement error, number of focus shifts, and completion time. Furthermore, workspace efficiency was quantified as the number and duration of potential surgeon-tablet collisions avoided by the ARHMD. RESULTS The ARHMD condition yielded smaller placement errors (Task1: 45%, P < 0.001; Task2: 14%, P = 0.01), fewer focus shifts (Task1: 93%, P < 0.001; Task2: 88%, P = 0.0039), and longer completion times (Task1: 31%, P < 0.001; Task2: 24%, P = 0.013). Furthermore, the ARHMD avoided potential tablet collisions (4.8 for 3.2 seconds in Task1; 3.8 for 1.3 seconds in Task2). CONCLUSION The ARHMD system promises to improve accuracy and to eliminate focus shifts in surgical telementoring. Because ARHMD participants were able to refine their execution of instructions, task completion time increased. Unlike a tablet system, the ARHMD does not require modifying natural motions to avoid collisions.\n
\n\n\n\n \n\n \n \n \n \n \n \n Surgical Telementoring Without Encumbrance: A Comparative Study of See-through Augmented Reality-based Approaches.\n \n \n \n \n\n\n \n Rojas-Muñoz, E.; Cabrera, M., E.; Andersen, D.; Popescu, V.; Marley, S.; Mullis, B.; Zarzaur, B.; and Wachs, J.\n\n\n \n\n\n\n Annals of Surgery, 270(2): 384-389. 4 2019.\n \n\n\n\n
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\n\n\n@article{\n title = {Surgical Telementoring Without Encumbrance: A Comparative Study of See-through Augmented Reality-based Approaches},\n type = {article},\n year = {2019},\n keywords = {augmented reality,surgical telementoring,telemedicine,teleproctoring},\n pages = {384-389},\n volume = {270},\n websites = {http://insights.ovid.com/crossref?an=00000658-900000000-95645},\n month = {4},\n id = {f3541794-d26a-313e-8cdf-e958649c4c3c},\n created = {2021-06-04T19:36:51.010Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.141Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Rojas-Munoz2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective:This study investigates the benefits of a surgical telementoring system based on an augmented reality head-mounted display (ARHMD) that overlays surgical instructions directly onto the surgeon's view of the operating field, without workspace obstruction.Summary Background Data:In conventional telestrator-based telementoring, the surgeon views annotations of the surgical field by shifting focus to a nearby monitor, which substantially increases cognitive load. As an alternative, tablets have been used between the surgeon and the patient to display instructions; however, tablets impose additional obstructions of surgeon's motions.Methods:Twenty medical students performed anatomical marking (Task1) and abdominal incision (Task2) on a patient simulator, in 1 of 2 telementoring conditions: ARHMD and telestrator. The dependent variables were placement error, number of focus shifts, and completion time. Furthermore, workspace efficiency was quantified as the number and duration of potential surgeon-tablet collisions avoided by the ARHMD.Results:The ARHMD condition yielded smaller placement errors (Task1: 45%, P < 0.001; Task2: 14%, P = 0.01), fewer focus shifts (Task1: 93%, P < 0.001; Task2: 88%, P = 0.0039), and longer completion times (Task1: 31%, P < 0.001; Task2: 24%, P = 0.013). Furthermore, the ARHMD avoided potential tablet collisions (4.8 for 3.2 seconds in Task1; 3.8 for 1.3 seconds in Task2).Conclusion:The ARHMD system promises to improve accuracy and to eliminate focus shifts in surgical telementoring. Because ARHMD participants were able to refine their execution of instructions, task completion time increased. Unlike a tablet system, the ARHMD does not require modifying natural motions to avoid collisions.},\n bibtype = {article},\n author = {Rojas-Muñoz, Edgar and Cabrera, Maria Eugenia and Andersen, Daniel and Popescu, Voicu and Marley, Sherri and Mullis, Brian and Zarzaur, Ben and Wachs, Juan},\n doi = {10.1097/SLA.0000000000002764},\n journal = {Annals of Surgery},\n number = {2}\n}\n\n Objective:This study investigates the benefits of a surgical telementoring system based on an augmented reality head-mounted display (ARHMD) that overlays surgical instructions directly onto the surgeon's view of the operating field, without workspace obstruction.Summary Background Data:In conventional telestrator-based telementoring, the surgeon views annotations of the surgical field by shifting focus to a nearby monitor, which substantially increases cognitive load. As an alternative, tablets have been used between the surgeon and the patient to display instructions; however, tablets impose additional obstructions of surgeon's motions.Methods:Twenty medical students performed anatomical marking (Task1) and abdominal incision (Task2) on a patient simulator, in 1 of 2 telementoring conditions: ARHMD and telestrator. The dependent variables were placement error, number of focus shifts, and completion time. Furthermore, workspace efficiency was quantified as the number and duration of potential surgeon-tablet collisions avoided by the ARHMD.Results:The ARHMD condition yielded smaller placement errors (Task1: 45%, P < 0.001; Task2: 14%, P = 0.01), fewer focus shifts (Task1: 93%, P < 0.001; Task2: 88%, P = 0.0039), and longer completion times (Task1: 31%, P < 0.001; Task2: 24%, P = 0.013). Furthermore, the ARHMD avoided potential tablet collisions (4.8 for 3.2 seconds in Task1; 3.8 for 1.3 seconds in Task2).Conclusion:The ARHMD system promises to improve accuracy and to eliminate focus shifts in surgical telementoring. Because ARHMD participants were able to refine their execution of instructions, task completion time increased. Unlike a tablet system, the ARHMD does not require modifying natural motions to avoid collisions.\n
\n\n\n\n \n\n \n \n \n \n \n Database of gesture attributes: Zero shot learning for gesture recognition.\n \n \n \n\n\n \n Madapana, N.; and Wachs, J.\n\n\n \n\n\n\n In Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019, 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Database of gesture attributes: Zero shot learning for gesture recognition},\n type = {inproceedings},\n year = {2019},\n id = {e258e92f-4e16-390e-bfa1-0c4a028f9e1a},\n created = {2021-06-04T19:36:51.159Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.364Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2019c},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Existing gesture classification techniques assign a categorical label to each gesture instance and learn to recognize only a predetermined set of gesture classes. These techniques lack adaptability to new or unseen gestures which is the premise of zero shot learning (ZSL). Hence we propose to identify the properties of gestures and thereby infer the categorical label instead of recognizing the class label directly. ZSL for gesture recognition has hardly been studied in the pattern recognition research. The reason is partly due to the lack of benchmarks and specialized datasets consisting of annotations for gesture attributes. In this regard, this paper presents the first annotated database of attributes for the gestures present in ChaLearn 2013 and MSRC - 12 datasets. This was achieved as follows; First, we identified a finite set of 64 discriminative and representative high level attributes of gestures from the literature. Further, we performed crowdsourced human studies using Amazon Mechanical Turk to obtain attribute annotations for 28 gesture classes. Next, we used our dataset to train existing ZSL classifiers to predict attribute labels. Finally, we provide benchmarks for unseen gesture class prediction on CGD2013 and MSRC-12. We have made this dataset publicly available to encourage researchers to further investigate this problem.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, Juan},\n doi = {10.1109/FG.2019.8756548},\n booktitle = {Proceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019}\n}\n\n Existing gesture classification techniques assign a categorical label to each gesture instance and learn to recognize only a predetermined set of gesture classes. These techniques lack adaptability to new or unseen gestures which is the premise of zero shot learning (ZSL). Hence we propose to identify the properties of gestures and thereby infer the categorical label instead of recognizing the class label directly. ZSL for gesture recognition has hardly been studied in the pattern recognition research. The reason is partly due to the lack of benchmarks and specialized datasets consisting of annotations for gesture attributes. In this regard, this paper presents the first annotated database of attributes for the gestures present in ChaLearn 2013 and MSRC - 12 datasets. This was achieved as follows; First, we identified a finite set of 64 discriminative and representative high level attributes of gestures from the literature. Further, we performed crowdsourced human studies using Amazon Mechanical Turk to obtain attribute annotations for 28 gesture classes. Next, we used our dataset to train existing ZSL classifiers to predict attribute labels. Finally, we provide benchmarks for unseen gesture class prediction on CGD2013 and MSRC-12. We have made this dataset publicly available to encourage researchers to further investigate this problem.\n
\n\n\n\n \n\n \n \n \n \n \n JISAP: Joint Inference for Surgeon Attributes Prediction during Robot-Assisted Surgery.\n \n \n \n\n\n \n Zhou, T.; Cha, J., S.; Gonzalez, G., T.; Sundaram, C., P.; Wachs, J., P.; and Yu, D.\n\n\n \n\n\n\n In IEEE International Conference on Intelligent Robots and Systems, pages 2246-2251, 2019. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {JISAP: Joint Inference for Surgeon Attributes Prediction during Robot-Assisted Surgery},\n type = {inproceedings},\n year = {2019},\n pages = {2246-2251},\n id = {4438c0e2-f1af-3d63-a143-fcf997e349ef},\n created = {2021-06-04T19:36:51.489Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.586Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In Robot-Assisted Surgery, predicting surgeon attributes such as task workload, operation performance, and expertise levels is important in providing tailored assistance. This paper proposes Joint Inference for Surgeon Attributes Prediction (JISAP), a computational framework to jointly infer surgeon attributes (i.e., task workload, operation performance, and expertise level) from multimodal physiological signals (heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity). JISAP was evaluated with a dataset of twelve surgeons operating on the da Vinci Skills Simulator. It was found that JISAP can simultaneously predict surgeon attributes with a percentage error of 11.05%. Additionally, joint inference was found to outperform isolated inference with a boost of 10%.},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Cha, Jackie S. and Gonzalez, Glebys T. and Sundaram, Chandru P. and Wachs, Juan P. and Yu, Denny},\n doi = {10.1109/IROS40897.2019.8968097},\n booktitle = {IEEE International Conference on Intelligent Robots and Systems}\n}\n\n In Robot-Assisted Surgery, predicting surgeon attributes such as task workload, operation performance, and expertise levels is important in providing tailored assistance. This paper proposes Joint Inference for Surgeon Attributes Prediction (JISAP), a computational framework to jointly infer surgeon attributes (i.e., task workload, operation performance, and expertise level) from multimodal physiological signals (heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity). JISAP was evaluated with a dataset of twelve surgeons operating on the da Vinci Skills Simulator. It was found that JISAP can simultaneously predict surgeon attributes with a percentage error of 11.05%. Additionally, joint inference was found to outperform isolated inference with a boost of 10%.\n
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\n \n \n\n \n \n \n \n \n Glovebox Handling of High-Consequence Materials with Super Baxter and Gesture-Based Programming.\n \n \n \n\n\n \n Soratana, T.; Balakuntala, M.; Abbaraju, P.; Voyles, R.; Wachs, J.; and Mahoor, M.\n\n\n \n\n\n\n In Proc. of Waste Management Symposium, 2018. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Glovebox Handling of High-Consequence Materials with Super Baxter and Gesture-Based Programming},\n type = {inproceedings},\n year = {2018},\n id = {0f215d4b-2979-3392-84d5-67416209c79e},\n created = {2018-02-19T03:12:37.954Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:13:15.201Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Soratana2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Soratana, T. and Balakuntala, M.V.S.M. and Abbaraju, P. and Voyles, R.M. and Wachs, J. and Mahoor, M},\n booktitle = {Proc. of Waste Management Symposium}\n}\n\n \n\n \n \n \n \n \n Biomechanical-based Approach to Data Augmentation for One-Shot Gesture Recognition.\n \n \n \n\n\n \n Cabrera, M.; and Wachs, J.\n\n\n \n\n\n\n In 13th IEEE International Conference on Automatic Face and Gesture Recognition, May 2018., 2018. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Biomechanical-based Approach to Data Augmentation for One-Shot Gesture Recognition},\n type = {inproceedings},\n year = {2018},\n id = {c9ad99a5-a075-3d3a-a8f5-beb2ae80867b},\n created = {2018-05-31T03:26:20.534Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.254Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2018b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Cabrera, M and Wachs, JP},\n booktitle = {13th IEEE International Conference on Automatic Face and Gesture Recognition, May 2018.}\n}\n\n \n\n \n \n \n \n \n Variability analysis on gestures for people with quadriplegia.\n \n \n \n\n\n \n Jiang, H.; Duerstock, B.; and Wachs, J.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics, 48(1). 2018.\n \n\n\n\n
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\n\n\n@article{\n title = {Variability analysis on gestures for people with quadriplegia},\n type = {article},\n year = {2018},\n keywords = {Assistive technologies,Hand gesture-based interfaces,Laban space,Spinal cord injury (SCI)},\n volume = {48},\n id = {280fafb2-1145-3f54-9ce8-50a3f1a33600},\n created = {2018-07-12T11:33:32.925Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.258Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Jiang2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2016 IEEE. Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.},\n bibtype = {article},\n author = {Jiang, H. and Duerstock, B.S. and Wachs, J.P.},\n doi = {10.1109/TCYB.2016.2635481},\n journal = {IEEE Transactions on Cybernetics},\n number = {1}\n}\n\n © 2016 IEEE. Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.\n
\n\n\n\n \n\n \n \n \n \n \n Collaborative Robots in Surgical Research.\n \n \n \n\n\n \n Sanchez-Tamayo, N.; and Wachs, J., P.\n\n\n \n\n\n\n In pages 231-232, 2018. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Collaborative Robots in Surgical Research},\n type = {inproceedings},\n year = {2018},\n pages = {231-232},\n id = {b1d1ce83-4390-355e-93cb-26b76833ee14},\n created = {2021-06-04T19:22:34.607Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.963Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Sanchez-Tamayo, Natalia and Wachs, Juan P.},\n doi = {10.1145/3173386.3176978}\n}\n\n \n\n \n \n \n \n \n Taxonomy of communications in the operating room.\n \n \n \n\n\n \n Velasquez, C., A., C.; Tian, Z.; Rashid, M.; Chaikhouni, A.; Wachs, J., J., P.; Mazhar, R.; Chaikhouni, A.; Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Advances in Intelligent Systems and Computing, volume 590, pages 251-262, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Taxonomy of communications in the operating room},\n type = {inproceedings},\n year = {2018},\n keywords = {Human factors,Human-Systems integration,Medical robotics systems engineering},\n pages = {251-262},\n volume = {590},\n id = {5d6ff29a-d909-356c-a039-066ab4f0eda2},\n created = {2021-06-04T19:36:47.491Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.412Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Velasquez2017},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,128681a6-ba46-469d-8c4e-cb337bbf0f22},\n private_publication = {false},\n abstract = {US hospitals are facing critical problems of nurse shortage. One report predicts that there will be a shortage of 260,000 registered nurses by 2025 in the USA, and it was found out that patient mortality risk is 6% higher in hospitals understaffed with nurses compared to units fully staffed. One possible solution to cope with such nurse shortage problem is to develop robotic scrub nurses that can collaborate with surgeons. Some robotic systems have been specifically developed to handle instruments to the surgeon in the operating room. These robotic systems work under the assumption that verbal and gesture communication are the most common modalities. This study shows that expert surgical staff do not use specific gestures to communicate and rely only in two modalities: predictions performed by the assistant and verbal commands. From a group of 68 instruments delivered, during three cardiothoracic surgeries, 47 corresponded to successful predictions and 23 to verbal commands. Only one wrong prediction was observed but no specific gesture.},\n bibtype = {inproceedings},\n author = {Velasquez, Carlos A. C.A. and Tian, Zhou and Rashid, Mazhar and Chaikhouni, Amer and Wachs, J.P. Juan P. and Mazhar, Rashid and Chaikhouni, Amer and Zhou, Tian and Wachs, J.P. Juan P.},\n doi = {10.1007/978-3-319-60483-1_25},\n booktitle = {Advances in Intelligent Systems and Computing}\n}\n\n US hospitals are facing critical problems of nurse shortage. One report predicts that there will be a shortage of 260,000 registered nurses by 2025 in the USA, and it was found out that patient mortality risk is 6% higher in hospitals understaffed with nurses compared to units fully staffed. One possible solution to cope with such nurse shortage problem is to develop robotic scrub nurses that can collaborate with surgeons. Some robotic systems have been specifically developed to handle instruments to the surgeon in the operating room. These robotic systems work under the assumption that verbal and gesture communication are the most common modalities. This study shows that expert surgical staff do not use specific gestures to communicate and rely only in two modalities: predictions performed by the assistant and verbal commands. From a group of 68 instruments delivered, during three cardiothoracic surgeries, 47 corresponded to successful predictions and 23 to verbal commands. Only one wrong prediction was observed but no specific gesture.\n
\n\n\n\n \n\n \n \n \n \n \n Early prediction for physical human robot collaboration in the operating room.\n \n \n \n\n\n \n Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n Autonomous Robots, 42(5): 977-995. 2018.\n \n\n\n\n
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\n\n\n@article{\n title = {Early prediction for physical human robot collaboration in the operating room},\n type = {article},\n year = {2018},\n keywords = {Dempster–Shafer theory,Human–robot interaction,Long short-term memory,Multimodal,Operating room,Recurrent neural network,Robot nurse,Sensor fusion,Turn-taking prediction},\n pages = {977-995},\n volume = {42},\n id = {b4f4b70c-a5c3-3d1d-86d8-6855082bb2a4},\n created = {2021-06-04T19:36:47.632Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.604Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {To enable a natural and fluent human robot collaboration flow, it is critical for a robot to comprehend their human peers’ on-going actions, predict their behaviors in the near future, and plan its actions correspondingly. Specifically, the capability of making early predictions is important, so that the robot can foresee the precise timing of a turn-taking event and start motion planning and execution early enough to smooth the turn-taking transition. Such proactive behavior would reduce human’s waiting time, increase efficiency and enhance naturalness in collaborative task. To that end, this paper presents the design and implementation of an early turn-taking prediction algorithm, catered for physical human robot collaboration scenarios. Specifically, a robotic scrub nurse system which can comprehend surgeon’s multimodal communication cues and perform turn-taking prediction is presented. The developed algorithm was tested on a collected data set of simulated surgical procedures in a surgeon–nurse tandem. The proposed turn-taking prediction algorithm is found to be significantly superior to its algorithmic counterparts, and is more accurate than human baseline when little partial input is given (less than 30% of full action). After observing more information, the algorithm can achieve comparable performances as humans with a F1 score of 0.90.},\n bibtype = {article},\n author = {Zhou, Tian and Wachs, J.P. Juan Pablo},\n doi = {10.1007/s10514-017-9670-9},\n journal = {Autonomous Robots},\n number = {5}\n}\n\n To enable a natural and fluent human robot collaboration flow, it is critical for a robot to comprehend their human peers’ on-going actions, predict their behaviors in the near future, and plan its actions correspondingly. Specifically, the capability of making early predictions is important, so that the robot can foresee the precise timing of a turn-taking event and start motion planning and execution early enough to smooth the turn-taking transition. Such proactive behavior would reduce human’s waiting time, increase efficiency and enhance naturalness in collaborative task. To that end, this paper presents the design and implementation of an early turn-taking prediction algorithm, catered for physical human robot collaboration scenarios. Specifically, a robotic scrub nurse system which can comprehend surgeon’s multimodal communication cues and perform turn-taking prediction is presented. The developed algorithm was tested on a collected data set of simulated surgical procedures in a surgeon–nurse tandem. The proposed turn-taking prediction algorithm is found to be significantly superior to its algorithmic counterparts, and is more accurate than human baseline when little partial input is given (less than 30% of full action). After observing more information, the algorithm can achieve comparable performances as humans with a F1 score of 0.90.\n
\n\n\n\n \n\n \n \n \n \n \n \n Joint Surgeon Attributes Estimation in Robot-Assisted Surgery.\n \n \n \n \n\n\n \n Zhou, T.; Cha, J., J., S.; Gonzalez, G., G., T.; Wachs, J., J., P.; Sundaram, C.; and Yu, D.\n\n\n \n\n\n\n In ACM/IEEE International Conference on Human-Robot Interaction, pages 285-286, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Joint Surgeon Attributes Estimation in Robot-Assisted Surgery},\n type = {inproceedings},\n year = {2018},\n keywords = {da vinci,machine learning,multimodality,robot-assisted surgery,surgeon assessment,teleoperation,workload},\n pages = {285-286},\n websites = {https://dl.acm.org/citation.cfm?id=3176981},\n id = {12ab69ec-cffd-3a15-a661-6b97da5d84b2},\n created = {2021-06-04T19:36:48.065Z},\n accessed = {2018-05-01},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.083Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper proposes a computational framework to estimate surgeon attributes during Robot-Assisted Surgery (RAS). The three investigated attributes are workload, performance, and expertise levels. The framework leverages multimodal sensing and joint estimation and was evaluated with twelve surgeons operating on the da Vinci Skills Simulator. The multimodal signals include heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity. The proposed framework reached an average estimation error of 11.05%, and jointly inferring surgeon attributes reduced estimation errors by 10.02%.},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Cha, JS Jackie S. and Gonzalez, GT Glebys T. and Wachs, JP Juan P. and Sundaram, Chandru and Yu, Denny},\n doi = {10.1145/3173386.3176981},\n booktitle = {ACM/IEEE International Conference on Human-Robot Interaction}\n}\n\n This paper proposes a computational framework to estimate surgeon attributes during Robot-Assisted Surgery (RAS). The three investigated attributes are workload, performance, and expertise levels. The framework leverages multimodal sensing and joint estimation and was evaluated with twelve surgeons operating on the da Vinci Skills Simulator. The multimodal signals include heart rate variability, wrist motion, electrodermal, electromyography, and electroencephalogram activity. The proposed framework reached an average estimation error of 11.05%, and jointly inferring surgeon attributes reduced estimation errors by 10.02%.\n
\n\n\n\n \n\n \n \n \n \n \n \n Early turn-taking prediction with spiking neural networks for human robot collaboration.\n \n \n \n \n\n\n \n Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings - IEEE International Conference on Robotics and Automation, pages 3250-3256, 5 2018. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Early turn-taking prediction with spiking neural networks for human robot collaboration},\n type = {inproceedings},\n year = {2018},\n pages = {3250-3256},\n websites = {https://ieeexplore.ieee.org/document/8461208/},\n month = {5},\n publisher = {IEEE},\n id = {5b81c2bd-ef07-3c8d-bdc9-107028e445c2},\n created = {2021-06-04T19:36:48.142Z},\n accessed = {2019-02-05},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.072Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 % of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Wachs, J.P. Juan P.},\n doi = {10.1109/ICRA.2018.8461208},\n booktitle = {Proceedings - IEEE International Conference on Robotics and Automation}\n}\n\n Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 % of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.\n
\n\n\n\n \n\n \n \n \n \n \n \n Biomechanical-based approach to data augmentation for one-shot gesture recognition.\n \n \n \n \n\n\n \n Cabrera, M., M., E.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n In Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, pages 38-44, 5 2018. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Biomechanical-based approach to data augmentation for one-shot gesture recognition},\n type = {inproceedings},\n year = {2018},\n keywords = {Biomechanics,Data Augmentation,Gesture Recognition,One Shot Learning},\n pages = {38-44},\n websites = {https://ieeexplore.ieee.org/document/8373809/},\n month = {5},\n publisher = {IEEE},\n id = {918b6d5f-0ad5-3fa8-a859-d33ae13c47a8},\n created = {2021-06-04T19:36:48.413Z},\n accessed = {2018-07-18},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.326Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2018b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Most common approaches to one-shot gesture recognition have leveraged mainly conventional machine learning solutions and image based data augmentation techniques, ignoring the mechanisms that are used by humans to perceive and execute gestures, a key contextual component in this process. The novelty of this work consists on modeling the process that leads to the creation of gestures, rather than observing the gesture alone. In this approach, the context considered involves the way in which humans produce the gestures - the kinematic and biomechanical characteristics associated with gesture production and execution. By understanding the main 'modes' of variation we can replicate the single observation many times. Consequently, the main strategy proposed in this paper includes generating a data set of human-like examples based on 'naturalistic' features extracted from a single gesture sample while preserving fundamentally human characteristics like visual saliency, smooth transitions and economy of motion. The availability of a large data set of realistic samples allows the use state-of-the-art classifiers for further recognition. Several classifiers were trained and their recognition accuracies were assessed and compared to previous one-shot learning approaches. An average recognition accuracy of 95% among all classifiers highlights the relevance of keeping the human 'in the loop' to effectively achieve one-shot gesture recognition.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. Maria Eugenia and Wachs, J.P. JP Juan Pablo},\n doi = {10.1109/FG.2018.00016},\n booktitle = {Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018}\n}\n\n Most common approaches to one-shot gesture recognition have leveraged mainly conventional machine learning solutions and image based data augmentation techniques, ignoring the mechanisms that are used by humans to perceive and execute gestures, a key contextual component in this process. The novelty of this work consists on modeling the process that leads to the creation of gestures, rather than observing the gesture alone. In this approach, the context considered involves the way in which humans produce the gestures - the kinematic and biomechanical characteristics associated with gesture production and execution. By understanding the main 'modes' of variation we can replicate the single observation many times. Consequently, the main strategy proposed in this paper includes generating a data set of human-like examples based on 'naturalistic' features extracted from a single gesture sample while preserving fundamentally human characteristics like visual saliency, smooth transitions and economy of motion. The availability of a large data set of realistic samples allows the use state-of-the-art classifiers for further recognition. Several classifiers were trained and their recognition accuracies were assessed and compared to previous one-shot learning approaches. An average recognition accuracy of 95% among all classifiers highlights the relevance of keeping the human 'in the loop' to effectively achieve one-shot gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n Collaborative Robots in Surgical Research: A Low-Cost Adaptation.\n \n \n \n\n\n \n Sanchez-Tamayo, N.; and Wachs, J., J., P.\n\n\n \n\n\n\n In ACM/IEEE International Conference on Human-Robot Interaction, volume Part F1351, pages 231-232, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Collaborative Robots in Surgical Research: A Low-Cost Adaptation},\n type = {inproceedings},\n year = {2018},\n keywords = {3d printing,hardware design,robotic-assisted surgery,teleoperation},\n pages = {231-232},\n volume = {Part F1351},\n id = {6325c0ca-7bc9-36ce-8715-b8c10c7933cf},\n created = {2021-06-04T19:36:48.587Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.652Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Sanchez-Tamayo2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This work demonstrates the adaptation of an industrial robotic system to an affordable and accessible open platform for education and research through rapid prototyping techniques. The ABB YuMi collaborative robot is controlled using a virtual reality teleoperation system and adapted using a low-cost gripper extension for surgical tools. The design and assessment of three surgical tools used in two mock surgical procedures are showcased in this paper. It was found that the perpendicular scalpel tool surpassed the others for performance time. Scissors were found more effective to cut the affected tissue in the melanoma extraction task than the parallel scalpel configuration (15% of healthy tissue removed versus 42%).},\n bibtype = {inproceedings},\n author = {Sanchez-Tamayo, Natalia and Wachs, J.P. Juan P.},\n doi = {10.1145/3173386.3176978},\n booktitle = {ACM/IEEE International Conference on Human-Robot Interaction}\n}\n\n This work demonstrates the adaptation of an industrial robotic system to an affordable and accessible open platform for education and research through rapid prototyping techniques. The ABB YuMi collaborative robot is controlled using a virtual reality teleoperation system and adapted using a low-cost gripper extension for surgical tools. The design and assessment of three surgical tools used in two mock surgical procedures are showcased in this paper. It was found that the perpendicular scalpel tool surpassed the others for performance time. Scissors were found more effective to cut the affected tissue in the melanoma extraction task than the parallel scalpel configuration (15% of healthy tissue removed versus 42%).\n
\n\n\n\n \n\n \n \n \n \n \n Coherence in One-Shot Gesture Recognition for Human-Robot Interaction.\n \n \n \n\n\n \n Cabrera, M., M., E.; Voyles, R., M., R.; and Wachs, J., J., P.\n\n\n \n\n\n\n In ACM/IEEE International Conference on Human-Robot Interaction, volume Part F1351, pages 75-76, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Coherence in One-Shot Gesture Recognition for Human-Robot Interaction},\n type = {inproceedings},\n year = {2018},\n keywords = {gesture recognition,one-shot learning,robotics},\n pages = {75-76},\n volume = {Part F1351},\n id = {39291ba0-c081-3c15-90f4-35b698d61c66},\n created = {2021-06-04T19:36:49.433Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.462Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2018c},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {An experiment was conducted where a robotic platform performs artificially generated gestures and both trained classifiers and human participants recognize. Classification accuracy is evaluated through a new metric of coherence in gesture recognition between humans and robots. Experimental results showed an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherence in one-shot gesture recognition was determined to be gamma = 93.8%. This new metric provides a quantifier for validating how realistic the robotic generated gestures are.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. Maria E. and Voyles, Richard M. R.M. and Wachs, J.P. Juan P.},\n doi = {10.1145/3173386.3176977},\n booktitle = {ACM/IEEE International Conference on Human-Robot Interaction}\n}\n\n An experiment was conducted where a robotic platform performs artificially generated gestures and both trained classifiers and human participants recognize. Classification accuracy is evaluated through a new metric of coherence in gesture recognition between humans and robots. Experimental results showed an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherence in one-shot gesture recognition was determined to be gamma = 93.8%. This new metric provides a quantifier for validating how realistic the robotic generated gestures are.\n
\n\n\n\n \n\n \n \n \n \n \n Variability analysis on gestures for people with quadriplegia.\n \n \n \n\n\n \n Jiang, H.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics, 48(1): 346-356. 2018.\n \n\n\n\n
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\n\n\n@article{\n title = {Variability analysis on gestures for people with quadriplegia},\n type = {article},\n year = {2018},\n keywords = {Assistive technologies,Hand gesture-based interfaces,Laban space,Spinal cord injury (SCI)},\n pages = {346-356},\n volume = {48},\n id = {39ec349b-f85b-3323-a24e-796127bbf921},\n created = {2021-06-04T19:36:49.946Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.093Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.},\n bibtype = {article},\n author = {Jiang, Hairong and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1109/TCYB.2016.2635481},\n journal = {IEEE Transactions on Cybernetics},\n number = {1}\n}\n\n Gesture-based interfaces have become an effective control modality within the human computer interaction realm to assist individuals with mobility impairments in accessing technologies for daily living to entertainment. Recent studies have shown that gesture-based interfaces in tandem with gaming consoles are being used to complement physical therapies at rehabilitation hospitals and in their homes. Because the motor movements of individuals with physical impairments are different from persons without disabilities, the gesture sets required to operate those interfaces must be customized. This limits significantly the number and quality of available software environments for users with motor impairments. Previous work presented an analytic approach to convert an existing gesture-based interface designed for individuals without disabilities to be usable by people with motor disabilities. The objective of this paper is to include gesture variability analysis into the existing framework using robotics as an additional validation framework. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.\n
\n\n\n\n \n\n \n \n \n \n \n \n Gestures for Picture Archiving and Communication Systems (PACS) operation in the operating room: Is there any standard?.\n \n \n \n \n\n\n \n Madapana, N.; Gonzalez, G.; Rodgers, R.; Zhang, L.; and Wachs, J., P.\n\n\n \n\n\n\n PLoS ONE, 13(6): e0198092. 6 2018.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Gestures for Picture Archiving and Communication Systems (PACS) operation in the operating room: Is there any standard?},\n type = {article},\n year = {2018},\n pages = {e0198092},\n volume = {13},\n websites = {https://dx.plos.org/10.1371/journal.pone.0198092,http://dx.plos.org/10.1371/journal.pone.0198092},\n month = {6},\n publisher = {Public Library of Science},\n day = {12},\n id = {573a6726-92f8-3c21-9d59-892c2ad62e4a},\n created = {2021-06-04T19:36:50.439Z},\n accessed = {2018-07-10},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.740Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective: Gestural interfaces allow accessing and manipulating Electronic Medical Records (EMR) in hospitals while keeping a complete sterile environment. Particularly, in the Operating Room (OR), these interfaces enable surgeons to browse Picture Archiving and Communication System (PACS) without the need of delegating functions to the surgical staff. Existing gesture based medical interfaces rely on a suboptimal and an arbitrary small set of gestures that are mapped to a few commands available in PACS software. The objective of this work is to discuss a method to determine the most suitable set of gestures based on surgeon's acceptability. To achieve this goal, the paper introduces two key innovations: (a) a novel methodology to incorporate gestures' semantic properties into the agreement analysis, and (b) a new agreement metric to determine the most suitable gesture set for a PACS. Materials and methods: Three neurosurgical diagnostic tasks were conducted by nine neurosurgeons. The set of commands and gesture lexicons were determined using a Wizard of Oz paradigm. The gestures were decomposed into a set of 55 semantic properties based on the motion trajectory, orientation and pose of the surgeons' hands and their ground truth values were manually annotated. Finally, a new agreement metric was developed, using the known Jaccard similarity to measure consensus between users over a gesture set. Results: A set of 34 PACS commands were found to be a sufficient number of actions for PACS manipulation. In addition, it was found that there is a level of agreement of 0.29 among the surgeons over the gestures found. Two statistical tests including paired t-test and Mann Whitney Wilcoxon test were conducted between the proposed metric and the traditional agreement metric. It was found that the agreement values computed using the former metric are significantly higher (p > 0.001) for both tests. Conclusions: This study reveals that the level of agreement among surgeons over the best gestures for PACS operation is higher than the previously reported metric (0.29 vs 0.13). This observation is based on the fact that the agreement focuses on main features of the gestures rather than the gestures themselves. The level of agreement is not very high, yet indicates a majority preference, and is better than using gestures based on authoritarian or arbitrary approaches. The methods described in this paper provide a guiding framework for the design of future gesture based PACS systems for the OR.},\n bibtype = {article},\n author = {Madapana, Naveen and Gonzalez, Glebys and Rodgers, Richard and Zhang, Lingsong and Wachs, Juan P.},\n editor = {van Ooijen, Peter M.A.},\n doi = {10.1371/journal.pone.0198092},\n journal = {PLoS ONE},\n number = {6}\n}\n\n Objective: Gestural interfaces allow accessing and manipulating Electronic Medical Records (EMR) in hospitals while keeping a complete sterile environment. Particularly, in the Operating Room (OR), these interfaces enable surgeons to browse Picture Archiving and Communication System (PACS) without the need of delegating functions to the surgical staff. Existing gesture based medical interfaces rely on a suboptimal and an arbitrary small set of gestures that are mapped to a few commands available in PACS software. The objective of this work is to discuss a method to determine the most suitable set of gestures based on surgeon's acceptability. To achieve this goal, the paper introduces two key innovations: (a) a novel methodology to incorporate gestures' semantic properties into the agreement analysis, and (b) a new agreement metric to determine the most suitable gesture set for a PACS. Materials and methods: Three neurosurgical diagnostic tasks were conducted by nine neurosurgeons. The set of commands and gesture lexicons were determined using a Wizard of Oz paradigm. The gestures were decomposed into a set of 55 semantic properties based on the motion trajectory, orientation and pose of the surgeons' hands and their ground truth values were manually annotated. Finally, a new agreement metric was developed, using the known Jaccard similarity to measure consensus between users over a gesture set. Results: A set of 34 PACS commands were found to be a sufficient number of actions for PACS manipulation. In addition, it was found that there is a level of agreement of 0.29 among the surgeons over the gestures found. Two statistical tests including paired t-test and Mann Whitney Wilcoxon test were conducted between the proposed metric and the traditional agreement metric. It was found that the agreement values computed using the former metric are significantly higher (p > 0.001) for both tests. Conclusions: This study reveals that the level of agreement among surgeons over the best gestures for PACS operation is higher than the previously reported metric (0.29 vs 0.13). This observation is based on the fact that the agreement focuses on main features of the gestures rather than the gestures themselves. The level of agreement is not very high, yet indicates a majority preference, and is better than using gestures based on authoritarian or arbitrary approaches. The methods described in this paper provide a guiding framework for the design of future gesture based PACS systems for the OR.\n
\n\n\n\n \n\n \n \n \n \n \n \n Image Exploration Procedure Classification with Spike-timing Neural Network for the Blind.\n \n \n \n \n\n\n \n Zhang, T.; Zhou, T.; Duerstock, B., S.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings - International Conference on Pattern Recognition, volume 2018-Augus, pages 3256-3261, 8 2018. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Image Exploration Procedure Classification with Spike-timing Neural Network for the Blind},\n type = {inproceedings},\n year = {2018},\n keywords = {Blind Community,Demspter-Shafer Theory,Exploration Procedures,Haptic-based Interface,Spatio-temporal Pattern,Spike-timing Neural Network},\n pages = {3256-3261},\n volume = {2018-Augus},\n websites = {https://ieeexplore.ieee.org/document/8545312/},\n month = {8},\n publisher = {IEEE},\n id = {5b42dd88-7153-3cd4-8d56-a9378edc90aa},\n created = {2021-06-04T19:36:50.475Z},\n accessed = {2019-02-03},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.754Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhang2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Individuals who are blind use exploration procedures (EPs) to navigate and understand digital images. The ability to model and detect these EPs can help the assistive technologies' community build efficient and accessible interfaces for the blind and overall enhance human-machine interaction. In this paper, we propose a framework to classify various EPs using spike-timing neural networks (SNNs). While users interact with a digital image using a haptic device, rotation and translation-invariant features are computed directly from exploration trajectories acquired from the haptic control. These features are further encoded as model strings through trained SNNs. A classification scheme is then proposed to distinguish these model strings to identify the EPs. The framework adapted a modified Dynamic Time Wrapping (DTW) for spatial-temporal matching with Dempster-Shafer Theory (DST) for multimodal fusion. Experimental results (87.05% as EPs' detection accuracy) indicate the effectiveness of the proposed framework and its potential application in human-machine interfaces.},\n bibtype = {inproceedings},\n author = {Zhang, Ting and Zhou, Tian and Duerstock, Bradley S. and Wachs, Juan P.},\n doi = {10.1109/ICPR.2018.8545312},\n booktitle = {Proceedings - International Conference on Pattern Recognition}\n}\n\n Individuals who are blind use exploration procedures (EPs) to navigate and understand digital images. The ability to model and detect these EPs can help the assistive technologies' community build efficient and accessible interfaces for the blind and overall enhance human-machine interaction. In this paper, we propose a framework to classify various EPs using spike-timing neural networks (SNNs). While users interact with a digital image using a haptic device, rotation and translation-invariant features are computed directly from exploration trajectories acquired from the haptic control. These features are further encoded as model strings through trained SNNs. A classification scheme is then proposed to distinguish these model strings to identify the EPs. The framework adapted a modified Dynamic Time Wrapping (DTW) for spatial-temporal matching with Dempster-Shafer Theory (DST) for multimodal fusion. Experimental results (87.05% as EPs' detection accuracy) indicate the effectiveness of the proposed framework and its potential application in human-machine interfaces.\n
\n\n\n\n \n\n \n \n \n \n \n Purpose-Built UAVs for Physical Sampling of Trace Contamination at the Portsmouth Gaseous Diffusion Plant.\n \n \n \n\n\n \n Soratana, T.; Balakuntala, M.; Abbaraju, P.; Voyles, R.; Wachs, J.; and Mahoor, M.\n\n\n \n\n\n\n In Waste Management (WM 2018), 44th International Symposium, 2018. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Purpose-Built UAVs for Physical Sampling of Trace Contamination at the Portsmouth Gaseous Diffusion Plant},\n type = {inproceedings},\n year = {2018},\n city = {Phoenix, AZ.},\n id = {ae8812fb-898c-3a24-b08c-b65b3b57154c},\n created = {2021-06-04T19:36:50.665Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T19:37:21.991Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Soratana2018b},\n country = {AZ.},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Soratana, T and Balakuntala, M.V.S.M. and Abbaraju, P and Voyles, R.M and Wachs, J. and Mahoor, M},\n booktitle = {Waste Management (WM 2018), 44th International Symposium}\n}\n\n \n\n \n \n \n \n \n \n Hard Zero Shot Learning for Gesture Recognition.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J., P.\n\n\n \n\n\n\n In 2018 24th International Conference on Pattern Recognition (ICPR), volume 2018-Augus, pages 3574-3579, 8 2018. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Hard Zero Shot Learning for Gesture Recognition},\n type = {inproceedings},\n year = {2018},\n pages = {3574-3579},\n volume = {2018-Augus},\n websites = {https://ieeexplore.ieee.org/document/8545869/},\n month = {8},\n publisher = {IEEE},\n id = {1ad6f778-415f-3a2d-bd07-a8c39a4bab19},\n created = {2021-06-04T19:36:50.991Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.138Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2018},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Gesture based systems allow humans to interact with devices and robots in a natural way. Yet, current gesture recognition systems can not recognize the gestures outside a limited lexicon. This opposes the idea of lifelong learning which require systems to adapt to unseen object classes. These issues can be best addressed using Zero Shot Learning (ZSL), a paradigm in machine learning that leverages the semantic information to recognize new classes. ZSL systems developed in the past used hundreds of training examples to detect new classes and assumed that test examples come from unseen classes. This work introduces two complex and more realistic learning problems referred as Hard Zero Shot Learning (HZSL) and Generalized HZSL (G-HZSL) necessary to achieve Life Long Learning. The main objective of these problems is to recognize unseen classes with limited training information and relax the assumption that test instances come from unseen classes. We propose to leverage one shot learning (OSL) techniques coupled with ZSL approaches to address and solve the problem of HZSL for gesture recognition. Further, supervised clustering techniques are used to discriminate seen classes from unseen classes. We assessed and compared the performance of various existing algorithms on HZSL for gestures using two standard datasets: MSRC-12 and CGD2011. For four unseen classes, results show that the marginal accuracy of HZSL-15.2% and G-HZSL-14.39% are comparable to the performance of conventional ZSL. Given that we used only one instance and do not assume that test classes are unseen, the performance of HZSL and G-HZSL models were remarkable.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, Juan P.},\n doi = {10.1109/ICPR.2018.8545869},\n booktitle = {2018 24th International Conference on Pattern Recognition (ICPR)}\n}\n\n Gesture based systems allow humans to interact with devices and robots in a natural way. Yet, current gesture recognition systems can not recognize the gestures outside a limited lexicon. This opposes the idea of lifelong learning which require systems to adapt to unseen object classes. These issues can be best addressed using Zero Shot Learning (ZSL), a paradigm in machine learning that leverages the semantic information to recognize new classes. ZSL systems developed in the past used hundreds of training examples to detect new classes and assumed that test examples come from unseen classes. This work introduces two complex and more realistic learning problems referred as Hard Zero Shot Learning (HZSL) and Generalized HZSL (G-HZSL) necessary to achieve Life Long Learning. The main objective of these problems is to recognize unseen classes with limited training information and relax the assumption that test instances come from unseen classes. We propose to leverage one shot learning (OSL) techniques coupled with ZSL approaches to address and solve the problem of HZSL for gesture recognition. Further, supervised clustering techniques are used to discriminate seen classes from unseen classes. We assessed and compared the performance of various existing algorithms on HZSL for gestures using two standard datasets: MSRC-12 and CGD2011. For four unseen classes, results show that the marginal accuracy of HZSL-15.2% and G-HZSL-14.39% are comparable to the performance of conventional ZSL. Given that we used only one instance and do not assume that test classes are unseen, the performance of HZSL and G-HZSL models were remarkable.\n
\n\n\n\n \n\n \n \n \n \n \n Augmented visual instruction for surgical practice and training.\n \n \n \n\n\n \n Andersen, D.; Lin, C.; Popescu, V.; Munoz, E., R.; Eugenia Cabrera, M.; Mullis, B.; Zarzaur, B.; Marley, S.; and Wachs, J.\n\n\n \n\n\n\n In 2018 IEEE Workshop on Augmented and Virtual Realities for Good, VAR4Good 2018, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Augmented visual instruction for surgical practice and training},\n type = {inproceedings},\n year = {2018},\n keywords = {Applied computing - Health care information system,Human-centered computing - Mixed / augmented reali},\n id = {8c18adde-14f4-352c-9cc0-fb90b0cc9294},\n created = {2021-06-04T19:36:51.348Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.525Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper presents two positions about the use of augmented reality (AR) in healthcare scenarios, informed by the authors' experience as an interdisciplinary team of academics and medical practicioners who have been researching, implementing, and validating an AR surgical telementoring system. First, AR has the potential to greatly improve the areas of surgical telementoring and of medical training on patient simulators. In austere environments, surgical telementoring that connects surgeons with remote experts can be enhanced with the use of AR annotations visualized directly in the surgeon's field of view. Patient simulators can gain additional value for medical training by overlaying the current and future steps of procedures as AR imagery onto a physical simulator. Second, AR annotations for telementoring and for simulator-based training can be delivered either by video see-through tablet displays or by AR head-mounted displays (HMDs). The paper discusses the two AR approaches by looking at accuracy, depth perception, visualization continuity, visualization latency, and user encumbrance. Specific advantages and disadvantages to each approach mean that the choice of one display method or another must be carefully tailored to the healthcare application in which it is being used.},\n bibtype = {inproceedings},\n author = {Andersen, Daniel and Lin, Chengyuan and Popescu, Voicu and Munoz, Edgar Rojas and Eugenia Cabrera, Maria and Mullis, Brian and Zarzaur, Ben and Marley, Sherri and Wachs, Juan},\n doi = {10.1109/VAR4GOOD.2018.8576884},\n booktitle = {2018 IEEE Workshop on Augmented and Virtual Realities for Good, VAR4Good 2018}\n}\n\n This paper presents two positions about the use of augmented reality (AR) in healthcare scenarios, informed by the authors' experience as an interdisciplinary team of academics and medical practicioners who have been researching, implementing, and validating an AR surgical telementoring system. First, AR has the potential to greatly improve the areas of surgical telementoring and of medical training on patient simulators. In austere environments, surgical telementoring that connects surgeons with remote experts can be enhanced with the use of AR annotations visualized directly in the surgeon's field of view. Patient simulators can gain additional value for medical training by overlaying the current and future steps of procedures as AR imagery onto a physical simulator. Second, AR annotations for telementoring and for simulator-based training can be delivered either by video see-through tablet displays or by AR head-mounted displays (HMDs). The paper discusses the two AR approaches by looking at accuracy, depth perception, visualization continuity, visualization latency, and user encumbrance. Specific advantages and disadvantages to each approach mean that the choice of one display method or another must be carefully tailored to the healthcare application in which it is being used.\n
\n\n\n\n \n\n \n \n \n \n \n Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (uavs).\n \n \n \n\n\n \n Ye, D., H.; Li, J.; Chen, Q.; Wachs, J.; and Bouman, C.\n\n\n \n\n\n\n In IS and T International Symposium on Electronic Imaging Science and Technology, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Deep learning for moving object detection and tracking from a single camera in unmanned aerial vehicles (uavs)},\n type = {inproceedings},\n year = {2018},\n id = {e093919e-9470-375a-b937-c8ac4c9351ec},\n created = {2021-06-04T19:36:51.391Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.570Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Unmanned Aerial Vehicles (UAVs) gain popularity in a wide range of civilian and military applications. Such emerging interest is pushing the development of effective collision avoidance systems which are especially crucial in a crowded airspace setting. Because of cost and weight limitations associated with UAVs' payload, the optical sensors, simply digital cameras, are widely used for collision avoidance systems in UAVs. This requires moving object detection and tracking algorithms from a video, which can be run on board efficiently. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify moving object candidates in the background subtracted image through deep learning classifier trained on manually labeled datasets. For each moving object candidates, we find spatio-temporal traits through optical flow matching and then prune them based on their motion patterns compared with the background. Kalman filter is applied on pruned moving objects to improve temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results demonstrate that our algorithm can effectively detect and track small UAVs with limited computing resources.},\n bibtype = {inproceedings},\n author = {Ye, Dong Hye and Li, Jing and Chen, Qiulin and Wachs, Juan and Bouman, Charles},\n doi = {10.2352/ISSN.2470-1173.2018.10.IMAWM-466},\n booktitle = {IS and T International Symposium on Electronic Imaging Science and Technology}\n}\n\n Unmanned Aerial Vehicles (UAVs) gain popularity in a wide range of civilian and military applications. Such emerging interest is pushing the development of effective collision avoidance systems which are especially crucial in a crowded airspace setting. Because of cost and weight limitations associated with UAVs' payload, the optical sensors, simply digital cameras, are widely used for collision avoidance systems in UAVs. This requires moving object detection and tracking algorithms from a video, which can be run on board efficiently. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify moving object candidates in the background subtracted image through deep learning classifier trained on manually labeled datasets. For each moving object candidates, we find spatio-temporal traits through optical flow matching and then prune them based on their motion patterns compared with the background. Kalman filter is applied on pruned moving objects to improve temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results demonstrate that our algorithm can effectively detect and track small UAVs with limited computing resources.\n
\n\n\n\n \n\n \n \n \n \n \n Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks.\n \n \n \n\n\n \n Cha, J.; Gonzalez, G.; Sulek, J.; Sundaram, C.; Wachs, J.; and Yu, D.\n\n\n \n\n\n\n Frontiers in Human Neuroscience, 12. 2018.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks},\n type = {article},\n year = {2018},\n volume = {12},\n id = {badf8858-f1ea-3e1b-8f58-bf75847ef217},\n created = {2021-06-04T19:36:51.755Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.772Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Cha, Jackie and Gonzalez, Glebys and Sulek, Jay and Sundaram, Chandru and Wachs, Juan and Yu, Denny},\n doi = {10.3389/conf.fnhum.2018.227.00036},\n journal = {Frontiers in Human Neuroscience}\n}\n\n \n\n \n \n \n \n \n Looking beyond the gesture: Vocabulary acceptability Criteria for gesture elicitation studies.\n \n \n \n\n\n \n Gonzalez, G.; Madapana, N.; Taneja, R.; Zhang, L.; Rodgers, R.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings of the Human Factors and Ergonomics Society, volume 2, pages 997-1001, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Looking beyond the gesture: Vocabulary acceptability Criteria for gesture elicitation studies},\n type = {inproceedings},\n year = {2018},\n pages = {997-1001},\n volume = {2},\n id = {3a5f9c85-4403-369c-9e63-11719bb25ab2},\n created = {2021-06-04T19:36:51.940Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.105Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Gonzalez2018},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The choice of what gestures should be part of a gesture language is a critical step in the design of gesture-based interfaces. This step is especially important when time and accuracy are key factors of the user experience, such as gestural interfaces in vehicle control and sterile control of a picture archiving and communication system (PACS) in the operating room (OR). Agreement studies are commonly used to find the gesture preference of the end users. These studies hypothesize that the best available gesture lexicon is the one preferred by a majority. However, these agreement approaches cannot offer a metric to assess the qualitative aspects of gestures. In this work, we propose an experimental framework to quantify, compare and evaluate gestures. This framework is grounded in the expert knowledge of speech and language professionals (SLPs). The development consisted of three studies: 1) Creation, 2) Evaluation and 3) Validation. In the creation study, we followed an adapted version of the Delphi's interview/discussion procedure with SLPs. The purpose was to obtain the Vocabulary Acceptability Criteria (VAC) to evaluate gestures. Next, in the evaluation study, a modified method of pairwise comparisons was used to rank and quantify the gestures based on each criteria (VAC). Lastly, in the validation study, we formulated an odd one out procedure, to prove that the VAC values of a gesture are representative and sufficiently distinctive, to select that particular gesture from a pool of gestures. We applied this framework to the gestures obtained from a gesture elicitation study conducted with nine neurosurgeons, to control an imaging software. In addition, 29 SLPs comprising of 17 experts and 12 graduate students participated in the VAC study. The best lexicons from the available pool were obtained through both agreement and VAC metrics. We used binomial tests to show that the results obtained from the validation procedure are significantly better than the baseline. These results verify our hypothesis that the VAC are representative of the gestures and the subjects should be able to select the right gesture given its VAC values.},\n bibtype = {inproceedings},\n author = {Gonzalez, Glebys and Madapana, Naveen and Taneja, Rahul and Zhang, Lingsong and Rodgers, Richard and Wachs, Juan P.},\n doi = {10.1177/1541931218621230},\n booktitle = {Proceedings of the Human Factors and Ergonomics Society}\n}\n\n The choice of what gestures should be part of a gesture language is a critical step in the design of gesture-based interfaces. This step is especially important when time and accuracy are key factors of the user experience, such as gestural interfaces in vehicle control and sterile control of a picture archiving and communication system (PACS) in the operating room (OR). Agreement studies are commonly used to find the gesture preference of the end users. These studies hypothesize that the best available gesture lexicon is the one preferred by a majority. However, these agreement approaches cannot offer a metric to assess the qualitative aspects of gestures. In this work, we propose an experimental framework to quantify, compare and evaluate gestures. This framework is grounded in the expert knowledge of speech and language professionals (SLPs). The development consisted of three studies: 1) Creation, 2) Evaluation and 3) Validation. In the creation study, we followed an adapted version of the Delphi's interview/discussion procedure with SLPs. The purpose was to obtain the Vocabulary Acceptability Criteria (VAC) to evaluate gestures. Next, in the evaluation study, a modified method of pairwise comparisons was used to rank and quantify the gestures based on each criteria (VAC). Lastly, in the validation study, we formulated an odd one out procedure, to prove that the VAC values of a gesture are representative and sufficiently distinctive, to select that particular gesture from a pool of gestures. We applied this framework to the gestures obtained from a gesture elicitation study conducted with nine neurosurgeons, to control an imaging software. In addition, 29 SLPs comprising of 17 experts and 12 graduate students participated in the VAC study. The best lexicons from the available pool were obtained through both agreement and VAC metrics. We used binomial tests to show that the results obtained from the validation procedure are significantly better than the baseline. These results verify our hypothesis that the VAC are representative of the gestures and the subjects should be able to select the right gesture given its VAC values.\n
\n\n\n\n \n\n \n \n \n \n \n A First-Person Mentee Second-Person Mentor AR Interface for Surgical Telementoring.\n \n \n \n\n\n \n Lin, C.; Andersen, D.; Popescu, V.; Rojas-Munoz, E.; Cabrera, M., E.; Mullis, B.; Zarzaur, B.; Anderson, K.; Marley, S.; and Wachs, J.\n\n\n \n\n\n\n In Adjunct Proceedings - 2018 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2018, pages 3-8, 2018. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A First-Person Mentee Second-Person Mentor AR Interface for Surgical Telementoring},\n type = {inproceedings},\n year = {2018},\n keywords = {Human computer interaction,Human-centered computing,Interaction paradigms,Mixed / augmented reality},\n pages = {3-8},\n id = {86d5efae-3e82-366b-9cd9-e71d825d151b},\n created = {2021-06-04T19:36:52.051Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.154Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This application paper presents the work of a multidisciplinary group of designing, implementing, and testing an Augmented Reality (AR) surgical telementoring system. The system acquires the surgical field with an overhead camera, the video feed is transmitted to the remote mentor, where it is displayed on a touch-based interaction table, the mentor annotates the video feed, the annotations are sent back to the mentee, where they are displayed into the mentee's field of view using an optical see-through AR head-mounted display (HMD). The annotations are reprojected from the mentor's second-person view of the surgical field to the mentee's first-person view. The mentee sees the annotations with depth perception, and the annotations remain anchored to the surgical field as the mentee moves their head. Average annotation display accuracy is 1.22cm. The system was tested in the context of a user study where surgery residents ($n = 20$) were asked to perform a lower-leg fasciotomy on cadaver models. Participants who benefited from telementoring using our system received a higher Individual Performance Score, and they reported higher usability and self confidence levels.},\n bibtype = {inproceedings},\n author = {Lin, Chengyuan and Andersen, Daniel and Popescu, Voicu and Rojas-Munoz, Edgar and Cabrera, Maria Eugenia and Mullis, Brian and Zarzaur, Ben and Anderson, Kathryn and Marley, Sherri and Wachs, Juan},\n doi = {10.1109/ISMAR-Adjunct.2018.00021},\n booktitle = {Adjunct Proceedings - 2018 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2018}\n}\n\n This application paper presents the work of a multidisciplinary group of designing, implementing, and testing an Augmented Reality (AR) surgical telementoring system. The system acquires the surgical field with an overhead camera, the video feed is transmitted to the remote mentor, where it is displayed on a touch-based interaction table, the mentor annotates the video feed, the annotations are sent back to the mentee, where they are displayed into the mentee's field of view using an optical see-through AR head-mounted display (HMD). The annotations are reprojected from the mentor's second-person view of the surgical field to the mentee's first-person view. The mentee sees the annotations with depth perception, and the annotations remain anchored to the surgical field as the mentee moves their head. Average annotation display accuracy is 1.22cm. The system was tested in the context of a user study where surgery residents ($n = 20$) were asked to perform a lower-leg fasciotomy on cadaver models. Participants who benefited from telementoring using our system received a higher Individual Performance Score, and they reported higher usability and self confidence levels.\n
\n\n\n\n \n\n \n \n \n \n \n \n Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks.\n \n \n \n \n\n\n \n Cha, J.; Gonzalez, G.; Sulek, J.; Sundaram, C.; Wachs, J.; and Yu, D.\n\n\n \n\n\n\n Frontiers in Human Neuroscience, 12. 2018.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks},\n type = {article},\n year = {2018},\n volume = {12},\n websites = {http://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389%2Fconf.fnhum.2018.227.00036},\n id = {e66465a0-eb3f-31e7-95d9-cf3ab6b0afaf},\n created = {2022-03-17T12:12:10.039Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T12:12:10.039Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Cha, Jackie and Gonzalez, Glebys and Sulek, Jay and Sundaram, Chandru and Wachs, Juan and Yu, Denny},\n doi = {10.3389/conf.fnhum.2018.227.00036},\n journal = {Frontiers in Human Neuroscience}\n}\n\n \n\n \n \n \n \n \n \n Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks.\n \n \n \n \n\n\n \n Cha, J.; Gonzalez, G.; Sulek, J.; Sundaram, C.; Wachs, J.; and Yu, D.\n\n\n \n\n\n\n Frontiers in Human Neuroscience, 12. 2018.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Measuring Workload Through EEG Signals in Simulated Robotic Assisted Surgery Tasks},\n type = {article},\n year = {2018},\n volume = {12},\n websites = {http://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389%2Fconf.fnhum.2018.227.00036},\n id = {6abf5b4d-5460-31cf-b342-cb5a0caced98},\n created = {2022-03-17T12:12:10.073Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-03-17T12:12:10.073Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Cha, Jackie and Gonzalez, Glebys and Sulek, Jay and Sundaram, Chandru and Wachs, Juan and Yu, Denny},\n doi = {10.3389/conf.fnhum.2018.227.00036},\n journal = {Frontiers in Human Neuroscience}\n}\n\n
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\n \n \n\n \n \n \n \n \n \n First International Workshop on Adaptive Shot Learning for Gesture Understanding and Production.\n \n \n \n \n\n\n \n Wachs, J., P.\n\n\n \n\n\n\n 2017.\n \n\n\n\n
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\n\n\n\n@misc{\n title = {First International Workshop on Adaptive Shot Learning for Gesture Understanding and Production},\n type = {misc},\n year = {2017},\n websites = {https://engineering.purdue.edu/ASL4GUP/},\n id = {3508fe1e-b155-3b5f-8064-601d2e84ff1c},\n created = {2017-09-25T20:17:56.972Z},\n accessed = {2017-01-01},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:07.697Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2017},\n private_publication = {false},\n bibtype = {misc},\n author = {Wachs, J. P}\n}\n\n \n\n \n \n \n \n \n What Makes a Gesture a Gesture? Neural Signatures Involved in Gesture Recognition.\n \n \n \n\n\n \n Cabrera, M.; Novak, K.; Foti, D.; Voyles, R.; and Wachs, J.\n\n\n \n\n\n\n In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge, 2017. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {What Makes a Gesture a Gesture? Neural Signatures Involved in Gesture Recognition},\n type = {inproceedings},\n year = {2017},\n id = {0e688866-b0bd-3719-8d4e-dffcf4bd6e20},\n created = {2018-03-14T02:09:57.030Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.368Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Cabrera2017a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2017 IEEE. Previous work in the area of gesture production, has made the assumption that machines can replicate humanlike gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that claim using electroencephalography (EEG). That is, this paper attempts to find neural signatures of gestures (also referred as placeholders) in human cognition, which facilitate the understanding, learning and repetition of gestures. Further, it is discussed whether there is a direct mapping between the placeholders and kinematic salient points in the gesture trajectories. These are expressed as relationships between inflection points in the gestures trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved by correlating fluctuations in mu power during gesture observation with salient motion points found for each gesture. Peaks in the EEG signal at central electrodes (motor cortex; C3/Cz/C4) and occipital electrodes (visual cortex; O3/Oz/O4) were used to isolate the salient events within each gesture. We found that a linear model predicting mu peaks from motion inflections fits the data well. Increases in EEG power were detected 380 and 500ms after inflection points at occipital and central electrodes, respectively. These results suggest that coordinated activity in visual and motor cortices is sensitive to motion trajectories during gesture observation, and it is consistent with the proposal that inflection points operate as placeholders in gesture recognition.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. and Novak, K. and Foti, D. and Voyles, R. and Wachs, J.P.},\n doi = {10.1109/FG.2017.93},\n booktitle = {Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge}\n}\n\n © 2017 IEEE. Previous work in the area of gesture production, has made the assumption that machines can replicate humanlike gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that claim using electroencephalography (EEG). That is, this paper attempts to find neural signatures of gestures (also referred as placeholders) in human cognition, which facilitate the understanding, learning and repetition of gestures. Further, it is discussed whether there is a direct mapping between the placeholders and kinematic salient points in the gesture trajectories. These are expressed as relationships between inflection points in the gestures trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved by correlating fluctuations in mu power during gesture observation with salient motion points found for each gesture. Peaks in the EEG signal at central electrodes (motor cortex; C3/Cz/C4) and occipital electrodes (visual cortex; O3/Oz/O4) were used to isolate the salient events within each gesture. We found that a linear model predicting mu peaks from motion inflections fits the data well. Increases in EEG power were detected 380 and 500ms after inflection points at occipital and central electrodes, respectively. These results suggest that coordinated activity in visual and motor cortices is sensitive to motion trajectories during gesture observation, and it is consistent with the proposal that inflection points operate as placeholders in gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n \n BIMANUAL MULTIMODAL IMAGE SUBSTITUTION PERCEPTION: A COMPARISON STUDY.\n \n \n \n \n\n\n \n Zhang, T.; Wachs, J.; and Duerstock, B.\n\n\n \n\n\n\n In resna.org, 2017. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {BIMANUAL MULTIMODAL IMAGE SUBSTITUTION PERCEPTION: A COMPARISON STUDY},\n type = {inproceedings},\n year = {2017},\n websites = {https://www.resna.org/sites/default/files/conference/2017/pdf_versions/cac/Ting.pdf},\n id = {0e306bdd-6bb7-3614-8096-f403762c6169},\n created = {2019-02-03T16:51:55.347Z},\n accessed = {2019-02-03},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:08.707Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Zhang2017b},\n private_publication = {false},\n abstract = {Rehabilitation engineering & assistive technology of North America (RESNA)},\n bibtype = {inproceedings},\n author = {Zhang, T and Wachs, JP and Duerstock, BS},\n booktitle = {resna.org}\n}\n\n Rehabilitation engineering & assistive technology of North America (RESNA)\n
\n\n\n\n \n\n \n \n \n \n \n Communication Modalities for Supervised Teleoperation in Highly Dexterous Tasks – Does one size fit all?.\n \n \n \n\n\n \n Zhou, T.; Cabrera, M.; and Wachs, J.\n\n\n \n\n\n\n 2017.\n \n\n\n\n
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\n\n\n@misc{\n title = {Communication Modalities for Supervised Teleoperation in Highly Dexterous Tasks – Does one size fit all?},\n type = {misc},\n year = {2017},\n source = {arXiv},\n id = {37e561f3-adb3-3250-a2de-e3a13d7f4c0d},\n created = {2020-11-03T23:59:00.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:31.062Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Copyright © 2017, arXiv, All rights reserved. This study tries to explain the connection between communication modalities and levels of supervision in teleoperation during a dexterous task, like surgery. This concept is applied to two surgical related tasks: incision and peg transfer. It was found that as the complexity of the task escalates, the combination linking human supervision with a more expressive modality shows better performance than other combinations of modalities and control. More specifically, in the peg transfer task, the combination of speech modality and action level supervision achieves shorter task completion time (77.1 ±3.4 s) with fewer mistakes (0.20±0.17 pegs dropped).},\n bibtype = {misc},\n author = {Zhou, T. and Cabrera, M.E. and Wachs, J.P.}\n}\n\n Copyright © 2017, arXiv, All rights reserved. This study tries to explain the connection between communication modalities and levels of supervision in teleoperation during a dexterous task, like surgery. This concept is applied to two surgical related tasks: incision and peg transfer. It was found that as the complexity of the task escalates, the combination linking human supervision with a more expressive modality shows better performance than other combinations of modalities and control. More specifically, in the peg transfer task, the combination of speech modality and action level supervision achieves shorter task completion time (77.1 ±3.4 s) with fewer mistakes (0.20±0.17 pegs dropped).\n
\n\n\n\n \n\n \n \n \n \n \n \n Bimanual Multimodal Image Substitution Perception: A Comparison Study.\n \n \n \n \n\n\n \n Zhang, T.; Wachs, J., P.; and Duerstock, B., S.\n\n\n \n\n\n\n In Annual Rehabilitation Engineering Society of North America Conference, 2017. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Bimanual Multimodal Image Substitution Perception: A Comparison Study},\n type = {inproceedings},\n year = {2017},\n websites = {https://www.resna.org/sites/default/files/conference/2017/cac/Ting.html},\n id = {bb6db8a5-b76f-3ab3-9421-b191d715ada5},\n created = {2021-06-04T19:22:43.197Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.260Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {An increasing number of computer interfaces have been developed to assist blind or visually impaired individuals to perceive or understand the content of digital images. However, there are a few studies focusing on increasing the efficiency and accuracy of image perception using different computer interface designs. This paper investigated two design factors discussed in previous research: single/bimanual interaction, and vertical/ horizontal image exploration. We developed three candidate systems by alternating the two factors. Pair-wised comparisons were made among these alternatives based on experiments with human subjects. Horizontal image exploration showed better performance than the vertical alternative. However, more study is needed to investigate the application of bimanual interaction.},\n bibtype = {inproceedings},\n author = {Zhang, Ting and Wachs, Juan P. and Duerstock, Bradley S.},\n booktitle = {Annual Rehabilitation Engineering Society of North America Conference}\n}\n\n An increasing number of computer interfaces have been developed to assist blind or visually impaired individuals to perceive or understand the content of digital images. However, there are a few studies focusing on increasing the efficiency and accuracy of image perception using different computer interface designs. This paper investigated two design factors discussed in previous research: single/bimanual interaction, and vertical/ horizontal image exploration. We developed three candidate systems by alternating the two factors. Pair-wised comparisons were made among these alternatives based on experiments with human subjects. Horizontal image exploration showed better performance than the vertical alternative. However, more study is needed to investigate the application of bimanual interaction.\n
\n\n\n\n \n\n \n \n \n \n \n \n Coherency in One-Shot Gesture Recognition.\n \n \n \n \n\n\n \n Wachs, J., J.; Voyles, R.; Cabrera, M., M.; Voyles, R.; and Wachs, J., J.\n\n\n \n\n\n\n arXiv preprint arXiv:1701.05924. 2017.\n \n\n\n\n
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\n\n\n@article{\n title = {Coherency in One-Shot Gesture Recognition},\n type = {article},\n year = {2017},\n websites = {https://arxiv.org/abs/1701.05924},\n id = {0530384c-513b-3c26-a243-c2a68515b66b},\n created = {2021-06-04T19:36:47.700Z},\n accessed = {2017-01-29},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:10.660Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2017e},\n private_publication = {false},\n abstract = {Copyright © 2017, arXiv, All rights reserved. User’s intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures itself, recently new approaches were proposed to deal with gesture perception and production as part of the same problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than mining the gesture’s associated features. This is achieved using kinematic, cognitive and biomechanic characteristics of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation. The generated samples are then used as training sets for different state-of-the-art classifiers. Performance is obtained first, by observing the machines’ gesture recognition percentages. Then, performance is computed by the human recognition from gestures performed by robots. Based on these two scenarios, a composite new metric of coherency is proposed relating to the amount of agreement between these two conditions. Experimental results provide an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherency in recognition was determined at 93.6%. While this new metric is not directly comparable to raw accuracy or other pure performance-based standard metrics, it provides a quantifier for validating how realistic the machine generated samples are and how accurate the resulting mimicry is.},\n bibtype = {article},\n author = {Wachs, J.P. Juan and Voyles, Richard and Cabrera, M.E. Maria and Voyles, Richard and Wachs, J.P. Juan},\n journal = {arXiv preprint arXiv:1701.05924}\n}\n\n Copyright © 2017, arXiv, All rights reserved. User’s intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures itself, recently new approaches were proposed to deal with gesture perception and production as part of the same problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than mining the gesture’s associated features. This is achieved using kinematic, cognitive and biomechanic characteristics of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation. The generated samples are then used as training sets for different state-of-the-art classifiers. Performance is obtained first, by observing the machines’ gesture recognition percentages. Then, performance is computed by the human recognition from gestures performed by robots. Based on these two scenarios, a composite new metric of coherency is proposed relating to the amount of agreement between these two conditions. Experimental results provide an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherency in recognition was determined at 93.6%. While this new metric is not directly comparable to raw accuracy or other pure performance-based standard metrics, it provides a quantifier for validating how realistic the machine generated samples are and how accurate the resulting mimicry is.\n
\n\n\n\n \n\n \n \n \n \n \n \n ZSGL: Zero shot gestural learning.\n \n \n \n \n\n\n \n Madapana, N.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings of the 19th ACM International Conference on Multimodal Interaction (ICMI), volume 2017-Janua, pages 331-335, 2017. ACM Press\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {ZSGL: Zero shot gestural learning},\n type = {inproceedings},\n year = {2017},\n keywords = {Attribute based classification,Gesture recognition,Semantic descriptions,Transfer learning,Zero Shot Learning,Zero shot learning,attribute based classification,gesture recognition,semantic descriptions,transfer learning},\n pages = {331-335},\n volume = {2017-Janua},\n websites = {http://dl.acm.org/citation.cfm?doid=3136755.3136774},\n publisher = {ACM Press},\n city = {New York, New York, USA},\n id = {a430994b-437e-3a0b-ada0-ea95d08862f9},\n created = {2021-06-04T19:36:47.913Z},\n accessed = {2018-02-04},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.803Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Madapana2017d},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Gesture recognition systems enable humans to interact with machines in an intuitive and a natural way. Humans tend to create the gestures on the fly and conventional systems lack adaptability to learn new gestures beyond the training stage. This problem can be best addressed using Zero Shot Learning (ZSL), a paradigm in machine learning that aims to recognize unseen objects by just having a description of them. ZSL for gestures has hardly been addressed in computer vision research due to the inherent ambiguity and the contextual dependency associated with the gestures. This work proposes an approach for Zero Shot Gestural Learning (ZSGL) by leveraging the semantic information that is embedded in the gestures. First, a human factors based approach has been followed to generate semantic descriptors for gestures that can generalize to the existing gesture classes. Second, we assess the performance of various existing state-of-The-Art algorithms on ZSL for gestures using two standard datasets: MSRC-12 and CGD2011 dataset. The obtained results (26.35% - unseen class accuracy) parallel the benchmark accuracies of attribute-based object recognition and justifies our claim that ZSL is a desirable paradigm for gesture based systems.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, Juan P.},\n doi = {10.1145/3136755.3136774},\n booktitle = {Proceedings of the 19th ACM International Conference on Multimodal Interaction (ICMI)}\n}\n\n Gesture recognition systems enable humans to interact with machines in an intuitive and a natural way. Humans tend to create the gestures on the fly and conventional systems lack adaptability to learn new gestures beyond the training stage. This problem can be best addressed using Zero Shot Learning (ZSL), a paradigm in machine learning that aims to recognize unseen objects by just having a description of them. ZSL for gestures has hardly been addressed in computer vision research due to the inherent ambiguity and the contextual dependency associated with the gestures. This work proposes an approach for Zero Shot Gestural Learning (ZSGL) by leveraging the semantic information that is embedded in the gestures. First, a human factors based approach has been followed to generate semantic descriptors for gestures that can generalize to the existing gesture classes. Second, we assess the performance of various existing state-of-The-Art algorithms on ZSL for gestures using two standard datasets: MSRC-12 and CGD2011 dataset. The obtained results (26.35% - unseen class accuracy) parallel the benchmark accuracies of attribute-based object recognition and justifies our claim that ZSL is a desirable paradigm for gesture based systems.\n
\n\n\n\n \n\n \n \n \n \n \n A Semantical & Analytical Approach for Zero Shot Gesture Learning.\n \n \n \n\n\n \n Madapana, N.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge, pages 796-801, 5 2017. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A Semantical & Analytical Approach for Zero Shot Gesture Learning},\n type = {inproceedings},\n year = {2017},\n pages = {796-801},\n month = {5},\n publisher = {IEEE},\n id = {73de080d-64e9-3e33-9aa9-65253306eb8a},\n created = {2021-06-04T19:36:48.417Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.511Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {de18aff7-aef8-4672-8a1e-b18809375bc4,b43d1b86-b425-4322-b575-14547700e015,efa197bd-47b9-49bc-a0e1-3b4e7ad48a48},\n private_publication = {false},\n abstract = {Zero shot learning (ZSL) is about being able to recognize gesture classes that were never seen before. This type of recognition involves the understanding that the presented gesture is a new form of expression from those observed so far, and yet carries embedded information universal to all the other gestures (also referred as context). As part of the same problem, it is required to determine what action/command this new gesture conveys, in order to react to the command autonomously. Research in this area may shed light to areas where ZSL occurs, such as spontaneous gestures. People perform gestures that may be new to the observer. This occurs when the gesturer is learning, solving a problem or acquiring a new language. The ability of having a machine recognizing spontaneous gesturing, in the same manner as humans do, would enable more fluent human-machine interaction. In this paper, we describe a new paradigm for ZSL based on adaptive learning, where it is possible to determine the amount of transfer learning carried out by the algorithm and how much knowledge is acquired from a new gesture observation. Another contribution is a procedure to determine what are the best semantic descriptors for a given command and how to use those as part of the ZSL approach proposed.},\n bibtype = {inproceedings},\n author = {Madapana, Naveen and Wachs, J.P. Juan P.},\n doi = {10.1109/FG.2017.100},\n booktitle = {Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge}\n}\n\n Zero shot learning (ZSL) is about being able to recognize gesture classes that were never seen before. This type of recognition involves the understanding that the presented gesture is a new form of expression from those observed so far, and yet carries embedded information universal to all the other gestures (also referred as context). As part of the same problem, it is required to determine what action/command this new gesture conveys, in order to react to the command autonomously. Research in this area may shed light to areas where ZSL occurs, such as spontaneous gestures. People perform gestures that may be new to the observer. This occurs when the gesturer is learning, solving a problem or acquiring a new language. The ability of having a machine recognizing spontaneous gesturing, in the same manner as humans do, would enable more fluent human-machine interaction. In this paper, we describe a new paradigm for ZSL based on adaptive learning, where it is possible to determine the amount of transfer learning carried out by the algorithm and how much knowledge is acquired from a new gesture observation. Another contribution is a procedure to determine what are the best semantic descriptors for a given command and how to use those as part of the ZSL approach proposed.\n
\n\n\n\n \n\n \n \n \n \n \n Finding a needle in a haystack: Recognizing surgical instruments through vision and manipulation.\n \n \n \n\n\n \n Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In IS and T International Symposium on Electronic Imaging Science and Technology, volume Part F1300, pages 37-45, 2017. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Finding a needle in a haystack: Recognizing surgical instruments through vision and manipulation},\n type = {inproceedings},\n year = {2017},\n pages = {37-45},\n volume = {Part F1300},\n id = {552e1da8-2a36-30c1-89f8-c0f6eaeb333d},\n created = {2021-06-04T19:36:48.631Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.703Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2017},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper presents an accurate and robust surgical instrument recognition algorithm to be used as part of a Robotic Scrub Nurse (RSN). Surgical instruments are often cluttered, occluded and displaying specular light, which cause a challenge for conventional vision algorithms. A learning-through-interaction paradigm was proposed to tackle this challenge. The approach combines computer vision with robot manipulation to achieve active recognition. The unknown instrument is firstly segmented out as blobs and its poses estimated, then the RSN system picks it up and presents it to an optical sensor in an established pose. Lastly the unknown instrument is recognized with high confidence. Experiments were conducted to evaluate the performance of the proposed segmentation and recognition algorithms, respectively. It is found out that the proposed patch-based segmentation algorithm and the instrument recognition algorithm greatly outperform their benchmark comparisons. Such results indicate the applicability and effectiveness of our RSN system in performing accurate and robust surgical instrument recognition.},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Wachs, J.P. Juan P.},\n doi = {10.2352/ISSN.2470-1173.2017.9.IRIACV-264},\n booktitle = {IS and T International Symposium on Electronic Imaging Science and Technology}\n}\n\n This paper presents an accurate and robust surgical instrument recognition algorithm to be used as part of a Robotic Scrub Nurse (RSN). Surgical instruments are often cluttered, occluded and displaying specular light, which cause a challenge for conventional vision algorithms. A learning-through-interaction paradigm was proposed to tackle this challenge. The approach combines computer vision with robot manipulation to achieve active recognition. The unknown instrument is firstly segmented out as blobs and its poses estimated, then the RSN system picks it up and presents it to an optical sensor in an established pose. Lastly the unknown instrument is recognized with high confidence. Experiments were conducted to evaluate the performance of the proposed segmentation and recognition algorithms, respectively. It is found out that the proposed patch-based segmentation algorithm and the instrument recognition algorithm greatly outperform their benchmark comparisons. Such results indicate the applicability and effectiveness of our RSN system in performing accurate and robust surgical instrument recognition.\n
\n\n\n\n \n\n \n \n \n \n \n Needle in a haystack: Interactive surgical instrument recognition through perception and manipulation.\n \n \n \n\n\n \n Zhou, T.; and Wachs, J., J., P.\n\n\n \n\n\n\n Robotics and Autonomous Systems, 97: 182-192. 2017.\n \n\n\n\n
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\n\n\n@article{\n title = {Needle in a haystack: Interactive surgical instrument recognition through perception and manipulation},\n type = {article},\n year = {2017},\n pages = {182-192},\n volume = {97},\n id = {90b6c5a9-7d19-3507-af6e-765e27a2210a},\n created = {2021-06-04T19:36:48.659Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.703Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2017b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper presents a solution to the challenge of accurate surgical instrument recognition by a Robotic Scrub Nurse (RSN) in the Operating Room (OR). Surgical instruments, placed on the surgical mayo tray, are often cluttered, occluded and display specular light which poses a challenge for conventional recognition algorithms. To tackle this problem we resort to a hybrid computer vision and robotic manipulation combined strategy. The instruments are first segmented and pose is estimated, then the RSN system picks up the unknown instruments and presents them to the optical sensor in the determined pose. Last, the instruments are recognized and delivered. Experiments were conducted to evaluate the performance of the proposed segmentation, grasping and recognition algorithms, respectively. The proposed patch-based segmentation algorithm can achieve an F-score of 0.90. The proposed force-based grasping protocol can achieve an average picking success rate of 92% with various instrument layouts, and the proposed attention-based instrument recognition module can reach a recognition accuracy of 95.6%. Experimental results indicate the applicability and effectiveness of a RSN to perform accurate and robust surgical instrument recognition.},\n bibtype = {article},\n author = {Zhou, Tian and Wachs, J.P. Juan P.},\n doi = {10.1016/j.robot.2017.08.013},\n journal = {Robotics and Autonomous Systems}\n}\n\n This paper presents a solution to the challenge of accurate surgical instrument recognition by a Robotic Scrub Nurse (RSN) in the Operating Room (OR). Surgical instruments, placed on the surgical mayo tray, are often cluttered, occluded and display specular light which poses a challenge for conventional recognition algorithms. To tackle this problem we resort to a hybrid computer vision and robotic manipulation combined strategy. The instruments are first segmented and pose is estimated, then the RSN system picks up the unknown instruments and presents them to the optical sensor in the determined pose. Last, the instruments are recognized and delivered. Experiments were conducted to evaluate the performance of the proposed segmentation, grasping and recognition algorithms, respectively. The proposed patch-based segmentation algorithm can achieve an F-score of 0.90. The proposed force-based grasping protocol can achieve an average picking success rate of 92% with various instrument layouts, and the proposed attention-based instrument recognition module can reach a recognition accuracy of 95.6%. Experimental results indicate the applicability and effectiveness of a RSN to perform accurate and robust surgical instrument recognition.\n
\n\n\n\n \n\n \n \n \n \n \n Multimodal perception of histological images for persons who are blind or visually impaired.\n \n \n \n\n\n \n Zhang, T.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n ACM Transactions on Accessible Computing, 9(3). 2017.\n \n\n\n\n
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\n\n\n@article{\n title = {Multimodal perception of histological images for persons who are blind or visually impaired},\n type = {article},\n year = {2017},\n keywords = {Blind or visually impaired,Haptics,Image perception,Multi-modality,Sensorial substitution,Vibrotactile},\n volume = {9},\n id = {6a1de9fe-27c0-32d7-94db-da87883ce394},\n created = {2021-06-04T19:36:49.640Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.727Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhang2017},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Lack of suitable substitute assistive technology is a roadblock for students and scientists who are blind or visually impaired (BVI) from advancing in careers in science, technology, engineering, and mathematics (STEM) fields. It is challenging for persons who are BVI to interpret real-time visual scientific data which is commonly generated during lab experimentation, such as performing light microscopy, spectrometry, and observing chemical reactions. To address this problem, a real-time multimodal image perception system was developed to allow standard laboratory blood smear images to be perceived by BVI individuals by employing a combination of auditory, haptic, and vibrotactile feedback. These sensory feedback modalities were used to convey visual information through alternative perceptual channels, thus creating a palette of multimodal, sensory information. Two sets of image features of interest (primary and peripheral features) were applied to characterize images. A Bayesian network was applied to construct causal relations between these two groups of features. In order to match primary features with sensor modalities, two methods were conceived. Experimental results confirmed that this real-time approach produced higher accuracy in recognizing and analyzing objects within images compared to conventional tactile images.},\n bibtype = {article},\n author = {Zhang, Ting and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1145/3026794},\n journal = {ACM Transactions on Accessible Computing},\n number = {3}\n}\n\n Lack of suitable substitute assistive technology is a roadblock for students and scientists who are blind or visually impaired (BVI) from advancing in careers in science, technology, engineering, and mathematics (STEM) fields. It is challenging for persons who are BVI to interpret real-time visual scientific data which is commonly generated during lab experimentation, such as performing light microscopy, spectrometry, and observing chemical reactions. To address this problem, a real-time multimodal image perception system was developed to allow standard laboratory blood smear images to be perceived by BVI individuals by employing a combination of auditory, haptic, and vibrotactile feedback. These sensory feedback modalities were used to convey visual information through alternative perceptual channels, thus creating a palette of multimodal, sensory information. Two sets of image features of interest (primary and peripheral features) were applied to characterize images. A Bayesian network was applied to construct causal relations between these two groups of features. In order to match primary features with sensor modalities, two methods were conceived. Experimental results confirmed that this real-time approach produced higher accuracy in recognizing and analyzing objects within images compared to conventional tactile images.\n
\n\n\n\n \n\n \n \n \n \n \n \n An augmented reality-based approach for surgical telementoring in austere environments.\n \n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M., M., E.; Shanghavi, A.; Mullis, B.; Marley, S.; Gomez, G.; and Wachs, J., J., P.\n\n\n \n\n\n\n Military Medicine, 182(S1): 310-315. 3 2017.\n \n\n\n\n
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\n\n\n@article{\n title = {An augmented reality-based approach for surgical telementoring in austere environments},\n type = {article},\n year = {2017},\n pages = {310-315},\n volume = {182},\n websites = {http://militarymedicine.amsus.org/doi/10.7205/MILMED-D-16-00051},\n month = {3},\n id = {c780f279-2a41-3a2a-b4c6-83f0cc41bd6b},\n created = {2021-06-04T19:36:50.270Z},\n accessed = {2017-07-28},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.548Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Telementoring can improve treatment of combat trauma injuries by connecting remote experienced surgeons with local less-experienced surgeons in an austere environment. Current surgical telementoring systems force the local surgeon to regularly shift focus away from the operating field to receive expert guidance, which can lead to surgery delays or even errors. The System for Telementoring with Augmented Reality (STAR) integrates expert-created annotations directly into the local surgeon’s field of view. The local surgeon views the operating field by looking at a tablet display suspended between the patient and the surgeon that captures video of the surgical field. The remote surgeon remotely adds graphical annotations to the video. The annotations are sent back and displayed to the local surgeon while being automatically anchored to the operating field elements they describe. A technical evaluation demonstrates that STAR robustly anchors annotations despite tablet repositioning and occlusions. In a user study, participants used either STAR or a conventional telementoring system to precisely mark locations on a surgical simulator under a remote surgeon’s guidance. Participants who used STAR completed the task with fewer focus shifts and with greater accuracy. The STAR reduces the local surgeon’s need to shift attention during surgery, allowing him or her to continuously work while looking “through” the tablet screen.},\n bibtype = {article},\n author = {Andersen, Dan and Popescu, Voicu and Cabrera, M.E. Maria Eugenia and Shanghavi, Aditya and Mullis, Brian and Marley, Sherri and Gomez, Gerardo and Wachs, J.P. Juan P.},\n doi = {10.7205/MILMED-D-16-00051},\n journal = {Military Medicine},\n number = {S1}\n}\n\n Telementoring can improve treatment of combat trauma injuries by connecting remote experienced surgeons with local less-experienced surgeons in an austere environment. Current surgical telementoring systems force the local surgeon to regularly shift focus away from the operating field to receive expert guidance, which can lead to surgery delays or even errors. The System for Telementoring with Augmented Reality (STAR) integrates expert-created annotations directly into the local surgeon’s field of view. The local surgeon views the operating field by looking at a tablet display suspended between the patient and the surgeon that captures video of the surgical field. The remote surgeon remotely adds graphical annotations to the video. The annotations are sent back and displayed to the local surgeon while being automatically anchored to the operating field elements they describe. A technical evaluation demonstrates that STAR robustly anchors annotations despite tablet repositioning and occlusions. In a user study, participants used either STAR or a conventional telementoring system to precisely mark locations on a surgical simulator under a remote surgeon’s guidance. Participants who used STAR completed the task with fewer focus shifts and with greater accuracy. The STAR reduces the local surgeon’s need to shift attention during surgery, allowing him or her to continuously work while looking “through” the tablet screen.\n
\n\n\n\n \n\n \n \n \n \n \n \n What Makes a Gesture a Gesture? Neural Signatures Involved in Gesture Recognition.\n \n \n \n \n\n\n \n Cabrera, M., M., E.; Novak, K.; Foti, D.; Voyles, R.; and Wachs, J., J., P.\n\n\n \n\n\n\n In 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pages 748-753, 5 2017. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {What Makes a Gesture a Gesture? Neural Signatures Involved in Gesture Recognition},\n type = {inproceedings},\n year = {2017},\n pages = {748-753},\n websites = {http://ieeexplore.ieee.org/document/7961816/},\n month = {5},\n publisher = {IEEE},\n city = {Washington, DC, DC, USA},\n id = {9b6663a8-19eb-3c73-aa5d-e31f089d5ce5},\n created = {2021-06-04T19:36:50.432Z},\n accessed = {2018-11-09},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.717Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2017f},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Previous work in the area of gesture production, has made the assumption that machines can replicate humanlike gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that claim using electroencephalography (EEG). That is, this paper attempts to find neural signatures of gestures (also referred as placeholders) in human cognition, which facilitate the understanding, learning and repetition of gestures. Further, it is discussed whether there is a direct mapping between the placeholders and kinematic salient points in the gesture trajectories. These are expressed as relationships between inflection points in the gestures trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved by correlating fluctuations in mu power during gesture observation with salient motion points found for each gesture. Peaks in the EEG signal at central electrodes (motor cortex; C3/Cz/C4) and occipital electrodes (visual cortex; O3/Oz/O4) were used to isolate the salient events within each gesture. We found that a linear model predicting mu peaks from motion inflections fits the data well. Increases in EEG power were detected 380 and 500ms after inflection points at occipital and central electrodes, respectively. These results suggest that coordinated activity in visual and motor cortices is sensitive to motion trajectories during gesture observation, and it is consistent with the proposal that inflection points operate as placeholders in gesture recognition.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. Maria E. and Novak, Keisha and Foti, Daniel and Voyles, Richard and Wachs, J.P. Juan P.},\n doi = {10.1109/FG.2017.93},\n booktitle = {2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)}\n}\n\n Previous work in the area of gesture production, has made the assumption that machines can replicate humanlike gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that claim using electroencephalography (EEG). That is, this paper attempts to find neural signatures of gestures (also referred as placeholders) in human cognition, which facilitate the understanding, learning and repetition of gestures. Further, it is discussed whether there is a direct mapping between the placeholders and kinematic salient points in the gesture trajectories. These are expressed as relationships between inflection points in the gestures trajectories with oscillatory mu rhythms (8-12 Hz) in the EEG. This is achieved by correlating fluctuations in mu power during gesture observation with salient motion points found for each gesture. Peaks in the EEG signal at central electrodes (motor cortex; C3/Cz/C4) and occipital electrodes (visual cortex; O3/Oz/O4) were used to isolate the salient events within each gesture. We found that a linear model predicting mu peaks from motion inflections fits the data well. Increases in EEG power were detected 380 and 500ms after inflection points at occipital and central electrodes, respectively. These results suggest that coordinated activity in visual and motor cortices is sensitive to motion trajectories during gesture observation, and it is consistent with the proposal that inflection points operate as placeholders in gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n One-Shot Gesture Recognition: One Step Towards Adaptive Learning.\n \n \n \n\n\n \n Cabrera, M., M., E.; Sanchez-Tamayo, N.; Voyles, R.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge, pages 784-789, 2017. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {One-Shot Gesture Recognition: One Step Towards Adaptive Learning},\n type = {inproceedings},\n year = {2017},\n pages = {784-789},\n id = {b9d12779-7b32-3b3f-b125-147a3abc473f},\n created = {2021-06-04T19:36:50.441Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.672Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2017d},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {User's intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures themselves, recently new approaches were proposed to deal with gesture perception and production as part of the recognition problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than treating the gestures as the outcomes of a stochastic process only. This is achieved by leveraging kinematic and cognitive aspects of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation, which in turn are used as training sets for state-of-the-art classifiers. Classification performance is evaluated in terms of recognition accuracy and coherency; the latter being a novel metric that determines the level of agreement between humans and machines. Specifically, the referred machines are robots which perform artificially generated examples. Coherency in recognition was determined at 93.8%, corresponding to a recognition accuracy of 89.2% for the classifiers and 92.5% for human participants. A proof of concept was performed towards the expansion of the proposed one shot learning approach to adaptive learning, and the results are presented and the implications discussed.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. Maria E. and Sanchez-Tamayo, Natalia and Voyles, Richard and Wachs, J.P. Juan P.},\n doi = {10.1109/FG.2017.98},\n booktitle = {Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge}\n}\n\n User's intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures themselves, recently new approaches were proposed to deal with gesture perception and production as part of the recognition problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than treating the gestures as the outcomes of a stochastic process only. This is achieved by leveraging kinematic and cognitive aspects of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation, which in turn are used as training sets for state-of-the-art classifiers. Classification performance is evaluated in terms of recognition accuracy and coherency; the latter being a novel metric that determines the level of agreement between humans and machines. Specifically, the referred machines are robots which perform artificially generated examples. Coherency in recognition was determined at 93.8%, corresponding to a recognition accuracy of 89.2% for the classifiers and 92.5% for human participants. A proof of concept was performed towards the expansion of the proposed one shot learning approach to adaptive learning, and the results are presented and the implications discussed.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Human-Centered Approach to One-Shot Gesture Learning.\n \n \n \n \n\n\n \n Cabrera, M., E.; and Wachs, J., P.\n\n\n \n\n\n\n Frontiers in Robotics and AI, 4(MAR): 1-18. 3 2017.\n \n\n\n\n
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\n\n\n@article{\n title = {A Human-Centered Approach to One-Shot Gesture Learning},\n type = {article},\n year = {2017},\n keywords = {Embodiment,Gesture recognition,Human-computer interaction,One-shot learning,Robotics},\n pages = {1-18},\n volume = {4},\n websites = {http://journal.frontiersin.org/article/10.3389/frobt.2017.00008,http://journal.frontiersin.org/article/10.3389/frobt.2017.00008/full},\n month = {3},\n day = {20},\n id = {76308aec-f46c-3f1c-887b-61b65cbdc03f},\n created = {2021-06-04T19:36:50.809Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.954Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cabrera2017g},\n folder_uuids = {252d62b8-fd23-412c-8a10-0feb91f68ed6,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This article discusses the problem of one-shot gesture recognition using a human-centered approach and its potential application to fields such as human-robot interaction where the user's intentions are indicated through spontaneous gesturing (one shot). Casual users have limited time to learn the gestures interface, which makes one-shot recognition an attractive alternative to interface customization. In the aim of natural interaction with machines, a framework must be developed to include the ability of humans to understand gestures from a single observation. Previous approaches to one-shot gesture recognition have relied heavily on statistical and data-mining-based solutions and have ignored the mechanisms that are used by humans to perceive and execute gestures and that can provide valuable context information. This omission has led to suboptimal solutions. The focus of this study is on the process that leads to the realization of a gesture, rather than on the gesture itself. In this case, context involves the way in which humans produce gestures-the kinematic and anthropometric characteristics. In the method presented here, the strategy is to generate a data set of realistic samples based on features extracted from a single gesture sample. These features, called the "gist of a gesture," are considered to represent what humans remember when seeing a gesture and, later, the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. The availability of a large data set of realistic samples allows the use of training classifiers for future recognition. The performance of the method is evaluated using different lexicons, and its efficiency is compared with that of traditional N-shot learning approaches. The strength of the approach is further illustrated through human and machine recognition of gestures performed by a dual-arm robotic platform.},\n bibtype = {article},\n author = {Cabrera, Maria Eugenia and Wachs, Juan Pablo},\n doi = {10.3389/frobt.2017.00008},\n journal = {Frontiers in Robotics and AI},\n number = {MAR}\n}\n\n This article discusses the problem of one-shot gesture recognition using a human-centered approach and its potential application to fields such as human-robot interaction where the user's intentions are indicated through spontaneous gesturing (one shot). Casual users have limited time to learn the gestures interface, which makes one-shot recognition an attractive alternative to interface customization. In the aim of natural interaction with machines, a framework must be developed to include the ability of humans to understand gestures from a single observation. Previous approaches to one-shot gesture recognition have relied heavily on statistical and data-mining-based solutions and have ignored the mechanisms that are used by humans to perceive and execute gestures and that can provide valuable context information. This omission has led to suboptimal solutions. The focus of this study is on the process that leads to the realization of a gesture, rather than on the gesture itself. In this case, context involves the way in which humans produce gestures-the kinematic and anthropometric characteristics. In the method presented here, the strategy is to generate a data set of realistic samples based on features extracted from a single gesture sample. These features, called the \"gist of a gesture,\" are considered to represent what humans remember when seeing a gesture and, later, the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. The availability of a large data set of realistic samples allows the use of training classifiers for future recognition. The performance of the method is evaluated using different lexicons, and its efficiency is compared with that of traditional N-shot learning approaches. The strength of the approach is further illustrated through human and machine recognition of gestures performed by a dual-arm robotic platform.\n
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\n \n \n\n \n \n \n \n \n \n Early Turn Taking Prediction in the Operating Room.\n \n \n \n \n\n\n \n Zhou, T.; and Wachs, J., P.\n\n\n \n\n\n\n In 2016 AAAI Fall Symposium Series, 2016. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Early Turn Taking Prediction in the Operating Room},\n type = {inproceedings},\n year = {2016},\n websites = {http://www.aaai.org/ocs/index.php/FSS/FSS16/paper/view/14074},\n id = {7b36f69e-73a5-3e7e-a539-4a024b938e57},\n created = {2017-01-29T21:36:02.000Z},\n accessed = {2016-12-13},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-17T19:13:08.261Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {zhou_early_2016},\n source_type = {inproceedings},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,128681a6-ba46-469d-8c4e-cb337bbf0f22,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Wachs, Juan P},\n booktitle = {2016 AAAI Fall Symposium Series}\n}\n\n \n\n \n \n \n \n \n Embodied gesture learning from one-shot.\n \n \n \n\n\n \n Cabrera, M.; and Wachs, J.\n\n\n \n\n\n\n In 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016, 2016. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Embodied gesture learning from one-shot},\n type = {inproceedings},\n year = {2016},\n id = {6177bcf0-73fc-3209-b0a0-70fd46b0c8e7},\n created = {2018-03-14T02:09:55.453Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.050Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Cabrera2016},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2016 IEEE. This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which humans produce the gestures - the kinematic and anthropometric characteristics and the users' proxemics (the use of the space around them). In the method presented, the strategy is to generate a dataset of realistic samples based on biomechanical features extracted from a single gesture sample. These features, called 'the gist of a gesture', are considered to represent what humans remember when seeing a gesture and the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. Having a large dataset of realistic samples enables training classifiers for future recognition. Three classifiers were trained and tested using a subset of ChaLearn dataset, resulting in all three classifiers showing rather similar performance around 80% recognition rate Our classification results show the feasibility and adaptability of the presented technique regardless of the classifier.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. and Wachs, J.P.},\n doi = {10.1109/ROMAN.2016.7745244},\n booktitle = {25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016}\n}\n\n © 2016 IEEE. This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which humans produce the gestures - the kinematic and anthropometric characteristics and the users' proxemics (the use of the space around them). In the method presented, the strategy is to generate a dataset of realistic samples based on biomechanical features extracted from a single gesture sample. These features, called 'the gist of a gesture', are considered to represent what humans remember when seeing a gesture and the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. Having a large dataset of realistic samples enables training classifiers for future recognition. Three classifiers were trained and tested using a subset of ChaLearn dataset, resulting in all three classifiers showing rather similar performance around 80% recognition rate Our classification results show the feasibility and adaptability of the presented technique regardless of the classifier.\n
\n\n\n\n \n\n \n \n \n \n \n The effect of embodied interaction in visual-spatial navigation.\n \n \n \n\n\n \n Zhang, T.; Li, Y.; and Wachs, J.\n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems, 7(1). 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {The effect of embodied interaction in visual-spatial navigation},\n type = {article},\n year = {2016},\n keywords = {Bayesian network,Embodied interaction,attention inference,gesture interaction,multimodal interaction},\n volume = {7},\n id = {5e0012cc-0f3d-3209-ba37-05620a774783},\n created = {2018-03-14T02:09:56.935Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.386Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Zhang2016},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.},\n bibtype = {article},\n author = {Zhang, T. and Li, Y.-T. and Wachs, J.P.},\n doi = {10.1145/2953887},\n journal = {ACM Transactions on Interactive Intelligent Systems},\n number = {1}\n}\n\n This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.\n
\n\n\n\n \n\n \n \n \n \n \n Medical telementoring using an augmented reality transparent display.\n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M.; Shanghavi, A.; Gomez, G.; Marley, S.; Mullis, B.; and Wachs, J.\n\n\n \n\n\n\n Surgery (United States), 159(6). 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {Medical telementoring using an augmented reality transparent display},\n type = {article},\n year = {2016},\n volume = {159},\n id = {4948756e-3899-31e9-be3e-8cc3a799cd74},\n created = {2018-03-14T02:09:57.304Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-07-06T18:38:49.088Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Andersen2016},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2016 Elsevier Inc. All rights reserved. Background The goal of this study was to design and implement a novel surgical telementoring system called the System for Telementoring with Augmented Reality (STAR) that uses a virtual transparent display to convey precise locations in the operating field to a trainee surgeon. This system was compared with a conventional system based on a telestrator for surgical instruction. Methods A telementoring system was developed and evaluated in a study which used a 1 × 2 between-subjects design with telementoring system, that is, STAR or conventional, as the independent variable. The participants in the study were 20 premedical or medical students who had no prior experience with telementoring. Each participant completed a task of port placement and a task of abdominal incision under telementoring using either the STAR or the conventional system. The metrics used to test performance when using the system were placement error, number of focus shifts, and time to task completion. Results When compared with the conventional system, participants using STAR completed the 2 tasks with less placement error (45% and 68%) and with fewer focus shifts (86% and 44%), but more slowly (19% for each task). Conclusions Using STAR resulted in decreased annotation placement error, fewer focus shifts, but greater times to task completion. STAR placed virtual annotations directly onto the trainee surgeon's field of view of the operating field by conveying location with great accuracy; this technology helped to avoid shifts in focus, decreased depth perception, and enabled fine-tuning execution of the task to match telementored instruction, but led to greater times to task completion.},\n bibtype = {article},\n author = {Andersen, D. and Popescu, V. and Cabrera, M.E. and Shanghavi, A. and Gomez, G. and Marley, S. and Mullis, B. and Wachs, J.P.},\n doi = {10.1016/j.surg.2015.12.016},\n journal = {Surgery (United States)},\n number = {6}\n}\n\n © 2016 Elsevier Inc. All rights reserved. Background The goal of this study was to design and implement a novel surgical telementoring system called the System for Telementoring with Augmented Reality (STAR) that uses a virtual transparent display to convey precise locations in the operating field to a trainee surgeon. This system was compared with a conventional system based on a telestrator for surgical instruction. Methods A telementoring system was developed and evaluated in a study which used a 1 × 2 between-subjects design with telementoring system, that is, STAR or conventional, as the independent variable. The participants in the study were 20 premedical or medical students who had no prior experience with telementoring. Each participant completed a task of port placement and a task of abdominal incision under telementoring using either the STAR or the conventional system. The metrics used to test performance when using the system were placement error, number of focus shifts, and time to task completion. Results When compared with the conventional system, participants using STAR completed the 2 tasks with less placement error (45% and 68%) and with fewer focus shifts (86% and 44%), but more slowly (19% for each task). Conclusions Using STAR resulted in decreased annotation placement error, fewer focus shifts, but greater times to task completion. STAR placed virtual annotations directly onto the trainee surgeon's field of view of the operating field by conveying location with great accuracy; this technology helped to avoid shifts in focus, decreased depth perception, and enabled fine-tuning execution of the task to match telementored instruction, but led to greater times to task completion.\n
\n\n\n\n \n\n \n \n \n \n \n A Comparative Study for Telerobotic Surgery Using Free Hand Gestures.\n \n \n \n\n\n \n Zhou, T.; Cabrera, M., E.; Low, T.; Sundaram, C.; and Wachs, J.\n\n\n \n\n\n\n Journal of Human-Robot Interaction, 5(2): 1. 2016.\n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A Comparative Study for Telerobotic Surgery Using Free Hand Gestures},\n type = {article},\n year = {2016},\n pages = {1},\n volume = {5},\n id = {31750cd0-4aa9-3c7f-bb57-35733c53f2ca},\n created = {2021-06-04T19:22:35.172Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.149Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This research presents an exploratory study among touch-based and touchless interfaces selected to teleoperate a highly dexterous surgical robot. The possibility of incorporating touchless interfaces into the surgical arena may provide surgeons with the ability to engage in telerobotic surgery similarly as if they were operating with their bare hands. On the other hand, precision and sensibility may be lost. To explore the advantages and drawbacks of these modalities, five interfaces were selected to send navigational commands to the Taurus robot in the system: Omega, Hydra, and a keyboard. The first represented touch-based, while Leap Motion and Kinect were selected as touchless interfaces. Three experimental designs were selected to test the system, based on standardized surgically related tasks and clinically relevant performance metrics measured to evaluate the user’s performance, learning rates, control stability, and interaction naturalness. The current work provides a benchmark and validation framework for the comparison of these two groups of interfaces and discusses their potential for current and future adoption in the surgical setting.},\n bibtype = {article},\n author = {Zhou, Tian and Cabrera, Maria Eugenia and Low, Thomas and Sundaram, Chandru and Wachs, Juan},\n doi = {10.5898/jhri.5.2.zhou},\n journal = {Journal of Human-Robot Interaction},\n number = {2}\n}\n\n This research presents an exploratory study among touch-based and touchless interfaces selected to teleoperate a highly dexterous surgical robot. The possibility of incorporating touchless interfaces into the surgical arena may provide surgeons with the ability to engage in telerobotic surgery similarly as if they were operating with their bare hands. On the other hand, precision and sensibility may be lost. To explore the advantages and drawbacks of these modalities, five interfaces were selected to send navigational commands to the Taurus robot in the system: Omega, Hydra, and a keyboard. The first represented touch-based, while Leap Motion and Kinect were selected as touchless interfaces. Three experimental designs were selected to test the system, based on standardized surgically related tasks and clinically relevant performance metrics measured to evaluate the user’s performance, learning rates, control stability, and interaction naturalness. The current work provides a benchmark and validation framework for the comparison of these two groups of interfaces and discusses their potential for current and future adoption in the surgical setting.\n
\n\n\n\n \n\n \n \n \n \n \n \n Embodied gesture learning from one-shot.\n \n \n \n \n\n\n \n Cabrera, M., M., E., M.; Wachs, J., J., J., P.; Cabrera, M.E, Wachs, J.; Cabrera, M., M., E., M.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n In 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016, pages 1092-1097, 2016. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Embodied gesture learning from one-shot},\n type = {inproceedings},\n year = {2016},\n pages = {1092-1097},\n websites = {http://ieeexplore.ieee.org/abstract/document/7745244/},\n publisher = {IEEE},\n id = {d98a2f6b-50ad-3b94-863b-515e5e8e690c},\n created = {2021-06-04T19:36:47.320Z},\n accessed = {2017-01-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.264Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {CabreraM.EWachs2016},\n folder_uuids = {252d62b8-fd23-412c-8a10-0feb91f68ed6,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which humans produce the gestures - the kinematic and anthropometric characteristics and the users' proxemics (the use of the space around them). In the method presented, the strategy is to generate a dataset of realistic samples based on biomechanical features extracted from a single gesture sample. These features, called 'the gist of a gesture', are considered to represent what humans remember when seeing a gesture and the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. Having a large dataset of realistic samples enables training classifiers for future recognition. Three classifiers were trained and tested using a subset of ChaLearn dataset, resulting in all three classifiers showing rather similar performance around 80% recognition rate Our classification results show the feasibility and adaptability of the presented technique regardless of the classifier.},\n bibtype = {inproceedings},\n author = {Cabrera, M.E. Maria E. ME and Wachs, J.P. JP Juan P. and Cabrera, M.E, Wachs, J. and Cabrera, M.E. Maria E. ME and Wachs, J.P. JP Juan P.},\n doi = {10.1109/ROMAN.2016.7745244},\n booktitle = {25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2016}\n}\n\n This paper discusses the problem of one shot gesture recognition. This is relevant to the field of human-robot interaction, where the user's intentions are indicated through spontaneous gesturing (one shot) to the robot. The novelty of this work consists of learning the process that leads to the creation of a gesture, rather on the gesture itself. In our case, the context involves the way in which humans produce the gestures - the kinematic and anthropometric characteristics and the users' proxemics (the use of the space around them). In the method presented, the strategy is to generate a dataset of realistic samples based on biomechanical features extracted from a single gesture sample. These features, called 'the gist of a gesture', are considered to represent what humans remember when seeing a gesture and the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. Having a large dataset of realistic samples enables training classifiers for future recognition. Three classifiers were trained and tested using a subset of ChaLearn dataset, resulting in all three classifiers showing rather similar performance around 80% recognition rate Our classification results show the feasibility and adaptability of the presented technique regardless of the classifier.\n
\n\n\n\n \n\n \n \n \n \n \n Introduction to Special Issue on Body Tracking and Healthcare.\n \n \n \n\n\n \n O’Hara, K.; Sellen, A.; Wachs, J.; O'Hara, K.; Sellen, A.; Wachs, J.; O’Hara, K.; Sellen, A.; and Wachs, J.\n\n\n \n\n\n\n Human-Computer Interaction, 31(3-4): 173-190. 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {Introduction to Special Issue on Body Tracking and Healthcare},\n type = {article},\n year = {2016},\n pages = {173-190},\n volume = {31},\n id = {9aadce40-09f8-3b84-b84e-1f0fd82e413e},\n created = {2021-06-04T19:36:47.368Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.372Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {OHara2016},\n source_type = {article},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The Special Issue on Body Tracking and Healthcare highlights the exciting possibilities that sensor technologies are opening up in health and well-being. From the assessment and monitoring of medical conditions, to new opportunities for rehabilitation, to innovations in interaction in the operating theatre, body-tracking technology makes possible a whole new world of applications and systems. One important aspect of this change is that advances in the availability, diversity, cost, robustness, weight, accuracy, and reliability of these sensing systems have meant that we are now seeing a move out of controlled, specialist laboratory settings into the real world.},\n bibtype = {article},\n author = {O’Hara, Kenton and Sellen, Abigail and Wachs, Juan and O'Hara, Kenton and Sellen, Abigail and Wachs, Juan and O’Hara, Kenton and Sellen, Abigail and Wachs, Juan},\n doi = {10.1080/07370024.2016.1151712},\n journal = {Human-Computer Interaction},\n number = {3-4}\n}\n\n The Special Issue on Body Tracking and Healthcare highlights the exciting possibilities that sensor technologies are opening up in health and well-being. From the assessment and monitoring of medical conditions, to new opportunities for rehabilitation, to innovations in interaction in the operating theatre, body-tracking technology makes possible a whole new world of applications and systems. One important aspect of this change is that advances in the availability, diversity, cost, robustness, weight, accuracy, and reliability of these sensing systems have meant that we are now seeing a move out of controlled, specialist laboratory settings into the real world.\n
\n\n\n\n \n\n \n \n \n \n \n Avoiding focus shifts in surgical telementoring using an augmented reality transparent display.\n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M., M., E.; Shnaghavi, A.; Gomez, G.; Marley, S.; Mullis, B.; Wachs, J.; Shanghavi, A.; Gomez, G.; Marley, S.; Mullis, B.; and Wachs, J.\n\n\n \n\n\n\n In Studies in Health Technology and Informatics, volume 220, pages 9-14, 2016. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Avoiding focus shifts in surgical telementoring using an augmented reality transparent display},\n type = {inproceedings},\n year = {2016},\n keywords = {Augmented reality,Telemedicine,Telementoring,Transparent displays},\n pages = {9-14},\n volume = {220},\n id = {0ddc2c0c-0071-3421-8e34-20c97376cd35},\n created = {2021-06-04T19:36:47.667Z},\n accessed = {2017-07-28},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.615Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Conventional surgical telementoring systems require the trainee to shift focus away from the operating field to a nearby monitor to receive mentor guidance. This paper presents the next generation of telementoring systems. Our system, STAR (System for Telementoring with Augmented Reality) avoids focus shifts by placing mentor annotations directly into the trainee's field of view using augmented reality transparent display technology. This prototype was tested with pre-medical and medical students. Experiments were conducted where participants were asked to identify precise operating field locations communicated to them using either STAR or a conventional telementoring system. STAR was shown to improve accuracy and to reduce focus shifts. The initial STAR prototype only provides an approximate transparent display effect, without visual continuity between the display and the surrounding area. The current version of our transparent display provides visual continuity by showing the geometry and color of the operating field from the trainee's viewpoint.},\n bibtype = {inproceedings},\n author = {Andersen, Daniel and Popescu, Voicu and Cabrera, M.E. Maria Eugenia and Shnaghavi, Aditya and Gomez, Gerardo and Marley, Sherri and Mullis, Brian and Wachs, Juan and Shanghavi, Aditya and Gomez, Gerardo and Marley, Sherri and Mullis, Brian and Wachs, Juan},\n doi = {10.3233/978-1-61499-625-5-9},\n booktitle = {Studies in Health Technology and Informatics}\n}\n\n Conventional surgical telementoring systems require the trainee to shift focus away from the operating field to a nearby monitor to receive mentor guidance. This paper presents the next generation of telementoring systems. Our system, STAR (System for Telementoring with Augmented Reality) avoids focus shifts by placing mentor annotations directly into the trainee's field of view using augmented reality transparent display technology. This prototype was tested with pre-medical and medical students. Experiments were conducted where participants were asked to identify precise operating field locations communicated to them using either STAR or a conventional telementoring system. STAR was shown to improve accuracy and to reduce focus shifts. The initial STAR prototype only provides an approximate transparent display effect, without visual continuity between the display and the surrounding area. The current version of our transparent display provides visual continuity by showing the geometry and color of the operating field from the trainee's viewpoint.\n
\n\n\n\n \n\n \n \n \n \n \n \n The effect of embodied interaction in visual-spatial navigation.\n \n \n \n \n\n\n \n Zhang, T.; Li, Y., Y., Y., T.; and Wachs, J., J., P., J., J., P.\n\n\n \n\n\n\n ACM Transactions on Interactive Intelligent Systems, 7(1): Forthcoming. 2016.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {The effect of embodied interaction in visual-spatial navigation},\n type = {article},\n year = {2016},\n keywords = {Bayesian network,Embodied interaction,attention inference,gesture interaction,multimodal interaction},\n pages = {Forthcoming},\n volume = {7},\n websites = {http://dl.acm.org/citation.cfm?id=2953887},\n id = {b10304dc-7d16-3251-9bb3-6e9ccb4e9eae},\n created = {2021-06-04T19:36:47.698Z},\n accessed = {2017-01-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.066Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhang2016b},\n folder_uuids = {85f08f20-a873-4450-a774-6d0c49753a48,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.},\n bibtype = {article},\n author = {Zhang, Ting and Li, Yu-Ting YT Yu Ting and Wachs, J.P. Juan P JP Juan P.},\n doi = {10.1145/2953887},\n journal = {ACM Transactions on Interactive Intelligent Systems},\n number = {1}\n}\n\n This article aims to assess the effect of embodied interaction on attention during the process of solving spatio-visual navigation problems. It presents a method that links operator's physical interaction, feedback, and attention. Attention is inferred through networks called Bayesian Attentional Networks (BANs). BANs are structures that describe cause-effect relationship between attention and physical action. Then, a utility function is used to determine the best combination of interaction modalities and feedback. Experiments involving five physical interaction modalities (vision-based gesture interaction, glove-based gesture interaction, speech, feet, and body stance) and two feedback modalities (visual and sound) are described. The main findings are: (i) physical expressions have an effect in the quality of the solutions to spatial navigation problems; (ii) the combination of feet gestures with visual feedback provides the best task performance.\n
\n\n\n\n \n\n \n \n \n \n \n \n Optimal Modality Selection for Cooperative Human-Robot Task Completion.\n \n \n \n \n\n\n \n Jacob, M., G., M.; and Wachs, J., J., P.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics, 46(12): 3388-3400. 12 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {Optimal Modality Selection for Cooperative Human-Robot Task Completion},\n type = {article},\n year = {2016},\n keywords = {Human-robot interaction (HRI),Pareto optimization,multimodal systems},\n pages = {3388-3400},\n volume = {46},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/26731783},\n month = {12},\n day = {25},\n id = {6c2314ca-e2b3-31b2-b823-45ba1f7055a5},\n created = {2021-06-04T19:36:48.352Z},\n accessed = {2016-05-06},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.423Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2015},\n folder_uuids = {17bc1bec-a75d-4612-93ed-2ce697382c4d,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Human-robot cooperation in complex environments must be fast, accurate, and resilient. This requires efficient communication channels where robots need to assimilate information using a plethora of verbal and nonverbal modalities such as hand gestures, speech, and gaze. However, even though hybrid human-robot communication frameworks and multimodal communication have been studied, a systematic methodology for designing multimodal interfaces does not exist. This paper addresses the gap by proposing a novel methodology to generate multimodal lexicons which maximizes multiple performance metrics over a wide range of communication modalities (i.e., lexicons). The metrics are obtained through a mixture of simulation and real-world experiments. The methodology is tested in a surgical setting where a robot cooperates with a surgeon to complete a mock abdominal incision and closure task by delivering surgical instruments. Experimental results show that predicted optimal lexicons significantly outperform predicted suboptimal lexicons (p < 0.05) in all metrics validating the predictability of the methodology. The methodology is validated in two scenarios (with and without modeling the risk of a human-robot collision) and the differences in the lexicons are analyzed.},\n bibtype = {article},\n author = {Jacob, Mithun George M.G. and Wachs, J.P. Juan P.},\n doi = {10.1109/TCYB.2015.2506985},\n journal = {IEEE Transactions on Cybernetics},\n number = {12}\n}\n\n Human-robot cooperation in complex environments must be fast, accurate, and resilient. This requires efficient communication channels where robots need to assimilate information using a plethora of verbal and nonverbal modalities such as hand gestures, speech, and gaze. However, even though hybrid human-robot communication frameworks and multimodal communication have been studied, a systematic methodology for designing multimodal interfaces does not exist. This paper addresses the gap by proposing a novel methodology to generate multimodal lexicons which maximizes multiple performance metrics over a wide range of communication modalities (i.e., lexicons). The metrics are obtained through a mixture of simulation and real-world experiments. The methodology is tested in a surgical setting where a robot cooperates with a surgeon to complete a mock abdominal incision and closure task by delivering surgical instruments. Experimental results show that predicted optimal lexicons significantly outperform predicted suboptimal lexicons (p < 0.05) in all metrics validating the predictability of the methodology. The methodology is validated in two scenarios (with and without modeling the risk of a human-robot collision) and the differences in the lexicons are analyzed.\n
\n\n\n\n \n\n \n \n \n \n \n \n Multi-target detection and tracking from a single camera in unmanned aerial vehicles (UAVs).\n \n \n \n \n\n\n \n Li, J., L.; Ye, D., D., H., D.; Chung, T.; Kolsch, M.; Wachs, J.; and Bouman, C.\n\n\n \n\n\n\n In IEEE International Conference on Intelligent Robots and Systems, volume 2016-Novem, pages 4992-4997, 2016. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Multi-target detection and tracking from a single camera in unmanned aerial vehicles (UAVs)},\n type = {inproceedings},\n year = {2016},\n pages = {4992-4997},\n volume = {2016-Novem},\n websites = {http://ieeexplore.ieee.org/abstract/document/7759733/},\n id = {bfb5fc6e-83b8-3725-8222-ba52037ec78f},\n created = {2021-06-04T19:36:48.460Z},\n accessed = {2017-03-09},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.502Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2016b},\n folder_uuids = {17bc1bec-a75d-4612-93ed-2ce697382c4d,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Despite the recent flight control regulations, Unmanned Aerial Vehicles (UAVs) are still gaining popularity in civilian and military applications, as much as for personal use. Such emerging interest is pushing the development of effective collision avoidance systems. Such systems play a critical role UAVs operations especially in a crowded airspace setting. Because of cost and weight limitations associated with UAVs payload, camera based technologies are the de-facto choice for collision avoidance navigation systems. This requires multitarget detection and tracking algorithms from a video, which can be run on board efficiently. While there has been a great deal of research on object detection and tracking from a stationary camera, few have attempted to detect and track small UAVs from a moving camera. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify distinctive points in the background subtracted image. We find spatio-temporal traits of each moving object through optical flow matching and then classify those candidate targets based on their motion patterns compared with the background. The performance is boosted through Kalman filter tracking. This results in temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results show that our algorithm can effectively detect and track small UAVs with limited computing resources.},\n bibtype = {inproceedings},\n author = {Li, Jing Li and Ye, DH Dong Hye D.H. and Chung, Timothy and Kolsch, Mathias and Wachs, Juan and Bouman, Charles},\n doi = {10.1109/IROS.2016.7759733},\n booktitle = {IEEE International Conference on Intelligent Robots and Systems}\n}\n\n Despite the recent flight control regulations, Unmanned Aerial Vehicles (UAVs) are still gaining popularity in civilian and military applications, as much as for personal use. Such emerging interest is pushing the development of effective collision avoidance systems. Such systems play a critical role UAVs operations especially in a crowded airspace setting. Because of cost and weight limitations associated with UAVs payload, camera based technologies are the de-facto choice for collision avoidance navigation systems. This requires multitarget detection and tracking algorithms from a video, which can be run on board efficiently. While there has been a great deal of research on object detection and tracking from a stationary camera, few have attempted to detect and track small UAVs from a moving camera. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify distinctive points in the background subtracted image. We find spatio-temporal traits of each moving object through optical flow matching and then classify those candidate targets based on their motion patterns compared with the background. The performance is boosted through Kalman filter tracking. This results in temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results show that our algorithm can effectively detect and track small UAVs with limited computing resources.\n
\n\n\n\n \n\n \n \n \n \n \n \n Medical telementoring using an augmented reality transparent display.\n \n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M., M., E.; Shanghavi, A.; Gomez, G.; Marley, S.; Mullis, B.; and Wachs, J., J., P.\n\n\n \n\n\n\n Surgery (United States), 159(6): 1646-1653. 1 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {Medical telementoring using an augmented reality transparent display},\n type = {article},\n year = {2016},\n pages = {1646-1653},\n volume = {159},\n websites = {http://www.surgjournal.com/article/S0039606015010545/fulltext},\n month = {1},\n publisher = {Elsevier},\n day = {21},\n id = {1e5cda70-93b1-3867-9f03-8cf5bb03ecef},\n created = {2021-06-04T19:36:48.754Z},\n accessed = {2016-01-26},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.846Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Andersen2016e},\n language = {English},\n folder_uuids = {0c3edeed-ac59-4b98-b750-f2079863a4e3,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {BACKGROUND: The goal of this study was to design and implement a novel surgical telementoring system called the System for Telementoring with Augmented Reality (STAR) that uses a virtual transparent display to convey precise locations in the operating field to a trainee surgeon. This system was compared with a conventional system based on a telestrator for surgical instruction. METHODS: A telementoring system was developed and evaluated in a study which used a 1 × 2 between-subjects design with telementoring system, that is, STAR or conventional, as the independent variable. The participants in the study were 20 premedical or medical students who had no prior experience with telementoring. Each participant completed a task of port placement and a task of abdominal incision under telementoring using either the STAR or the conventional system. The metrics used to test performance when using the system were placement error, number of focus shifts, and time to task completion. RESULTS: When compared with the conventional system, participants using STAR completed the 2 tasks with less placement error (45% and 68%) and with fewer focus shifts (86% and 44%), but more slowly (19% for each task). CONCLUSIONS: Using STAR resulted in decreased annotation placement error, fewer focus shifts, but greater times to task completion. STAR placed virtual annotations directly onto the trainee surgeon's field of view of the operating field by conveying location with great accuracy; this technology helped to avoid shifts in focus, decreased depth perception, and enabled fine-tuning execution of the task to match telementored instruction, but led to greater times to task completion.},\n bibtype = {article},\n author = {Andersen, Daniel and Popescu, Voicu and Cabrera, M.E. Maria Eugenia and Shanghavi, Aditya and Gomez, Gerardo and Marley, Sherri and Mullis, Brian and Wachs, J.P. Juan P.},\n doi = {10.1016/j.surg.2015.12.016},\n journal = {Surgery (United States)},\n number = {6}\n}\n\n BACKGROUND: The goal of this study was to design and implement a novel surgical telementoring system called the System for Telementoring with Augmented Reality (STAR) that uses a virtual transparent display to convey precise locations in the operating field to a trainee surgeon. This system was compared with a conventional system based on a telestrator for surgical instruction. METHODS: A telementoring system was developed and evaluated in a study which used a 1 × 2 between-subjects design with telementoring system, that is, STAR or conventional, as the independent variable. The participants in the study were 20 premedical or medical students who had no prior experience with telementoring. Each participant completed a task of port placement and a task of abdominal incision under telementoring using either the STAR or the conventional system. The metrics used to test performance when using the system were placement error, number of focus shifts, and time to task completion. RESULTS: When compared with the conventional system, participants using STAR completed the 2 tasks with less placement error (45% and 68%) and with fewer focus shifts (86% and 44%), but more slowly (19% for each task). CONCLUSIONS: Using STAR resulted in decreased annotation placement error, fewer focus shifts, but greater times to task completion. STAR placed virtual annotations directly onto the trainee surgeon's field of view of the operating field by conveying location with great accuracy; this technology helped to avoid shifts in focus, decreased depth perception, and enabled fine-tuning execution of the task to match telementored instruction, but led to greater times to task completion.\n
\n\n\n\n \n\n \n \n \n \n \n Special issue on real-time image and video processing for pattern recognition systems and applications.\n \n \n \n\n\n \n Wachs, J.; Mejail, M.; Fishbain, B.; and Alvarez, L.\n\n\n \n\n\n\n 2016.\n \n\n\n\n
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\n\n\n\n@misc{\n title = {Special issue on real-time image and video processing for pattern recognition systems and applications},\n type = {misc},\n year = {2016},\n source = {Journal of Real-Time Image Processing},\n pages = {247-249},\n volume = {11},\n issue = {2},\n id = {4830f9bf-1614-304b-9d28-a78f9e35ee09},\n created = {2021-06-04T19:36:48.773Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.751Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2016},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {misc},\n author = {Wachs, Juan and Mejail, Marta and Fishbain, Barak and Alvarez, Luis},\n doi = {10.1007/s11554-014-0452-8}\n}\n\n \n\n \n \n \n \n \n Enhanced control of a wheelchair-mounted robotic manipulator using 3-D vision and multimodal interaction.\n \n \n \n\n\n \n Jiang, H.; Zhang, T.; Wachs, J., J., P.; and Duerstock, B., B., S.\n\n\n \n\n\n\n Computer Vision and Image Understanding, 149: 21-31. 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {Enhanced control of a wheelchair-mounted robotic manipulator using 3-D vision and multimodal interaction},\n type = {article},\n year = {2016},\n keywords = {3D vision,Assistive robotics,Multi-modal interface,Wheelchair mounted robotic manipulator},\n pages = {21-31},\n volume = {149},\n id = {3ae55264-9105-3b08-b308-72cf7deb64fc},\n created = {2021-06-04T19:36:49.230Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.236Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2016},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper presents a multiple-sensors, 3D vision-based, autonomous wheelchair-mounted robotic manipulator (WMRM). Two 3D sensors were employed: one for object recognition, and the other for recognizing body parts (face and hands). The goal is to recognize everyday items and automatically interact with them in an assistive fashion. For example, when a cereal box is recognized, it is grasped, poured in a bowl, and brought to the user. Daily objects (i.e. bowl and hat) were automatically detected and classified using a three-steps procedure: (1) remove background based on 3D information and find the point cloud of each object; (2) extract feature vectors for each segmented object from its 3D point cloud and its color image; and (3) classify feature vectors as objects after applying a nonlinear support vector machine (SVM). To retrieve specific objects, three user interface methods were adopted: voice-based, gesture-based, and hybrid commands. The presented system was tested using two common activities of daily living — feeding and dressing. The results revealed that an accuracy of 98.96% is achieved for a dataset with twelve daily objects. The experimental results indicated that hybrid (gesture and speech) interaction outperforms any single modal interaction.},\n bibtype = {article},\n author = {Jiang, Hairong and Zhang, Ting and Wachs, J.P. Juan P. and Duerstock, B.S. Bradley S.},\n doi = {10.1016/j.cviu.2016.03.015},\n journal = {Computer Vision and Image Understanding}\n}\n\n This paper presents a multiple-sensors, 3D vision-based, autonomous wheelchair-mounted robotic manipulator (WMRM). Two 3D sensors were employed: one for object recognition, and the other for recognizing body parts (face and hands). The goal is to recognize everyday items and automatically interact with them in an assistive fashion. For example, when a cereal box is recognized, it is grasped, poured in a bowl, and brought to the user. Daily objects (i.e. bowl and hat) were automatically detected and classified using a three-steps procedure: (1) remove background based on 3D information and find the point cloud of each object; (2) extract feature vectors for each segmented object from its 3D point cloud and its color image; and (3) classify feature vectors as objects after applying a nonlinear support vector machine (SVM). To retrieve specific objects, three user interface methods were adopted: voice-based, gesture-based, and hybrid commands. The presented system was tested using two common activities of daily living — feeding and dressing. The results revealed that an accuracy of 98.96% is achieved for a dataset with twelve daily objects. The experimental results indicated that hybrid (gesture and speech) interaction outperforms any single modal interaction.\n
\n\n\n\n \n\n \n \n \n \n \n An optimized real-time hands gesture recognition based interface for individuals with upper-level spinal cord injuries.\n \n \n \n\n\n \n Jiang, H.; Wachs, J., J., P.; and Duerstock, B., B., S.\n\n\n \n\n\n\n Journal of Real-Time Image Processing, 11(2): 301-314. 6 2016.\n \n\n\n\n
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\n\n\n@article{\n title = {An optimized real-time hands gesture recognition based interface for individuals with upper-level spinal cord injuries},\n type = {article},\n year = {2016},\n keywords = {3D particle filter,CONDENSATION,Dynamic time warping (DTW),Gesture recognition,Neighborhood search},\n pages = {301-314},\n volume = {11},\n month = {6},\n id = {08d647a8-6b8e-3170-9f85-2ae0bf4df74d},\n created = {2021-06-04T19:36:49.360Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.410Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n language = {en},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,933326c4-c752-4ad2-ac23-72d7285e1dc0},\n private_publication = {false},\n abstract = {This paper presents a hand gesture-based interface to facilitate interaction with individuals with upper-level spinal cord injuries, and offers an alternative way to perform “hands-on” laboratory tasks. The presented system consists of four modules: hand detection, tracking, trajectory recognition, and actuated device control. A 3D particle filter framework based on color and depth information is proposed to provide a more efficient solution to the independent face and hands tracking problem. More specifically, an interaction model utilizing spatial and motion information was integrated into the particle filter framework to tackle the “false merge” and “false labeling” problem through hand interaction and occlusion. To obtain an optimal parameter set for the interaction model, a neighborhood search algorithm was employed. An accuracy of 98.81 % was achieved by applying the optimal parameter set to the tracking module of the system. Once the hands were tracked successfully, the acquired gesture trajectories were compared with motion models. The dynamic time warping method was used for signals’ time alignment, and they were classified by a CONDENSATION algorithm with a recognition accuracy of 97.5 %. In a validation experiment, the decoded gestures were passed as commands to a mobile service robot and a robotic arm to perform simulated laboratory tasks. Control policies using the gestural control were studied and optimal policies were selected to achieve optimal performance. The computational cost of each system module demonstrated a real-time performance.},\n bibtype = {article},\n author = {Jiang, Hairong and Wachs, J.P. Juan P. and Duerstock, B.S. Bradley S.},\n doi = {10.1007/s11554-013-0352-3},\n journal = {Journal of Real-Time Image Processing},\n number = {2}\n}\n\n This paper presents a hand gesture-based interface to facilitate interaction with individuals with upper-level spinal cord injuries, and offers an alternative way to perform “hands-on” laboratory tasks. The presented system consists of four modules: hand detection, tracking, trajectory recognition, and actuated device control. A 3D particle filter framework based on color and depth information is proposed to provide a more efficient solution to the independent face and hands tracking problem. More specifically, an interaction model utilizing spatial and motion information was integrated into the particle filter framework to tackle the “false merge” and “false labeling” problem through hand interaction and occlusion. To obtain an optimal parameter set for the interaction model, a neighborhood search algorithm was employed. An accuracy of 98.81 % was achieved by applying the optimal parameter set to the tracking module of the system. Once the hands were tracked successfully, the acquired gesture trajectories were compared with motion models. The dynamic time warping method was used for signals’ time alignment, and they were classified by a CONDENSATION algorithm with a recognition accuracy of 97.5 %. In a validation experiment, the decoded gestures were passed as commands to a mobile service robot and a robotic arm to perform simulated laboratory tasks. Control policies using the gestural control were studied and optimal policies were selected to achieve optimal performance. The computational cost of each system module demonstrated a real-time performance.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Hand-Held, Self-Contained Simulated Transparent Display.\n \n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Lin, C.; Cabrera, M., M., E.; Shanghavi, A.; and Wachs, J.\n\n\n \n\n\n\n In Adjunct Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2016, pages 96-101, 9 2016. IEEE\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A Hand-Held, Self-Contained Simulated Transparent Display},\n type = {inproceedings},\n year = {2016},\n keywords = {Simulated transparent smartphone,infrastructure for augmented reality applications,simulated transparent tablet},\n pages = {96-101},\n websites = {http://ieeexplore.ieee.org/document/7836470/},\n month = {9},\n publisher = {IEEE},\n institution = {IEEE},\n id = {7937a691-ee68-3238-a618-5f59e73d498d},\n created = {2021-06-04T19:36:49.441Z},\n accessed = {2017-07-28},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.533Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {andersen2016hand},\n source_type = {inproceedings},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Hand-held transparent displays are important infrastructure for augmented reality applications. Truly transparent displays are not yet feasible in hand-held form, and a promising alternative is to simulate transparency by displaying the image the user would see if the display were not there. Previous simulated transparent displays have important limitations, such as being tethered to auxiliary workstations, requiring the user to wear obtrusive head-tracking devices, or lacking the depth acquisition support that is needed for an accurate transparency effect for close-range scenes.We describe a general simulated transparent display and three prototype implementations (P1, P2, and P3), which take advantage of emerging mobile devices and accessories. P1 uses an off-theshelf smartphone with built-in head-tracking support; P1 is compact and suitable for outdoor scenes, providing an accurate transparency effect for scene distances greater than 6m. P2 uses a tablet with a built-in depth camera; P2 is compact and suitable for short-distance indoor scenes, but the user has to hold the display in a fixed position. P3 uses a conventional tablet enhanced with on-board depth acquisition and head tracking accessories; P3 compensates for user head motion and provides accurate transparency even for closerange scenes. The prototypes are hand-held and self-contained, without the need of auxiliary workstations for computation.},\n bibtype = {inproceedings},\n author = {Andersen, Daniel and Popescu, Voicu and Lin, Chengyuan and Cabrera, M.E. Maria Eugenia and Shanghavi, Aditya and Wachs, Juan},\n doi = {10.1109/ISMAR-Adjunct.2016.0049},\n booktitle = {Adjunct Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2016}\n}\n\n Hand-held transparent displays are important infrastructure for augmented reality applications. Truly transparent displays are not yet feasible in hand-held form, and a promising alternative is to simulate transparency by displaying the image the user would see if the display were not there. Previous simulated transparent displays have important limitations, such as being tethered to auxiliary workstations, requiring the user to wear obtrusive head-tracking devices, or lacking the depth acquisition support that is needed for an accurate transparency effect for close-range scenes.We describe a general simulated transparent display and three prototype implementations (P1, P2, and P3), which take advantage of emerging mobile devices and accessories. P1 uses an off-theshelf smartphone with built-in head-tracking support; P1 is compact and suitable for outdoor scenes, providing an accurate transparency effect for scene distances greater than 6m. P2 uses a tablet with a built-in depth camera; P2 is compact and suitable for short-distance indoor scenes, but the user has to hold the display in a fixed position. P3 uses a conventional tablet enhanced with on-board depth acquisition and head tracking accessories; P3 compensates for user head motion and provides accurate transparency even for closerange scenes. The prototypes are hand-held and self-contained, without the need of auxiliary workstations for computation.\n
\n\n\n\n \n\n \n \n \n \n \n \n Virtual annotations of the surgical field through an augmented reality transparent display.\n \n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M., M., E.; Shanghavi, A.; Gomez, G.; Marley, S.; Mullis, B.; and Wachs, J.\n\n\n \n\n\n\n The Visual Computer, 32(11): 1481-1498. 5 2016.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Virtual annotations of the surgical field through an augmented reality transparent display},\n type = {article},\n year = {2016},\n keywords = {Annotation anchoring,Augmented reality,Telemedicine,Telementoring},\n pages = {1481-1498},\n volume = {32},\n websites = {http://link.springer.com/10.1007/s00371-015-1135-6},\n month = {5},\n publisher = {Springer},\n day = {27},\n id = {ac6eeb1f-317a-3d3d-bca9-d3912eb890fa},\n created = {2021-06-04T19:36:49.706Z},\n accessed = {2015-05-29},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.812Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {andersen2016virtual},\n source_type = {article},\n folder_uuids = {0c3edeed-ac59-4b98-b750-f2079863a4e3,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Existing telestrator-based surgical telementoring systems require a trainee surgeon to shift focus frequently between the operating field and a nearby monitor to acquire and apply instructions from a remote mentor. We present a novel approach to surgical telementoring where annotations are superimposed directly onto the surgical field using an augmented reality (AR) simulated transparent display. We present our first steps towards realizing this vision, using two networked conventional tablets to allow a mentor to remotely annotate the operating field as seen by a trainee. Annotations are anchored to the surgical field as the trainee tablet moves and as the surgical field deforms or becomes occluded. The system is built exclusively from compact commodity-level components—all imaging and processing are performed on the two tablets.},\n bibtype = {article},\n author = {Andersen, Daniel and Popescu, Voicu and Cabrera, M.E. Maria Eugenia and Shanghavi, Aditya and Gomez, Gerardo and Marley, Sherri and Mullis, Brian and Wachs, Juan},\n doi = {10.1007/s00371-015-1135-6},\n journal = {The Visual Computer},\n number = {11}\n}\n\n Existing telestrator-based surgical telementoring systems require a trainee surgeon to shift focus frequently between the operating field and a nearby monitor to acquire and apply instructions from a remote mentor. We present a novel approach to surgical telementoring where annotations are superimposed directly onto the surgical field using an augmented reality (AR) simulated transparent display. We present our first steps towards realizing this vision, using two networked conventional tablets to allow a mentor to remotely annotate the operating field as seen by a trainee. Annotations are anchored to the surgical field as the trainee tablet moves and as the surgical field deforms or becomes occluded. The system is built exclusively from compact commodity-level components—all imaging and processing are performed on the two tablets.\n
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\n \n \n\n \n \n \n \n \n Touchless telerobotic surgery - Is it possible at all?.\n \n \n \n\n\n \n Zhou, T.; Cabrera, M., E.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings of the National Conference on Artificial Intelligence, volume 6, pages 4228-4229, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Touchless telerobotic surgery - Is it possible at all?},\n type = {inproceedings},\n year = {2015},\n pages = {4228-4229},\n volume = {6},\n id = {c815b243-b1d7-33bd-b61c-4606ce5c1ff3},\n created = {2018-03-14T02:09:55.917Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-08T13:46:01.362Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2015a},\n private_publication = {false},\n abstract = {This paper presents a comprehensive evaluation among touchless, vision-based hand tracking interfaces (Kinect and Leap Motion) and the feasibility of their adoption into the surgical theater compared to traditional interfaces.},\n bibtype = {inproceedings},\n author = {Zhou, Tian and Cabrera, Maria E. and Wachs, Juan P.},\n booktitle = {Proceedings of the National Conference on Artificial Intelligence}\n}\n\n This paper presents a comprehensive evaluation among touchless, vision-based hand tracking interfaces (Kinect and Leap Motion) and the feasibility of their adoption into the surgical theater compared to traditional interfaces.\n
\n\n\n\n \n\n \n \n \n \n \n A Comparative Study for Touchless Telerobotic Surgery.\n \n \n \n\n\n \n Zhou, T.; Cabrera, M.; and Wachs, J.\n\n\n \n\n\n\n 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@book{\n title = {A Comparative Study for Touchless Telerobotic Surgery},\n type = {book},\n year = {2015},\n source = {Computer-Assisted Musculoskeletal Surgery: Thinking and Executing in 3D},\n keywords = {Dexterous movement,Gaming technology,Robot assisted surgery,Touchless control scheme},\n id = {d5b41df8-72a3-3547-b7ef-fe4d3efafd48},\n created = {2018-03-14T02:09:55.969Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:23.575Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Zhou2015},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© Springer International Publishing Switzerland 2016. This chapter presents a comparative study among different interfaces used to teleoperate a robot to complete surgical tasks. The objective of this study is to assess the feasibility on touchless surgery and its drawbacks compared to its counterpart, touch based surgery. The five interfaces evaluated include both touch-based and touchless gaming technologies, such as Kinect, Hydra, Leap Motion, Omega 7 and a standard keyboard. The main motivation for selecting touchless controlling devices is based on direct use of the hands to perform surgical tasks without compromising the sterility required in operating rooms (OR); the trade-off when working with touchless interfaces is the loss of direct force-feedback. However, based on the paradigm of sensory substitution, feedback is provided in the form of sound and visual cues. The experiments conducted to evaluate the different interaction modalities involve two surgical tasks, namely incision and peg transfer. Both tasks were conducted using a teleoperated high dexterous robot. Experiment results revealed that in the incision task, touchless interfaces provide higher sense of control compared with their touch-based counterparts with statistical significance (p < 0.01). While maintaining a fixed depth during incision, Kinect and keyboard showed the least variance due to the discrete control protocol used. In the peg transfer experiment, the Omega controller led to shorter task completion times, while the fastest learning rate was found when using the Leap motion sensor.},\n bibtype = {book},\n author = {Zhou, T. and Cabrera, M.E. and Wachs, J.P.},\n doi = {10.1007/978-3-319-12943-3_17}\n}\n\n © Springer International Publishing Switzerland 2016. This chapter presents a comparative study among different interfaces used to teleoperate a robot to complete surgical tasks. The objective of this study is to assess the feasibility on touchless surgery and its drawbacks compared to its counterpart, touch based surgery. The five interfaces evaluated include both touch-based and touchless gaming technologies, such as Kinect, Hydra, Leap Motion, Omega 7 and a standard keyboard. The main motivation for selecting touchless controlling devices is based on direct use of the hands to perform surgical tasks without compromising the sterility required in operating rooms (OR); the trade-off when working with touchless interfaces is the loss of direct force-feedback. However, based on the paradigm of sensory substitution, feedback is provided in the form of sound and visual cues. The experiments conducted to evaluate the different interaction modalities involve two surgical tasks, namely incision and peg transfer. Both tasks were conducted using a teleoperated high dexterous robot. Experiment results revealed that in the incision task, touchless interfaces provide higher sense of control compared with their touch-based counterparts with statistical significance (p < 0.01). While maintaining a fixed depth during incision, Kinect and keyboard showed the least variance due to the discrete control protocol used. In the peg transfer experiment, the Omega controller led to shorter task completion times, while the fastest learning rate was found when using the Leap motion sensor.\n
\n\n\n\n \n\n \n \n \n \n \n A review and framework of laser-based collaboration support.\n \n \n \n\n\n \n Bechar, A.; Nof, S.; and Wachs, J.\n\n\n \n\n\n\n Annual Reviews in Control, 39. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A review and framework of laser-based collaboration support},\n type = {article},\n year = {2015},\n volume = {39},\n id = {9af9bbdb-8e27-3bb4-8a88-b2a1003e6969},\n created = {2018-03-14T02:09:56.183Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:13:03.072Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Bechar2015},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© 2015 Elsevier Ltd. New technologies are emerging to enable and support physical, implicit and explicit collaborations. They are essential for dealing with increasingly complex systems in unstructured, dynamic environments. The purpose of this article is to review the role of laser technology in enabling better, more precise interactions and their control, and to identify opportunities and challenges in this area. While the most common applications of laser technology are found in medical and health care, manufacturing, and communication, other domains such as safety, quality assurance, agriculture, construction, entertainment, defense, transportation, and law enforcement also benefit from it. In spite of the rapid dissemination of this technology, its role in support of collaboration and discovery is still in its infancy. Research activities concerning new ways of using lasers as a collaboration supporting technology that may strengthen new areas have been relatively limited. Nevertheless, the translation to this domain of collaboration support has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents (e.g., humans, machines, particles) and digital, possibly also photonic agents. Recent advances in laser technology in a number of application domains are reviewed in this article, focusing primarily on lasers' role for supporting different forms of precision interactions and collaboration. In addition, a framework with five collaboration support functions and five collaboration dimensions is defined for this review. The taxonomy framework is useful for enabling better understanding of the existing and emerging opportunities that laser-based technology offers for collaboration support, its advantages and several research gaps.},\n bibtype = {article},\n author = {Bechar, A. and Nof, S.Y. and Wachs, J.P.},\n doi = {10.1016/j.arcontrol.2015.03.003},\n journal = {Annual Reviews in Control}\n}\n\n © 2015 Elsevier Ltd. New technologies are emerging to enable and support physical, implicit and explicit collaborations. They are essential for dealing with increasingly complex systems in unstructured, dynamic environments. The purpose of this article is to review the role of laser technology in enabling better, more precise interactions and their control, and to identify opportunities and challenges in this area. While the most common applications of laser technology are found in medical and health care, manufacturing, and communication, other domains such as safety, quality assurance, agriculture, construction, entertainment, defense, transportation, and law enforcement also benefit from it. In spite of the rapid dissemination of this technology, its role in support of collaboration and discovery is still in its infancy. Research activities concerning new ways of using lasers as a collaboration supporting technology that may strengthen new areas have been relatively limited. Nevertheless, the translation to this domain of collaboration support has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents (e.g., humans, machines, particles) and digital, possibly also photonic agents. Recent advances in laser technology in a number of application domains are reviewed in this article, focusing primarily on lasers' role for supporting different forms of precision interactions and collaboration. In addition, a framework with five collaboration support functions and five collaboration dimensions is defined for this review. The taxonomy framework is useful for enabling better understanding of the existing and emerging opportunities that laser-based technology offers for collaboration support, its advantages and several research gaps.\n
\n\n\n\n \n\n \n \n \n \n \n \n Model-Based System Specification with Tesperanto: Readable Text from Formal Graphics.\n \n \n \n \n\n\n \n Blekhman, A.; Wachs, J., J., P.; and Dori, D.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(11): 1-1. 2015.\n \n\n\n\n
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\n\n\n@article{\n title = {Model-Based System Specification with Tesperanto: Readable Text from Formal Graphics},\n type = {article},\n year = {2015},\n keywords = {Analytical models,Computational modeling,Enterprise standards,Graphics,Mathematical model,Standards,Unified modeling language,medical treatment,modeling,object-process methodology (OPM),requirements,systems engineering (SE),technical documents},\n pages = {1-1},\n volume = {45},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7058446},\n id = {24ea50b9-8f62-3fcf-8bc4-77f2d56bc82a},\n created = {2021-06-04T19:36:48.084Z},\n accessed = {2015-09-21},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.081Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Blekhman2015b},\n short_title = {Systems, Man, and Cybernetics: Systems, IEEE Trans},\n folder_uuids = {1f3d793c-0c1b-418f-88e7-fd77440916d9,0c3edeed-ac59-4b98-b750-f2079863a4e3,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Technical reports and papers may be represented by a fundamental model, which can take the form of a block diagram, a state-machine, a flow diagram, or alternatively some ad hoc chart. This basic scheme can convey better the true value of otherwise verbose and potentially encumbered narrative-based specifications. We present a model-based methodology for authoring technical documents. The underlying idea is to first formalize the system to be specified using a conceptual model, and then automatically generate from the tested and verified model a humanly-readable text in a subset of English we call Tesperanto. This technical documents' authoring methodology is carried out in an integrated bimodal text-graphics document authoring environment. The methodology was evaluated with the International Organization for Standardization standards and a medical robotics case study. The evaluation resulted in tangible improvements in the quality and consistency of international standards. Further, it can serve to document complex dynamics among agents, such as interaction between an operation room technician robot and the surgeon, suggesting that it could be applied to represent and bring value to other types of technical documents.},\n bibtype = {article},\n author = {Blekhman, Alex and Wachs, J.P. Juan P. and Dori, Dov},\n doi = {10.1109/TSMC.2015.2406753},\n journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},\n number = {11}\n}\n\n Technical reports and papers may be represented by a fundamental model, which can take the form of a block diagram, a state-machine, a flow diagram, or alternatively some ad hoc chart. This basic scheme can convey better the true value of otherwise verbose and potentially encumbered narrative-based specifications. We present a model-based methodology for authoring technical documents. The underlying idea is to first formalize the system to be specified using a conceptual model, and then automatically generate from the tested and verified model a humanly-readable text in a subset of English we call Tesperanto. This technical documents' authoring methodology is carried out in an integrated bimodal text-graphics document authoring environment. The methodology was evaluated with the International Organization for Standardization standards and a medical robotics case study. The evaluation resulted in tangible improvements in the quality and consistency of international standards. Further, it can serve to document complex dynamics among agents, such as interaction between an operation room technician robot and the surgeon, suggesting that it could be applied to represent and bring value to other types of technical documents.\n
\n\n\n\n \n\n \n \n \n \n \n \n User-Centered and Analytic-Based Approaches to Generate Usable Gestures for Individuals With Quadriplegia.\n \n \n \n \n\n\n \n Jiang, H.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n IEEE Transactions on Human-Machine Systems, PP(3): 1-7. 2015.\n \n\n\n\n
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\n\n\n@article{\n title = {User-Centered and Analytic-Based Approaches to Generate Usable Gestures for Individuals With Quadriplegia},\n type = {article},\n year = {2015},\n keywords = {Assistive technologies,Feature extraction,Interviews,Laban space,Manifolds,Standards,Trajectory,Transforms,Yttrium,hand gesture-based interfaces,spinal cord injury (SCI)},\n pages = {1-7},\n volume = {PP},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7332939},\n id = {cea377aa-270d-347b-9551-c9c1b26dd556},\n created = {2021-06-04T19:36:48.592Z},\n accessed = {2015-12-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.671Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2015b},\n short_title = {Human-Machine Systems, IEEE Transactions on},\n folder_uuids = {252d62b8-fd23-412c-8a10-0feb91f68ed6,b43d1b86-b425-4322-b575-14547700e015,933326c4-c752-4ad2-ac23-72d7285e1dc0},\n private_publication = {false},\n abstract = {Hand gesture-based interfaces have become increasingly popular as a form to interact with computing devices. Unfortunately, standard gesture interfaces are not very usable by individuals with upper limb motor impairments, including quadriplegics due to spinal cord injury (SCI). The objective of this paper is to convert an existing interface to be usable by users with motor impairments. The key idea is to project existing patterns of gestural behavior to match those exhibited by users with quadriplegia due to common cervical SCIs. Two complementary approaches (a user-centered and an analytic approach) have been developed and validated to provide both subjective and quantitative solutions to interface design. The feasibility of the proposed methodology was validated through user-based experimental paradigms. Through this study, subjects with upper extremity motor impairments preferred (gave a significantly lower Borg scale) the use of alternative constrained gestures generated by the proposed approach rather than the standard gestures.},\n bibtype = {article},\n author = {Jiang, Hairong and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1109/THMS.2015.2497346},\n journal = {IEEE Transactions on Human-Machine Systems},\n number = {3}\n}\n\n Hand gesture-based interfaces have become increasingly popular as a form to interact with computing devices. Unfortunately, standard gesture interfaces are not very usable by individuals with upper limb motor impairments, including quadriplegics due to spinal cord injury (SCI). The objective of this paper is to convert an existing interface to be usable by users with motor impairments. The key idea is to project existing patterns of gestural behavior to match those exhibited by users with quadriplegia due to common cervical SCIs. Two complementary approaches (a user-centered and an analytic approach) have been developed and validated to provide both subjective and quantitative solutions to interface design. The feasibility of the proposed methodology was validated through user-based experimental paradigms. Through this study, subjects with upper extremity motor impairments preferred (gave a significantly lower Borg scale) the use of alternative constrained gestures generated by the proposed approach rather than the standard gestures.\n
\n\n\n\n \n\n \n \n \n \n \n Determining natural and accessible gestures using uncontrolled manifolds and cybernetics.\n \n \n \n\n\n \n Jiang, H.; Hsu, C., C., H.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n In IEEE International Conference on Intelligent Robots and Systems, volume 2015-Decem, pages 4078-4083, 2015. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Determining natural and accessible gestures using uncontrolled manifolds and cybernetics},\n type = {inproceedings},\n year = {2015},\n pages = {4078-4083},\n volume = {2015-Decem},\n id = {35f65567-312c-359b-8c02-76a10e1c1bf5},\n created = {2021-06-04T19:36:49.108Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.107Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2015},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Recent studies revealed that hand gesture-based interfaces can complement therapies for individuals with upper motor impairments and reduce the need of traditional rehabilitation sessions through hospital visits. Unfortunately, existing gesture-based interfaces have been developed without considering the physical limitations of users with motor impairments. An analytic approach was presented in our previous work to convert existing gesture-based interfaces designed for able-bodied individuals to be usable by individuals with quadriplegia using the Laban Theory of Movement. This paper extends the previous work by including gesture variability analysis (based on Uncontrolled Manifolds theory) and robotic execution. A WAM robotic arm was used to mimic gesture trajectories and a physical metric was empirically obtained to evaluate the physical effort of each gesture. At last, an integration method was presented to determine the accessible gesture set based on both the stability and empirical robot execution. For all the gesture classes, the accessible gestures were found to lie within 31% of the optimality of stability and work, respectively.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Hsu, C.-H. Chun Hao and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1109/IROS.2015.7353953},\n booktitle = {IEEE International Conference on Intelligent Robots and Systems}\n}\n\n Recent studies revealed that hand gesture-based interfaces can complement therapies for individuals with upper motor impairments and reduce the need of traditional rehabilitation sessions through hospital visits. Unfortunately, existing gesture-based interfaces have been developed without considering the physical limitations of users with motor impairments. An analytic approach was presented in our previous work to convert existing gesture-based interfaces designed for able-bodied individuals to be usable by individuals with quadriplegia using the Laban Theory of Movement. This paper extends the previous work by including gesture variability analysis (based on Uncontrolled Manifolds theory) and robotic execution. A WAM robotic arm was used to mimic gesture trajectories and a physical metric was empirically obtained to evaluate the physical effort of each gesture. At last, an integration method was presented to determine the accessible gesture set based on both the stability and empirical robot execution. For all the gesture classes, the accessible gestures were found to lie within 31% of the optimality of stability and work, respectively.\n
\n\n\n\n \n\n \n \n \n \n \n \n A User-Developed 3-D Hand Gesture Set for Human–Computer Interaction.\n \n \n \n \n\n\n \n Pereira, A.; Wachs, J., P.; Park, K.; and Rempel, D.\n\n\n \n\n\n\n Human Factors: The Journal of the Human Factors and Ergonomics Society, 57(4): 607-621. 6 2015.\n \n\n\n\n
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\n\n\n@article{\n title = {A User-Developed 3-D Hand Gesture Set for Human–Computer Interaction},\n type = {article},\n year = {2015},\n keywords = {HCI,fatigue,gesture,human-computer interaction,usability},\n pages = {607-621},\n volume = {57},\n websites = {http://hfs.sagepub.com/content/early/2014/11/21/0018720814559307.abstract,http://journals.sagepub.com/doi/10.1177/0018720814559307},\n month = {6},\n day = {24},\n id = {f0debf55-2777-37b1-ad42-492734475ba7},\n created = {2021-06-04T19:36:50.811Z},\n accessed = {2015-05-28},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-17T19:13:08.239Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Pereira2015a},\n private_publication = {false},\n abstract = {Objective: The purpose of this study was to develop a lexicon for 3-D hand gestures for common humancomputer interaction (HCI) tasks by considering usability and effort ratings. Background: Recent technologies create an opportunity for developing a free-form 3-D hand gesture lexicon for HCI. Method: Subjects (N = 30) with prior experience using 2-D gestures on touch screens performed 3-D gestures of their choice for 34 common HCI tasks and rated their gestures on preference, match, ease, and effort. Videos of the 1,300 generated gestures were analyzed for gesture popularity, order, and response times. Gesture hand postures were rated by the authors on biomechanical risk and fatigue. Results: A final task gesture set is proposed based primarily on subjective ratings and hand posture risk. The different dimensions used for evaluating task gestures were not highly correlated and, therefore, measured different properties of the taskgesture match. Application: A method is proposed for generating a user-developed 3-D gesture lexicon for common HCIs that involves subjective ratings and a posture risk rating for minimizing arm and hand fatigue.},\n bibtype = {article},\n author = {Pereira, Anna and Wachs, Juan P. and Park, Kunwoo and Rempel, David},\n doi = {10.1177/0018720814559307},\n journal = {Human Factors: The Journal of the Human Factors and Ergonomics Society},\n number = {4}\n}\n\n Objective: The purpose of this study was to develop a lexicon for 3-D hand gestures for common humancomputer interaction (HCI) tasks by considering usability and effort ratings. Background: Recent technologies create an opportunity for developing a free-form 3-D hand gesture lexicon for HCI. Method: Subjects (N = 30) with prior experience using 2-D gestures on touch screens performed 3-D gestures of their choice for 34 common HCI tasks and rated their gestures on preference, match, ease, and effort. Videos of the 1,300 generated gestures were analyzed for gesture popularity, order, and response times. Gesture hand postures were rated by the authors on biomechanical risk and fatigue. Results: A final task gesture set is proposed based primarily on subjective ratings and hand posture risk. The different dimensions used for evaluating task gestures were not highly correlated and, therefore, measured different properties of the taskgesture match. Application: A method is proposed for generating a user-developed 3-D gesture lexicon for common HCIs that involves subjective ratings and a posture risk rating for minimizing arm and hand fatigue.\n
\n\n\n\n \n\n \n \n \n \n \n A Transparent Display for Surgical Telementoring in Austere Environments.\n \n \n \n\n\n \n Andersen, D.; Popescu, V.; Cabrera, M., E.; Mullis, B.; Marley, S.; Gomez, G.; and Wachs, J., P.\n\n\n \n\n\n\n In Military Health System Research Symposium (MHSRS), 2015, 2015. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {A Transparent Display for Surgical Telementoring in Austere Environments},\n type = {inproceedings},\n year = {2015},\n id = {b3d8f0f5-075e-3b7d-b627-ca87af67e76b},\n created = {2021-06-04T19:36:51.143Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T19:37:20.415Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Andersen2015},\n folder_uuids = {0c3edeed-ac59-4b98-b750-f2079863a4e3,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Andersen, Daniel and Popescu, Voicu and Cabrera, Maria Eugenia and Mullis, Brian and Marley, Sherri and Gomez, Gerry and Wachs, Juan Pablo},\n booktitle = {Military Health System Research Symposium (MHSRS), 2015}\n}\n\n \n\n \n \n \n \n \n \n Effective and interactive interpretation of gestures by individuals with mobility impairments.\n \n \n \n \n\n\n \n Jiang, H.; Wachs, J., P.; and Duerstock, B., S.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@phdthesis{\n title = {Effective and interactive interpretation of gestures by individuals with mobility impairments},\n type = {phdthesis},\n year = {2015},\n source = {ProQuest Dissertations and Theses},\n keywords = {0464:Computer Engineering,0544:Electrical engineering,0546:Industrial engineering,Applied sciences,Assistive technology,Computer Engineering,Electrical engineering,Gesture-based interface,Industrial engineering,Laban movement analysis,Laban space,Uncontrolled manifold},\n pages = {168},\n websites = {https://login.ezproxy.uta.edu/login?url=https://search.proquest.com/docview/1842430646?accountid=7117%0Ahttps://uta.alma.exlibrisgroup.com/discovery/openurl?institution=01UTAR_INST&vid=01UTAR_INST:Services&rft.genre=dissertations+%26+theses&rft.title=Effe},\n id = {e894a694-3296-31f4-9047-0b351a9eaf8f},\n created = {2021-06-04T19:36:51.488Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.599Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {There has been an increasing attention in the adoption of commodity cameras and gaming based technology to support and complement rehabilitation therapies for users with upper extremity mobility impairments (UEMIs). Innovative applications leveraging on Kinect® and Wii® based technologies have been integrated in rehabilitation programs as part of a new paradigm often referred as exergaming – exercising while playing. These platforms involve the use of physical expressions, such as gestures, as the main form of interaction. Such platforms offer an alternative to traditional rehabilitation sessions, in order to improve rehabilitative outcomes and prevent rehospitalization. The problem is that such platforms rely on gesture interfaces, which are designed primarily for individuals without motor limitations, thus excluding a critical mass of users with some degree of UEMI who could benefit from this technology. The assistive technologies (AT) community has addressed this challenge through customizing hand gesture-based interfaces for specific quadriplegic users, which is tedious, time-consuming, and costly. There is no systematic method to convert existing gesture interfaces (designed for individuals without disabilities) to usable interfaces for persons with UEMIs. The objective of this research is to solve this hurdle by proposing a framework to establish guidelines, metrics, and procedures for the design of gesture sets (lexicons) suitable for users with UEMIs using fundamentally scientific sound principles. The key idea is to project the existing patterns of gestural behavior from persons without disabilities to match those exhibited by users with quadriplegia due to common cervical spinal cord injuries (SCIs). Two approaches (a user-centered and an analytic approach) have been developed and validated to provide users with quadriplegia with both individualized and universal solutions. The feasibility of the proposed methodology was validated through simulation and user-based experiments. Through these studies, it was found that subjects with UEMIs preferred gestures generated by our approach rather than the standard gestures (thirty-six out of forty-two constrained gestures). Gesture-variability analysis was conducted to further validate the gesture sets, and finally robotic execution was used to mimic gesture trajectories. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on the stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.},\n bibtype = {phdthesis},\n author = {Jiang, Hairong and Wachs, Juan P and Duerstock, Bradley S}\n}\n\n There has been an increasing attention in the adoption of commodity cameras and gaming based technology to support and complement rehabilitation therapies for users with upper extremity mobility impairments (UEMIs). Innovative applications leveraging on Kinect® and Wii® based technologies have been integrated in rehabilitation programs as part of a new paradigm often referred as exergaming – exercising while playing. These platforms involve the use of physical expressions, such as gestures, as the main form of interaction. Such platforms offer an alternative to traditional rehabilitation sessions, in order to improve rehabilitative outcomes and prevent rehospitalization. The problem is that such platforms rely on gesture interfaces, which are designed primarily for individuals without motor limitations, thus excluding a critical mass of users with some degree of UEMI who could benefit from this technology. The assistive technologies (AT) community has addressed this challenge through customizing hand gesture-based interfaces for specific quadriplegic users, which is tedious, time-consuming, and costly. There is no systematic method to convert existing gesture interfaces (designed for individuals without disabilities) to usable interfaces for persons with UEMIs. The objective of this research is to solve this hurdle by proposing a framework to establish guidelines, metrics, and procedures for the design of gesture sets (lexicons) suitable for users with UEMIs using fundamentally scientific sound principles. The key idea is to project the existing patterns of gestural behavior from persons without disabilities to match those exhibited by users with quadriplegia due to common cervical spinal cord injuries (SCIs). Two approaches (a user-centered and an analytic approach) have been developed and validated to provide users with quadriplegia with both individualized and universal solutions. The feasibility of the proposed methodology was validated through simulation and user-based experiments. Through these studies, it was found that subjects with UEMIs preferred gestures generated by our approach rather than the standard gestures (thirty-six out of forty-two constrained gestures). Gesture-variability analysis was conducted to further validate the gesture sets, and finally robotic execution was used to mimic gesture trajectories. Based on this, a physical metric (referred as work) was empirically obtained to compare the physical effort of each gesture. An integration method was presented to determine the accessible gesture set based on the stability and empirical robot execution. For all the gesture types, the accessible gestures were found to lie within 34% of the optimality of stability and work. Lastly, the gesture set determined by the proposed methodology was practically evaluated by target users in experiments while solving a spatial navigational problem.\n
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\n \n \n\n \n \n \n \n \n \n A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments.\n \n \n \n \n\n\n \n Jiang, H.; Duerstock, B.; and Wachs, J.\n\n\n \n\n\n\n IEEE transactions on systems, man, and cybernetics., 44(5): 630-641. 2014.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments},\n type = {article},\n year = {2014},\n pages = {630-641},\n volume = {44},\n websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6576857},\n id = {9586f165-b710-3d26-9b02-c8f2819796ea},\n created = {2014-09-16T19:05:34.000Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.039Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2014e},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n bibtype = {article},\n author = {Jiang, H and Duerstock, BS and Wachs, JP},\n journal = {IEEE transactions on systems, man, and cybernetics.},\n number = {5}\n}\n\n \n\n \n \n \n \n \n Touchless telerobotic surgery–A Comparative Study.\n \n \n \n\n\n \n Zhou, T.; Cabrera, M.; and Wachs, J.\n\n\n \n\n\n\n In Workshop in Telerobotics for Real-Life Applications: Opportunities, Challenges, and New Developments. September 18, 2014 at IROS 2014, 2014. \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Touchless telerobotic surgery–A Comparative Study.},\n type = {inproceedings},\n year = {2014},\n id = {b4d03f10-160d-31ad-a4a9-5c58045b7650},\n created = {2015-02-01T22:03:28.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:56.695Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhou2014},\n folder_uuids = {1f3d793c-0c1b-418f-88e7-fd77440916d9,46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zhou, T and Cabrera, M and Wachs, J},\n booktitle = {Workshop in Telerobotics for Real-Life Applications: Opportunities, Challenges, and New Developments. September 18, 2014 at IROS 2014}\n}\n\n \n\n \n \n \n \n \n \n A Bayesian Approach to Determine Focus of Attention in Spatial and Time-Sensitive Decision Making Scenarios.\n \n \n \n \n\n\n \n Li, Y.; and Wachs, J., P.\n\n\n \n\n\n\n In AAAI'14 Workshop on Cognitive Computing for Augmented Human Intelligence. AAAI Conference on Artificial Intelligence (AAAI-14), 2014. \n \n\n\n\n
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Paper\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {A Bayesian Approach to Determine Focus of Attention in Spatial and Time-Sensitive Decision Making Scenarios.},\n type = {inproceedings},\n year = {2014},\n id = {fec72eb8-efcc-3ada-b0bd-0213136f0321},\n created = {2015-02-01T22:14:00.000Z},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-08T13:46:01.596Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2014f},\n folder_uuids = {1f3d793c-0c1b-418f-88e7-fd77440916d9,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {© Copyright 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Complex decision making scenarios require maintaining high level of concentration and acquiring knowledge about the context of the task in hand. Focus of attention is not only affected by contextual factors but also by the way operators interact with the information. Conversely, determining optimal ways to interact with this information can augment operators' cognition. However, challenges exist for determining efficient mathematical frameworks and sound metrics to infer, reason and assess the level of attention during spatio-temporal complex problem solving in hybrid human-machine systems. This paper proposes a computational framework based on a Bayesian approach (BAN) to infer users' focus of attention based on physical expression generated from embodied interaction and further support decision-making in an unobtrusive manner. Experiments involving five interaction modalities (vision- based gesture interaction, glove-based gesture interaction, speech, feet, and body balance) were conducted to assess the proposed framework's feasibility including the likelihood of assessed attention from enhanced BAN and task performance. Results confirm that physical expressions have a determining effect in the quality of the solutions in spatio-navigational type of problems.},\n bibtype = {inproceedings},\n author = {Li, Y.T. and Wachs, J. P},\n booktitle = {AAAI'14 Workshop on Cognitive Computing for Augmented Human Intelligence. AAAI Conference on Artificial Intelligence (AAAI-14)}\n}\n\n © Copyright 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Complex decision making scenarios require maintaining high level of concentration and acquiring knowledge about the context of the task in hand. Focus of attention is not only affected by contextual factors but also by the way operators interact with the information. Conversely, determining optimal ways to interact with this information can augment operators' cognition. However, challenges exist for determining efficient mathematical frameworks and sound metrics to infer, reason and assess the level of attention during spatio-temporal complex problem solving in hybrid human-machine systems. This paper proposes a computational framework based on a Bayesian approach (BAN) to infer users' focus of attention based on physical expression generated from embodied interaction and further support decision-making in an unobtrusive manner. Experiments involving five interaction modalities (vision- based gesture interaction, glove-based gesture interaction, speech, feet, and body balance) were conducted to assess the proposed framework's feasibility including the likelihood of assessed attention from enhanced BAN and task performance. Results confirm that physical expressions have a determining effect in the quality of the solutions in spatio-navigational type of problems.\n
\n\n\n\n \n\n \n \n \n \n \n Guest editorial - Special issue on robust recognition methods for multimodal interaction.\n \n \n \n\n\n \n Gomez, L.; Wachs, J.; and Jacobo-Berlles, J.\n\n\n \n\n\n\n Pattern Recognition Letters, 36(1). 2014.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Guest editorial - Special issue on robust recognition methods for multimodal interaction},\n type = {article},\n year = {2014},\n volume = {36},\n id = {078d6544-08bd-3b29-af95-70bef26fdb7b},\n created = {2018-03-14T02:09:56.037Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:58.534Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Gomez2014},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Gomez, L. and Wachs, J.P. and Jacobo-Berlles, J.},\n doi = {10.1016/j.patrec.2013.09.010},\n journal = {Pattern Recognition Letters},\n number = {1}\n}\n\n \n\n \n \n \n \n \n \n Touchless interaction in surgery.\n \n \n \n \n\n\n \n O'Hara, K.; Gonzalez, G.; Sellen, A.; Penney, G.; Varnavas, A.; Mentis, H.; Criminisi, A.; Corish, R.; Rouncefield, M.; Dastur, N.; Carrell, T.; Gonzalez, G.; Sellen, A.; Penney, G.; Varnavas, A.; Mentis, H.; Criminisi, A.; Corish, R.; and Rouncefield, M.\n\n\n \n\n\n\n Communications of the ACM, 57(1): 70-77. 1 2014.\n \n\n\n\n
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\n\n\n@article{\n title = {Touchless interaction in surgery},\n type = {article},\n year = {2014},\n pages = {70-77},\n volume = {57},\n websites = {http://dl.acm.org/citation.cfm?doid=2541883.2541899,http://dl.acm.org/citation.cfm?id=2541899},\n month = {1},\n id = {c03bc785-6549-34d6-8740-4bd393241bbb},\n created = {2021-06-04T19:36:47.143Z},\n accessed = {2017-09-13},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:10.839Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {OHara2014b},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58},\n private_publication = {false},\n abstract = {除证明非接触式交互系统的技术可行 性外，还应将外科手术中的这类系统进 行合理设计，让其能在手术室操作环境 内工作。 手势设计不仅应考虑与医学影像的个 体交互，还应考虑如何在协作讨论环境 下使用这些影像。 与单手和双手相关的手势设计应能满 足表达丰富度的要求，以及手术医生双 手操作的要求，同时还要受到了手术小 组成员靠近程度及无菌操作所产生的动 作限制的限制。},\n bibtype = {article},\n author = {O'Hara, Kenton and Gonzalez, Gerardo and Sellen, Abigail and Penney, Graeme and Varnavas, Andreas and Mentis, Helena and Criminisi, Antonio and Corish, Robert and Rouncefield, Mark and Dastur, Neville and Carrell, Tom and Gonzalez, Gerardo and Sellen, Abigail and Penney, Graeme and Varnavas, Andreas and Mentis, Helena and Criminisi, Antonio and Corish, Robert and Rouncefield, Mark},\n doi = {10.1145/2541883.2541899},\n journal = {Communications of the ACM},\n number = {1}\n}\n\n 除证明非接触式交互系统的技术可行 性外，还应将外科手术中的这类系统进 行合理设计，让其能在手术室操作环境 内工作。 手势设计不仅应考虑与医学影像的个 体交互，还应考虑如何在协作讨论环境 下使用这些影像。 与单手和双手相关的手势设计应能满 足表达丰富度的要求，以及手术医生双 手操作的要求，同时还要受到了手术小 组成员靠近程度及无菌操作所产生的动 作限制的限制。\n
\n\n\n\n \n\n \n \n \n \n \n \n Linking attention to physical action in complex decision making problems.\n \n \n \n \n\n\n \n Li, Y., Y., Y., T.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 2014-Janua, pages 1241-1246, 10 2014. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Linking attention to physical action in complex decision making problems},\n type = {inproceedings},\n year = {2014},\n keywords = {Bayesian modeling,Embodied interaction,Hybrid human-machine system},\n pages = {1241-1246},\n volume = {2014-Janua},\n issue = {January},\n websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6974084},\n month = {10},\n id = {8f767709-c307-314d-8d83-21160a0b90db},\n created = {2021-06-04T19:36:47.694Z},\n accessed = {2015-04-06},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.615Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2014c},\n folder_uuids = {85f08f20-a873-4450-a774-6d0c49753a48,b43d1b86-b425-4322-b575-14547700e015,ca95f434-f813-4373-a513-3f4b95ca15bf},\n private_publication = {false},\n abstract = {Embodied interaction concerns the way that user senses the environment, acquires information, and exhibits intention by means of physical action. In a complex decision making scenario, which requires maintaining high level of attention continuously and deep understanding about the task and its context, the use of embodied interaction has the potential to promote thinking and learning. Creating a framework that allows decision makers to interact with information using the whole body in intuitive ways may offer cognitive advantages and greater efficiency. This paper proposes such a computational framework based on a Bayesian approach (coined BAN) to infer operators' focus of attention based on the operators' physical expressions. Then, utility theory is adopted in order to determine the best combinations of interaction modalities and feedback. Experiments involving five physical interaction modalities (touchless, glove-based, and step gestures, speech, and body balance) and two feedback modalities (visual and sound) were conducted to assess the proposed framework's performance. This also includes the likelihood of assessed attention from enhanced BANs and task performance as a function of the interaction and control modalities. Results show that physical expressions have a determining factor in the quality of the solutions in spationavigational type of problems.},\n bibtype = {inproceedings},\n author = {Li, Yu-Ting YT Yu Ting and Wachs, J.P. JP Juan P.},\n doi = {10.1109/SMC.2014.6974084},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n Embodied interaction concerns the way that user senses the environment, acquires information, and exhibits intention by means of physical action. In a complex decision making scenario, which requires maintaining high level of attention continuously and deep understanding about the task and its context, the use of embodied interaction has the potential to promote thinking and learning. Creating a framework that allows decision makers to interact with information using the whole body in intuitive ways may offer cognitive advantages and greater efficiency. This paper proposes such a computational framework based on a Bayesian approach (coined BAN) to infer operators' focus of attention based on the operators' physical expressions. Then, utility theory is adopted in order to determine the best combinations of interaction modalities and feedback. Experiments involving five physical interaction modalities (touchless, glove-based, and step gestures, speech, and body balance) and two feedback modalities (visual and sound) were conducted to assess the proposed framework's performance. This also includes the likelihood of assessed attention from enhanced BANs and task performance as a function of the interaction and control modalities. Results show that physical expressions have a determining factor in the quality of the solutions in spationavigational type of problems.\n
\n\n\n\n \n\n \n \n \n \n \n \n HEGM: A hierarchical elastic graph matching for hand gesture recognition.\n \n \n \n \n\n\n \n Li, Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n Pattern Recognition, 47(1): 80-88. 1 2014.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {HEGM: A hierarchical elastic graph matching for hand gesture recognition},\n type = {article},\n year = {2014},\n keywords = {Elastic bunch graph,Feature extraction,Feature hierarchy,Graph matching,Hand gesture recognition},\n pages = {80-88},\n volume = {47},\n websites = {http://www.sciencedirect.com/science/article/pii/S0031320313002537},\n month = {1},\n id = {cb955429-e894-3ce4-a73d-aaf49ba4c03d},\n created = {2021-06-04T19:36:48.241Z},\n accessed = {2014-09-07},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.305Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2014g},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,891b5d9e-a304-468b-857c-8003112c8b0e,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods. © 2013 Elsevier Ltd. All rights reserved.},\n bibtype = {article},\n author = {Li, Yu-Ting Ting and Wachs, J.P. Juan P.},\n doi = {10.1016/j.patcog.2013.05.028},\n journal = {Pattern Recognition},\n number = {1}\n}\n\n A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods. © 2013 Elsevier Ltd. All rights reserved.\n
\n\n\n\n \n\n \n \n \n \n \n \n An augmented reality approach to surgical telementoring.\n \n \n \n \n\n\n \n Loescher, T.; Lee, S., Y., S., S.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 2014-Janua, pages 2341-2346, 2014. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {An augmented reality approach to surgical telementoring},\n type = {inproceedings},\n year = {2014},\n pages = {2341-2346},\n volume = {2014-Janua},\n issue = {January},\n websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6974276},\n id = {0062efd1-d937-3732-bde8-ef07d2830b5e},\n created = {2021-06-04T19:36:48.249Z},\n accessed = {2015-01-27},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.305Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Loescher2014a},\n folder_uuids = {ef47f247-b122-4733-9f1b-89eb5113ab66,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Optimal surgery and trauma treatment integrates different surgical skills frequently unavailable in rural/field hospitals. Telementoring can provide the missing expertise, but current systems require the trainee to focus on a nearby telestrator, fail to illustrate coming surgical steps, and give the mentor an incomplete picture of the ongoing surgery. A new telementoring system is presented that utilizes augmented reality to enhance the sense of co-presence. The system allows a mentor to add annotations to be displayed for a mentee during surgery. The annotations are displayed on a tablet held between the mentee and the surgical site as a heads-up display. As it moves, the system uses computer vision algorithms to track and align the annotations with the surgical region. Tracking is achieved through feature matching. To assess its performance, comparisons are made between SURF and SIFT detector, brute force and FLANN matchers, and hessian blob thresholds. The results show that the combination of a FLANN matcher and a SURF detector with a 1500 hessian threshold can optimize this system across scenarios of tablet movement and occlusion.},\n bibtype = {inproceedings},\n author = {Loescher, Timo and Lee, Shih Yu SY S.Y. and Wachs, J.P. JP Juan P.},\n doi = {10.1109/smc.2014.6974276},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n Optimal surgery and trauma treatment integrates different surgical skills frequently unavailable in rural/field hospitals. Telementoring can provide the missing expertise, but current systems require the trainee to focus on a nearby telestrator, fail to illustrate coming surgical steps, and give the mentor an incomplete picture of the ongoing surgery. A new telementoring system is presented that utilizes augmented reality to enhance the sense of co-presence. The system allows a mentor to add annotations to be displayed for a mentee during surgery. The annotations are displayed on a tablet held between the mentee and the surgical site as a heads-up display. As it moves, the system uses computer vision algorithms to track and align the annotations with the surgical region. Tracking is achieved through feature matching. To assess its performance, comparisons are made between SURF and SIFT detector, brute force and FLANN matchers, and hessian blob thresholds. The results show that the combination of a FLANN matcher and a SURF detector with a 1500 hessian threshold can optimize this system across scenarios of tablet movement and occlusion.\n
\n\n\n\n \n\n \n \n \n \n \n \n Operation room tool handling and miscommunication scenarios: An object-process methodology conceptual model.\n \n \n \n \n\n\n \n Wachs, J., J., J., P.; Frenkel, B.; and Dori, D.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 62(3): 153-163. 11 2014.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Operation room tool handling and miscommunication scenarios: An object-process methodology conceptual model},\n type = {article},\n year = {2014},\n keywords = {Concept formation,Conceptual modeling,Operative surgical procedures,Process model,Surgical robots},\n pages = {153-163},\n volume = {62},\n websites = {http://www.aiimjournal.com/article/S0933365714001092/fulltext,http://www.sciencedirect.com/science/article/pii/S0933365714001092},\n month = {11},\n publisher = {Elsevier},\n id = {ed01a4cc-39f0-3a8e-999d-f7487e1d369e},\n created = {2021-06-04T19:36:48.482Z},\n accessed = {2014-11-24},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.548Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2014c},\n language = {English},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,ef47f247-b122-4733-9f1b-89eb5113ab66,0c3edeed-ac59-4b98-b750-f2079863a4e3,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Objective: Errors in the delivery of medical care are the principal cause of inpatient mortality and morbidity, accounting for around 98,000 deaths in the United States of America (USA) annually. Ineffective team communication, especially in the operation room (OR), is a major root of these errors. This miscommunication can be reduced by analyzing and constructing a conceptual model of communication and miscommunication in the OR. We introduce the principles underlying Object-Process Methodology (OPM)-based modeling of the intricate interactions between the surgeon and the surgical technician while handling surgical instruments in the OR. This model is a software- and hardware-independent description of the agents engaged in communication events, their physical activities, and their interactions. The model enables assessing whether the task-related objectives of the surgical procedure were achieved and completed successfully and what errors can occur during the communication. Methods and material: The facts used to construct the model were gathered from observations of various types of operations miscommunications in the operating room and its outcomes. The model takes advantage of the compact ontology of OPM, which is comprised of stateful objects - things that exist physically or informatically, and processes - things that transform objects by creating them, consuming them or changing their state. The modeled communication modalities are verbal and non-verbal, and errors are modeled as processes that deviate from the "sunny day" scenario. Using OPM refinement mechanism of in-zooming, key processes are drilled into and elaborated, along with the objects that are required as agents or instruments, or objects that these processes transform. The model was developed through an iterative process of observation, modeling, group discussions, and simplification. Results: The model faithfully represents the processes related to tool handling that take place in an OR during an operation. The specification is at various levels of detail, each level is depicted in a separate diagram, and all the diagrams are "aware" of each other as part of the whole model. Providing ontology of verbal and non-verbal modalities of communication in the OR, the resulting conceptual model is a solid basis for analyzing and understanding the source of the large variety of errors occurring in the course of an operation, providing an opportunity to decrease the quantity and severity of mistakes related to the use and misuse of surgical instrumentations. Since the model is event driven, rather than person driven, the focus is on the factors causing the errors, rather than the specific person. This approach advocates searching for technological solutions to alleviate tool-related errors rather than finger-pointing. Concretely, the model was validated through a structured questionnaire and it was found that surgeons agreed that the conceptual model was flexible (3.8 of 5, std. =. 0.69), accurate, and it generalizable (3.7 of 5, std. =. 0.37 and 3.7 of 5, std. =. 0.85, respectively). Conclusion: The detailed conceptual model of the tools handling subsystem of the operation performed in an OR focuses on the details of the communication and the interactions taking place between the surgeon and the surgical technician during an operation, with the objective of pinpointing the exact circumstances in which errors can happen. Exact and concise specification of the communication events in general and the surgical instrument requests in particular is a prerequisite for a methodical analysis of the various modes of errors and the circumstances under which they occur. This has significant potential value in both reduction in tool-handling-related errors during an operation and providing a solid formal basis for designing a cybernetic agent which can replace a surgical technician in routine tool handling activities during an operation, freeing the technician to focus on quality assurance, monitoring and control of the cybernetic agent activities. This is a critical step in designing the next generation of cybernetic OR assistants.},\n bibtype = {article},\n author = {Wachs, J.P. JP Juan P. and Frenkel, Boaz and Dori, Dov},\n doi = {10.1016/j.artmed.2014.10.006},\n journal = {Artificial Intelligence in Medicine},\n number = {3}\n}\n\n Objective: Errors in the delivery of medical care are the principal cause of inpatient mortality and morbidity, accounting for around 98,000 deaths in the United States of America (USA) annually. Ineffective team communication, especially in the operation room (OR), is a major root of these errors. This miscommunication can be reduced by analyzing and constructing a conceptual model of communication and miscommunication in the OR. We introduce the principles underlying Object-Process Methodology (OPM)-based modeling of the intricate interactions between the surgeon and the surgical technician while handling surgical instruments in the OR. This model is a software- and hardware-independent description of the agents engaged in communication events, their physical activities, and their interactions. The model enables assessing whether the task-related objectives of the surgical procedure were achieved and completed successfully and what errors can occur during the communication. Methods and material: The facts used to construct the model were gathered from observations of various types of operations miscommunications in the operating room and its outcomes. The model takes advantage of the compact ontology of OPM, which is comprised of stateful objects - things that exist physically or informatically, and processes - things that transform objects by creating them, consuming them or changing their state. The modeled communication modalities are verbal and non-verbal, and errors are modeled as processes that deviate from the \"sunny day\" scenario. Using OPM refinement mechanism of in-zooming, key processes are drilled into and elaborated, along with the objects that are required as agents or instruments, or objects that these processes transform. The model was developed through an iterative process of observation, modeling, group discussions, and simplification. Results: The model faithfully represents the processes related to tool handling that take place in an OR during an operation. The specification is at various levels of detail, each level is depicted in a separate diagram, and all the diagrams are \"aware\" of each other as part of the whole model. Providing ontology of verbal and non-verbal modalities of communication in the OR, the resulting conceptual model is a solid basis for analyzing and understanding the source of the large variety of errors occurring in the course of an operation, providing an opportunity to decrease the quantity and severity of mistakes related to the use and misuse of surgical instrumentations. Since the model is event driven, rather than person driven, the focus is on the factors causing the errors, rather than the specific person. This approach advocates searching for technological solutions to alleviate tool-related errors rather than finger-pointing. Concretely, the model was validated through a structured questionnaire and it was found that surgeons agreed that the conceptual model was flexible (3.8 of 5, std. =. 0.69), accurate, and it generalizable (3.7 of 5, std. =. 0.37 and 3.7 of 5, std. =. 0.85, respectively). Conclusion: The detailed conceptual model of the tools handling subsystem of the operation performed in an OR focuses on the details of the communication and the interactions taking place between the surgeon and the surgical technician during an operation, with the objective of pinpointing the exact circumstances in which errors can happen. Exact and concise specification of the communication events in general and the surgical instrument requests in particular is a prerequisite for a methodical analysis of the various modes of errors and the circumstances under which they occur. This has significant potential value in both reduction in tool-handling-related errors during an operation and providing a solid formal basis for designing a cybernetic agent which can replace a surgical technician in routine tool handling activities during an operation, freeing the technician to focus on quality assurance, monitoring and control of the cybernetic agent activities. This is a critical step in designing the next generation of cybernetic OR assistants.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments.\n \n \n \n \n\n\n \n Jiang, H.; Duerstock, B., B., B., S.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5): 630-641. 5 2014.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments},\n type = {article},\n year = {2014},\n keywords = {Condensation,dynamic time warping,gesture recognition,one shot learning,particle filter},\n pages = {630-641},\n volume = {44},\n websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6576857},\n month = {5},\n id = {b0aad9e4-f5a0-34b3-94c8-3d6a79e76738},\n created = {2021-06-04T19:36:48.751Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.760Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2014e},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80,0e0d4081-4658-45de-ac20-4c61beb125e6,933326c4-c752-4ad2-ac23-72d7285e1dc0},\n private_publication = {false},\n abstract = {A machine vision-based gestural interface was developed to provide individuals with upper extremity physical impairments an alternative way to perform laboratory tasks that require physical manipulation of components. A color and depth based 3-D particle filter framework was constructed with unique descriptive features for face and hands representation. This framework was integrated into an interaction model utilizing spatial and motion information to deal efficiently with occlusions and its negative effects. More specifically, the suggested method proposed solves the false merging and false labeling problems characteristic in tracking through occlusion. The same feature encoding technique was subsequently used to detect, track and recognize users' hands. Experimental results demonstrated that the proposed approach was superior to other state-of-the-art tracking algorithms when interaction was present (97.52% accuracy). For gesture encoding, dynamic motion models were created employing the dynamic time warping method. The gestures were classified using a conditional density propagation-based trajectory recognition method. The hand trajectories were classified into different classes (commands) with a recognition accuracy of 95.9%. In addition, the new approach was validated with the 'one shot learning' paradigm with comparable results to those reported in 2012. In a validation experiment, the gestures were used to control a mobile service robot and a robotic arm in a laboratory chemistry experiment. Effective control policies were selected to achieve optimal performance for the presented gestural control system through comparison of task completion time between different control modes. © 2013 IEEE.},\n bibtype = {article},\n author = {Jiang, Hairong and Duerstock, BS B.S. Bradley S. and Wachs, J.P. JP Juan P.},\n doi = {10.1109/TSMC.2013.2270226},\n journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},\n number = {5}\n}\n\n A machine vision-based gestural interface was developed to provide individuals with upper extremity physical impairments an alternative way to perform laboratory tasks that require physical manipulation of components. A color and depth based 3-D particle filter framework was constructed with unique descriptive features for face and hands representation. This framework was integrated into an interaction model utilizing spatial and motion information to deal efficiently with occlusions and its negative effects. More specifically, the suggested method proposed solves the false merging and false labeling problems characteristic in tracking through occlusion. The same feature encoding technique was subsequently used to detect, track and recognize users' hands. Experimental results demonstrated that the proposed approach was superior to other state-of-the-art tracking algorithms when interaction was present (97.52% accuracy). For gesture encoding, dynamic motion models were created employing the dynamic time warping method. The gestures were classified using a conditional density propagation-based trajectory recognition method. The hand trajectories were classified into different classes (commands) with a recognition accuracy of 95.9%. In addition, the new approach was validated with the 'one shot learning' paradigm with comparable results to those reported in 2012. In a validation experiment, the gestures were used to control a mobile service robot and a robotic arm in a laboratory chemistry experiment. Effective control policies were selected to achieve optimal performance for the presented gestural control system through comparison of task completion time between different control modes. © 2013 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n Designing embodied and virtual agents for the operating room: Taking a closer look at multimodal medical-service robots and other cyber-physical systems.\n \n \n \n\n\n \n Wachs, J.\n\n\n \n\n\n\n Speech Technology and Text Mining in Medicine and Health Care, pages 107-133. 2014.\n \n\n\n\n
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\n\n\n@inbook{\n type = {inbook},\n year = {2014},\n pages = {107-133},\n id = {1fb01bd2-8e5b-3ee3-b65f-dd95785afa98},\n created = {2021-06-04T19:36:49.388Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-08T13:46:01.727Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2014a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Mistakes in the delivery of health care contribute significantly to patient mortality and morbidity, with an estimate of about 100,000 such cases per year. Some of these mistakes can be directly traced to a lack of effective communication among the surgical team. Studies of verbal and non-verbal communication in the operating theater found that miscommunications frequently occur. While there are other factors that lead to negative case outcomes, such as “team instability” in which teams of nurses and surgeons are not cohesive, or lack of minimal personnel, this chapter will focus specifically on those problems related to lack of communication. This problem is partially solved by the adoption of intelligent sensors along with automation and intuitive technologies in the operating room (OR) to assist surgical teams and improve patient safety. Three different kinds of cyber-physical agents are presented in this chapter. They consist of the Gestix and Gestonurse systems, which are used respectively to assist the main surgeon by displaying patient medical images and in the delivery of surgical instruments, and a telementoring agent that is used during the performance of surgical procedures so as to provide expert guidance to a surgeon in rural areas or in the battlefield.},\n bibtype = {inbook},\n author = {Wachs, J.P.},\n doi = {10.1515/9781614515159.107},\n chapter = {Designing embodied and virtual agents for the operating room: Taking a closer look at multimodal medical-service robots and other cyber-physical systems},\n title = {Speech Technology and Text Mining in Medicine and Health Care}\n}\n\n Mistakes in the delivery of health care contribute significantly to patient mortality and morbidity, with an estimate of about 100,000 such cases per year. Some of these mistakes can be directly traced to a lack of effective communication among the surgical team. Studies of verbal and non-verbal communication in the operating theater found that miscommunications frequently occur. While there are other factors that lead to negative case outcomes, such as “team instability” in which teams of nurses and surgeons are not cohesive, or lack of minimal personnel, this chapter will focus specifically on those problems related to lack of communication. This problem is partially solved by the adoption of intelligent sensors along with automation and intuitive technologies in the operating room (OR) to assist surgical teams and improve patient safety. Three different kinds of cyber-physical agents are presented in this chapter. They consist of the Gestix and Gestonurse systems, which are used respectively to assist the main surgeon by displaying patient medical images and in the delivery of surgical instruments, and a telementoring agent that is used during the performance of surgical procedures so as to provide expert guidance to a surgeon in rural areas or in the battlefield.\n
\n\n\n\n \n\n \n \n \n \n \n \n Optimal modality selection for multimodal human-machine systems using RIMAG.\n \n \n \n \n\n\n \n Jacob, M., G., M.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 2014-Janua, pages 2108-2113, 10 2014. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Optimal modality selection for multimodal human-machine systems using RIMAG},\n type = {inproceedings},\n year = {2014},\n keywords = {Computational modeling,Hidden Markov models,Human-Robot Interaction,Human-Robot interaction,Instruments,Measurement,Multimodal systems,Pareto optimization,RIMAG,Robots,Speech,Surgery,human-robot interaction,interpersonal communication,medical robotics,multimodal human-machine systems,multiobjective optimization,optimal lexicons,optimal modality selection,robot-human teams,robotic nurse,surgical instruments,surgical setting},\n pages = {2108-2113},\n volume = {2014-Janua},\n issue = {January},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6974233},\n month = {10},\n publisher = {IEEE},\n id = {b885e3b0-1391-301f-bc5b-08b8bd84cd01},\n created = {2021-06-04T19:36:49.521Z},\n accessed = {2015-01-27},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.557Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2014b},\n short_title = {Systems, Man and Cybernetics (SMC), 2014 IEEE Inte},\n folder_uuids = {ef47f247-b122-4733-9f1b-89eb5113ab66,46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {Interpersonal communication in human teams is multimodal by nature and hybrid robot-human teams should be capable of utilizing diverse verbal and non-verbal communication channels (e.g. gestures, speech, and gaze). Additionally, this interaction must fulfill requirements such as speed, accuracy and resilience. While multimodal communication has been researched and human-robot mixed team communication frameworks have been developed, the computation of an effective combination of communication modalities (multimodal lexicon) to maximize effectiveness is an untapped area of research. The proposed framework objectively determines the set of optimal lexicons through multiobjective optimization of performance metrics over all feasible lexicons. The methodology is applied to the surgical setting, where a robotic nurse can collaborate with a surgical team by delivering surgical instruments as required. In this time-sensitive, high-risk context, performance metrics are obtained through a mixture of real-world experiments and simulation. Experimental results validate the predictability of the method since predicted optimal lexicons significantly (p <; 0.01) outperform predicted suboptimal lexicons in time, error rate and false positive rates.},\n bibtype = {inproceedings},\n author = {Jacob, Mithun George M.G. and Wachs, J.P. Juan P.},\n doi = {10.1109/smc.2014.6974233},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n Interpersonal communication in human teams is multimodal by nature and hybrid robot-human teams should be capable of utilizing diverse verbal and non-verbal communication channels (e.g. gestures, speech, and gaze). Additionally, this interaction must fulfill requirements such as speed, accuracy and resilience. While multimodal communication has been researched and human-robot mixed team communication frameworks have been developed, the computation of an effective combination of communication modalities (multimodal lexicon) to maximize effectiveness is an untapped area of research. The proposed framework objectively determines the set of optimal lexicons through multiobjective optimization of performance metrics over all feasible lexicons. The methodology is applied to the surgical setting, where a robotic nurse can collaborate with a surgical team by delivering surgical instruments as required. In this time-sensitive, high-risk context, performance metrics are obtained through a mixture of real-world experiments and simulation. Experimental results validate the predictability of the method since predicted optimal lexicons significantly (p <; 0.01) outperform predicted suboptimal lexicons in time, error rate and false positive rates.\n
\n\n\n\n \n\n \n \n \n \n \n Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping.\n \n \n \n\n\n \n Zhong, H.; Wachs, J., J., P.; and Nof, S., S., Y.\n\n\n \n\n\n\n Computers in Industry, 65(4): 550-562. 2014.\n \n\n\n\n
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\n\n\n@article{\n title = {Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping},\n type = {article},\n year = {2014},\n keywords = {Collaborative design,Collaborative telerobotics,Conflict and error prevention,Intelligent insight visualization},\n pages = {550-562},\n volume = {65},\n id = {c82113df-a5d2-3ead-a1a4-cceb06feaa73},\n created = {2021-06-04T19:36:49.679Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.774Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhong2014},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The HUB-CI model is investigated in a telerobotic system in a client/server network to manage the lifecycle of engineering design and prototyping. The purpose of this platform is to support collaborative engineering design and proof of concept to enhance distributed team collaboration and resource utilization. The suggested platform is exemplified in two collaboration support tools and a physical prototyping platform. Structured Co-Insight Management is developed to support innovative idea exchanges and the consensus decision-making during the design process. Conflict/error detection management helps preventing conflicts and errors during the lifecycle of design and development. Physical collaboration over the network occurs when a team controls the telerobot operation during prototyping and testing in design cycles. A pilot system is implemented with a group project for the design of an electronic circuit (including both hardware and software designs). The functional assessment method is used to compare this platform to other collaborative design tools. The system presented offers unique qualitative advantages as an integrated collaboration support system. © 2014 Elsevier B.V.},\n bibtype = {article},\n author = {Zhong, Hao and Wachs, J.P. Juan P. and Nof, S.Y. Shimon Y.},\n doi = {10.1016/j.compind.2013.12.011},\n journal = {Computers in Industry},\n number = {4}\n}\n\n The HUB-CI model is investigated in a telerobotic system in a client/server network to manage the lifecycle of engineering design and prototyping. The purpose of this platform is to support collaborative engineering design and proof of concept to enhance distributed team collaboration and resource utilization. The suggested platform is exemplified in two collaboration support tools and a physical prototyping platform. Structured Co-Insight Management is developed to support innovative idea exchanges and the consensus decision-making during the design process. Conflict/error detection management helps preventing conflicts and errors during the lifecycle of design and development. Physical collaboration over the network occurs when a team controls the telerobot operation during prototyping and testing in design cycles. A pilot system is implemented with a group project for the design of an electronic circuit (including both hardware and software designs). The functional assessment method is used to compare this platform to other collaborative design tools. The system presented offers unique qualitative advantages as an integrated collaboration support system. © 2014 Elsevier B.V.\n
\n\n\n\n \n\n \n \n \n \n \n Integrated vision-based system for efficient, semi-automated control of a robotic manipulator.\n \n \n \n\n\n \n Jiang, H.; Wachs, J., J., P.; and Duerstock, B., B., S.\n\n\n \n\n\n\n International Journal of Intelligent Computing and Cybernetics, 7(3): 253-266. 2014.\n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Integrated vision-based system for efficient, semi-automated control of a robotic manipulator},\n type = {article},\n year = {2014},\n keywords = {Gesture recognition,Object recognition,Spinal cord injuries,Wheelchair-mounted robotic arm},\n pages = {253-266},\n volume = {7},\n id = {913aaf2f-6fb4-35bc-afce-5252f1277abb},\n created = {2021-06-04T19:36:49.862Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.985Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2014b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Purpose – The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/methodology/approach – Two Kinects cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator’s face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject’s face. Findings – The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects. Originality/value – Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.},\n bibtype = {article},\n author = {Jiang, Hairong and Wachs, J.P. Juan P. and Duerstock, B.S. Bradley S.},\n doi = {10.1108/IJICC-09-2013-0042},\n journal = {International Journal of Intelligent Computing and Cybernetics},\n number = {3}\n}\n\n Purpose – The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/methodology/approach – Two Kinects cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator’s face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject’s face. Findings – The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects. Originality/value – Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.\n
\n\n\n\n \n\n \n \n \n \n \n \n Multimodal approach to image perception of histology for the blind or visually impaired.\n \n \n \n \n\n\n \n Zhang, T.; Williams, G., G., J.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 2014-Janua, pages 3924-3929, 10 2014. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Multimodal approach to image perception of histology for the blind or visually impaired},\n type = {inproceedings},\n year = {2014},\n keywords = {BVI,Bayes methods,Bayesian network,Blind or visually impaired,Error analysis,Feature extraction,Haptics,Image color analysis,Image perception,Multi-modality,Sensorial substitution,Shape,Time factors,Vibrotactile,Viscosity,auditory feedbacks,belief networks,blind or visually impaired,blind or visually impaired people,force feedback,handicapped aids,haptic feedbacks,haptic interfaces,haptics,histology image perception,image characterization,image perception,lab blood smear image,multi-modality,multimodal information,real-time multimodal image perception system,sensorial information,sensorial substitution,sensory feedbacks,vibrotactile,vibrotactile feedbacks,visual information},\n pages = {3924-3929},\n volume = {2014-Janua},\n issue = {January},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6974544},\n month = {10},\n publisher = {IEEE},\n id = {992ae2f8-187e-3e7d-b6dc-711980cd8ac8},\n created = {2021-06-04T19:36:49.990Z},\n accessed = {2015-01-27},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.156Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhang2014a},\n short_title = {Systems, Man and Cybernetics (SMC), 2014 IEEE Inte},\n folder_uuids = {ef47f247-b122-4733-9f1b-89eb5113ab66,46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {Currently there is no suitable substitute technology to enable blind or visually impaired people (BVI) to interpret visual scientific data commonly generated during lab experimentation in real time, such as performing light microscopy, spectrometry, and observing chemical reactions. This reliance upon visual interpretation of scientific data certainly impedes BVIs from advancing in careers in medicine, biology and chemistry. To address this challenge, a real-time multimodal image perception system is developed to transform the standard lab blood smear image for persons with BVI to perceive, employing a combination of auditory, haptic, and vibrotactile feedbacks. These sensory feedbacks are used to convey visual information in appropriate perceptual channels, thus creating a palette of multimodal, sensorial information. A Bayesian network is developed to characterize images through two groups of features of interest: primary and peripheral features. Then, a method is conceived for optimal matching between primary features and sensory modalities. Experimental results confirmed this real-time approach of higher accuracy in recognizing and analyzing objects within images compared to tactile papers.},\n bibtype = {inproceedings},\n author = {Zhang, Ting and Williams, G.J. Greg J. and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1109/SMC.2014.6974544},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n Currently there is no suitable substitute technology to enable blind or visually impaired people (BVI) to interpret visual scientific data commonly generated during lab experimentation in real time, such as performing light microscopy, spectrometry, and observing chemical reactions. This reliance upon visual interpretation of scientific data certainly impedes BVIs from advancing in careers in medicine, biology and chemistry. To address this challenge, a real-time multimodal image perception system is developed to transform the standard lab blood smear image for persons with BVI to perceive, employing a combination of auditory, haptic, and vibrotactile feedbacks. These sensory feedbacks are used to convey visual information in appropriate perceptual channels, thus creating a palette of multimodal, sensorial information. A Bayesian network is developed to characterize images through two groups of features of interest: primary and peripheral features. Then, a method is conceived for optimal matching between primary features and sensory modalities. Experimental results confirmed this real-time approach of higher accuracy in recognizing and analyzing objects within images compared to tactile papers.\n
\n\n\n\n \n\n \n \n \n \n \n \n Context-based hand gesture recognition for the operating room.\n \n \n \n \n\n\n \n Jacob, M., G., M.; and Wachs, J., J., P.\n\n\n \n\n\n\n Pattern Recognition Letters, 36(1): 196-203. 1 2014.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Context-based hand gesture recognition for the operating room},\n type = {article},\n year = {2014},\n keywords = {Continuous gesture recognition,Human computer interaction,Operating room},\n pages = {196-203},\n volume = {36},\n websites = {http://www.sciencedirect.com/science/article/pii/S0167865513002225},\n month = {1},\n id = {d5491a28-6839-38de-b20d-0209310efb37},\n created = {2021-06-04T19:36:50.237Z},\n accessed = {2014-10-14},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.369Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2014c},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {A sterile, intuitive context-integrated system for navigating MRIs through freehand gestures during a neurobiopsy procedure is presented. Contextual cues are used to determine the intent of the user to improve continuous gesture recognition, and the discovery and exploration of MRIs. One of the challenges in gesture interaction in the operating room is to discriminate between intentional and non-intentional gestures. This problem is also referred as spotting. In this paper, a novel method for training gesture spotting networks is presented. The continuous gesture recognition system was shown to successfully detect gestures 92.26% of the time with a reliability of 89.97%. Experimental results show that significant improvements in task completion time were obtained through the effect of context integration. © 2013 Elsevier B.V. All rights reserved.},\n bibtype = {article},\n author = {Jacob, Mithun George M.G. and Wachs, J.P. Juan Pablo},\n doi = {10.1016/j.patrec.2013.05.024},\n journal = {Pattern Recognition Letters},\n number = {1}\n}\n\n A sterile, intuitive context-integrated system for navigating MRIs through freehand gestures during a neurobiopsy procedure is presented. Contextual cues are used to determine the intent of the user to improve continuous gesture recognition, and the discovery and exploration of MRIs. One of the challenges in gesture interaction in the operating room is to discriminate between intentional and non-intentional gestures. This problem is also referred as spotting. In this paper, a novel method for training gesture spotting networks is presented. The continuous gesture recognition system was shown to successfully detect gestures 92.26% of the time with a reliability of 89.97%. Experimental results show that significant improvements in task completion time were obtained through the effect of context integration. © 2013 Elsevier B.V. All rights reserved.\n
\n\n\n\n \n\n \n \n \n \n \n \n An analytic approach to decipher usable gestures for quadriplegic users.\n \n \n \n \n\n\n \n Jiang, H.; Duerstock, B., B., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 2014-Janua, pages 3912-3917, 10 2014. IEEE\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@inproceedings{\n title = {An analytic approach to decipher usable gestures for quadriplegic users},\n type = {inproceedings},\n year = {2014},\n keywords = {Assistive technology,Educational institutions,Feature extraction,HCI,Hand gesture-based interfaces,Kinematics,LMA theory,Laban movement analysis theory,Laban space,Mobility impairments,Standards,Trajectory,Transforms,Vectors,assisted living,assistive technology,feature vector,gaming technology,gestural behavior,gesture recognition,gesture trajectory,gesture-based control system,gesture-based gaming,hand gesture,hand gesture-based interfaces,handicapped aids,human computer interaction,interface customization,mobility impairments,mobility-related injury,patient rehabilitation,quadriplegic user,rehabilitation therapy,spinal cord injury,transform function,upper extremity mobility impairment,usable gestures,vectors},\n pages = {3912-3917},\n volume = {2014-Janua},\n issue = {January},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6974542},\n month = {10},\n publisher = {IEEE},\n id = {bae17fb0-6329-316b-906a-0f182ac26673},\n created = {2021-06-04T19:36:50.356Z},\n accessed = {2015-01-27},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.557Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2014d},\n short_title = {Systems, Man and Cybernetics (SMC), 2014 IEEE Inte},\n folder_uuids = {ef47f247-b122-4733-9f1b-89eb5113ab66,b43d1b86-b425-4322-b575-14547700e015,933326c4-c752-4ad2-ac23-72d7285e1dc0},\n private_publication = {false},\n abstract = {With the advent of new gaming technologies, hand gestures are gaining popularity as an effective communication channel for human computer interaction (HCI). This is particularly relevant for patients recovering from mobility-related injuries or debilitating conditions who use gesture-based gaming for rehabilitation therapy. Unfortunately, most gesture-based gaming systems are designed for able-bodied users and are difficult and costly to adapt to people with upper extremity mobility impairments. While interface customization is an active area of work in assistive technologies (AT), there is no existing formal and analytical grounded methodology to adapt gesture-based control systems for quadriplegics. The goal of this work is to solve this hurdle by developing a mathematical framework to project the patterns of gestural behavior designed for existing gesture systems to those exhibited by quadriplegic subjects due to spinal cord injury (SCI). A key component of our framework relied on Laban movement analysis (LMA) theory, and consisted of four steps: acquiring and preprocessing gesture trajectories, extracting feature vectors, training transform functions, and generating constrained gestures. The feasibility of this framework was validated through user-based experimental paradigms and subject validation. It was found that 100% of gestures selected by subjects with high-level SCIs came from the constrained gesture set. Even for the low-level quadriplegic subject, the alternative gestures were preferred.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Duerstock, B.S. Bradley S. and Wachs, J.P. Juan P.},\n doi = {10.1109/SMC.2014.6974542},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n With the advent of new gaming technologies, hand gestures are gaining popularity as an effective communication channel for human computer interaction (HCI). This is particularly relevant for patients recovering from mobility-related injuries or debilitating conditions who use gesture-based gaming for rehabilitation therapy. Unfortunately, most gesture-based gaming systems are designed for able-bodied users and are difficult and costly to adapt to people with upper extremity mobility impairments. While interface customization is an active area of work in assistive technologies (AT), there is no existing formal and analytical grounded methodology to adapt gesture-based control systems for quadriplegics. The goal of this work is to solve this hurdle by developing a mathematical framework to project the patterns of gestural behavior designed for existing gesture systems to those exhibited by quadriplegic subjects due to spinal cord injury (SCI). A key component of our framework relied on Laban movement analysis (LMA) theory, and consisted of four steps: acquiring and preprocessing gesture trajectories, extracting feature vectors, training transform functions, and generating constrained gestures. The feasibility of this framework was validated through user-based experimental paradigms and subject validation. It was found that 100% of gestures selected by subjects with high-level SCIs came from the constrained gesture set. Even for the low-level quadriplegic subject, the alternative gestures were preferred.\n
\n\n\n\n \n\n \n \n \n \n \n Precision collaboration and advanced integration using laser systems and techniques.\n \n \n \n\n\n \n Bechar, A.; Nof, S., Y.; and Wachs, J., P.\n\n\n \n\n\n\n Laser and Photonic Systems: Design and Integration, pages 165-200. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inbook{\n type = {inbook},\n year = {2014},\n pages = {165-200},\n id = {828c25c3-9865-32d4-a040-e6815301adb7},\n created = {2021-06-04T19:36:51.871Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.996Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Lasers were introduced five decades ago, and since then, they have impacted almost every aspect of our life, from healthcare to defense systems, by enabling a new ultraprecise, multipurpose technology. Laser technology has been implemented as an end product and as part of the production chain. This dual functionality is possible due to unique characteristics, such as high-rate energy transmission, high irradiance, and spatial and temporal coherence and precision. While the most common applications are found in medical and communication technologies, other areas such as manufacturing, agriculture, construction, and defense also benefit from this groundbreaking scientific discovery. In spite of the rapid dissemination of laser technologies to diverse and varied application fields, its role in support of collaboration and discovery is still in its infancy. Research activities brining laser-based technology to these areas have been relatively limited. Nevertheless, the translation to this domain has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents, including humans, machines, and digital, possibly photonic agents.},\n bibtype = {inbook},\n author = {Bechar, Avital and Nof, Shimon Y. and Wachs, Juan P.},\n doi = {10.1201/b16900},\n chapter = {Precision collaboration and advanced integration using laser systems and techniques},\n title = {Laser and Photonic Systems: Design and Integration}\n}\n\n Lasers were introduced five decades ago, and since then, they have impacted almost every aspect of our life, from healthcare to defense systems, by enabling a new ultraprecise, multipurpose technology. Laser technology has been implemented as an end product and as part of the production chain. This dual functionality is possible due to unique characteristics, such as high-rate energy transmission, high irradiance, and spatial and temporal coherence and precision. While the most common applications are found in medical and communication technologies, other areas such as manufacturing, agriculture, construction, and defense also benefit from this groundbreaking scientific discovery. In spite of the rapid dissemination of laser technologies to diverse and varied application fields, its role in support of collaboration and discovery is still in its infancy. Research activities brining laser-based technology to these areas have been relatively limited. Nevertheless, the translation to this domain has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents, including humans, machines, and digital, possibly photonic agents.\n
\n\n\n\n \n\n \n \n \n \n \n \n Autonomous Performance of Multistep Activities with a Wheelchair Mounted Robotic Manipulator Using Body Dependent Positioning.\n \n \n \n \n\n\n \n Jiang, H.; Zhang, T.; Wachs, J., P.; and Duerstock, B., S.\n\n\n \n\n\n\n In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems Workshop on Assistive Robotics for Individuals with Disabilities: HRI Issues and Beyond , 2014. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Autonomous Performance of Multistep Activities with a Wheelchair Mounted Robotic Manipulator Using Body Dependent Positioning},\n type = {inproceedings},\n year = {2014},\n websites = {https://pdfs.semanticscholar.org/e54d/bc1ba85c8004a85bc1f34525362f9ea07a52.pdf},\n id = {e5c3b368-b426-3ce2-ac5f-146b7a5b5dc1},\n created = {2021-06-04T19:36:51.994Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.141Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, an autonomous vision-based system was developed to control a wheelchair mounted robotic manipulator (WMRM). Two 3D cameras were applied for object and body part recognition (face and hands) of the wheelchair user. Two human robot interface modalities were used to control the WMRM: voice and gesture recognition. Daily objects were automatically recognized by employing a two-step process: 1) using Histogram of Oriented Gradients (HOG) algorithm to extract the feature vector for each detected object; 2) applying nonlinear support vector machine (SVM) algorithm to train the model and classify the objects. Four simulated tasks for daily objects delivery and retrieval were designed to test the validity of the proposed system. The results demonstrated that the automatic control requires significantly fewer time than the predefined control for phone calling and photography tasks (P = 0.015, P = 0.035), respectively. The gesture modality outperforms the voice control for the drinking and phone calling tasks (P = 0.016, P = 0.015), respectively.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Zhang, Ting and Wachs, Juan P. and Duerstock, Bradley S.},\n booktitle = {2014 IEEE/RSJ International Conference on Intelligent Robots and Systems Workshop on Assistive Robotics for Individuals with Disabilities: HRI Issues and Beyond }\n}\n\n In this paper, an autonomous vision-based system was developed to control a wheelchair mounted robotic manipulator (WMRM). Two 3D cameras were applied for object and body part recognition (face and hands) of the wheelchair user. Two human robot interface modalities were used to control the WMRM: voice and gesture recognition. Daily objects were automatically recognized by employing a two-step process: 1) using Histogram of Oriented Gradients (HOG) algorithm to extract the feature vector for each detected object; 2) applying nonlinear support vector machine (SVM) algorithm to train the model and classify the objects. Four simulated tasks for daily objects delivery and retrieval were designed to test the validity of the proposed system. The results demonstrated that the automatic control requires significantly fewer time than the predefined control for phone calling and photography tasks (P = 0.015, P = 0.035), respectively. The gesture modality outperforms the voice control for the drinking and phone calling tasks (P = 0.016, P = 0.015), respectively.\n
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\n \n \n\n \n \n \n \n \n \n A cyber-physical management system for delivering and monitoring surgical instruments in the OR.\n \n \n \n \n\n\n \n Li, Y.; Jacob, M.; Akingba, G.; and Wachs, J., P.\n\n\n \n\n\n\n Surgical innovation, 20(4): 377-84. 8 2013.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A cyber-physical management system for delivering and monitoring surgical instruments in the OR.},\n type = {article},\n year = {2013},\n keywords = {Cybernetics,Cybernetics: instrumentation,Cybernetics: methods,Equipment Design,Gestures,Humans,Operating Room Technicians,Operating Rooms,Pattern Recognition, Automated,Robotics,Robotics: instrumentation,Software,Surgery, Computer-Assisted,Surgery, Computer-Assisted: instrumentation,Surgery, Computer-Assisted: methods,Surgical Instruments},\n pages = {377-84},\n volume = {20},\n websites = {http://www.ncbi.nlm.nih.gov/pubmed/23037804},\n month = {8},\n id = {b55425d7-e139-3fb0-b777-80e415de1eb1},\n created = {2014-10-29T21:08:16.000Z},\n accessed = {2014-10-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:14.645Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2013c},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {BACKGROUND: The standard practice in the operating room (OR) is having a surgical technician deliver surgical instruments to the surgeon quickly and inexpensively, as required. This human "in the loop" system may result in mistakes (eg, missing information, ambiguity of instructions, and delays).\n\nOBJECTIVE: Errors can be reduced or eliminated by integrating information technology (IT) and cybernetics into the OR. Gesture and voice automatic acquisition, processing, and interpretation allow interaction with these new systems without disturbing the normal flow of surgery.\n\nMETHODS: This article describes the development of a cyber-physical management system (CPS), including a robotic scrub nurse, to support surgeons by passing surgical instruments during surgery as required and recording counts of surgical instruments into a personal health record (PHR). The robot used responds to hand signals and voice messages detected through sophisticated computer vision and data mining techniques.\n\nRESULTS: The CPS was tested during a mock surgery in the OR. The in situ experiment showed that the robot recognized hand gestures reliably (with an accuracy of 97%), it can retrieve instruments as close as 25 mm, and the total delivery time was less than 3 s on average.\n\nCONCLUSIONS: This online health tool allows the exchange of clinical and surgical information to electronic medical record-based and PHR-based applications among different hospitals, regardless of the style viewer. The CPS has the potential to be adopted in the OR to handle surgical instruments and track them in a safe and accurate manner, releasing the human scrub tech from these tasks.},\n bibtype = {article},\n author = {Li, Yu-Ting and Jacob, Mithun and Akingba, George and Wachs, Juan P},\n doi = {10.1177/1553350612459109},\n journal = {Surgical innovation},\n number = {4}\n}\n\n BACKGROUND: The standard practice in the operating room (OR) is having a surgical technician deliver surgical instruments to the surgeon quickly and inexpensively, as required. This human \"in the loop\" system may result in mistakes (eg, missing information, ambiguity of instructions, and delays).\n\nOBJECTIVE: Errors can be reduced or eliminated by integrating information technology (IT) and cybernetics into the OR. Gesture and voice automatic acquisition, processing, and interpretation allow interaction with these new systems without disturbing the normal flow of surgery.\n\nMETHODS: This article describes the development of a cyber-physical management system (CPS), including a robotic scrub nurse, to support surgeons by passing surgical instruments during surgery as required and recording counts of surgical instruments into a personal health record (PHR). The robot used responds to hand signals and voice messages detected through sophisticated computer vision and data mining techniques.\n\nRESULTS: The CPS was tested during a mock surgery in the OR. The in situ experiment showed that the robot recognized hand gestures reliably (with an accuracy of 97%), it can retrieve instruments as close as 25 mm, and the total delivery time was less than 3 s on average.\n\nCONCLUSIONS: This online health tool allows the exchange of clinical and surgical information to electronic medical record-based and PHR-based applications among different hospitals, regardless of the style viewer. The CPS has the potential to be adopted in the OR to handle surgical instruments and track them in a safe and accurate manner, releasing the human scrub tech from these tasks.\n
\n\n\n\n \n\n \n \n \n \n \n \n A collaborative telerobotics network framework with hand gesture interface and conflict prevention.\n \n \n \n \n\n\n \n Zhong, H.; Wachs, J., P.; and Nof, S., Y.\n\n\n \n\n\n\n International Journal of Production Research, 51(15): 4443-4463. 8 2013.\n \n\n\n\n
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\n\n\n@article{\n title = {A collaborative telerobotics network framework with hand gesture interface and conflict prevention},\n type = {article},\n year = {2013},\n keywords = {collaborative intelligence,collaborative telerobotics,computer vision,conflict and error prevention,gesture recognition},\n pages = {4443-4463},\n volume = {51},\n websites = {http://www.tandfonline.com/doi/abs/10.1080/00207543.2012.756591},\n month = {8},\n id = {d3966b5a-c854-3dea-b077-39d8b640287b},\n created = {2018-03-14T02:09:55.969Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-08T19:21:56.563Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhong2013a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Hand gesture control is an efficient modality for telerobot control because of gesture expressiveness and naturalness. This paper discusses a collaborative cybernetic system, where telerobots are controlled simultaneously by a group of distributed operators over the network to accomplish a task. Computer vision algorithms for hand gesture recognition are introduced to facilitate the human-robot interface. The gestures are converted into commands that are delivered to robots for dexterous task completion. Commands from multiple operators are aggregated by a collaboration protocol into a single control stream. The aggregation is updated according to operators performance. A distributed conflict and error detection-prediction network is designed and applied to a case study of collaborative control for a robotic nuclear decommissioning task. Operators use hand gestures to command telerobots to disassemble facilities in a contaminated area. The hypothesis is tested that collaborative control is more effective and less susceptible to conflicts/errors than the standard single-operator control. During collaboration, operators performed gesture commands simultaneously to control a set of robots. The system can reliably recognise operators hands with a 96% accuracy in cluttered backgrounds. Collaboration between expert and novice operators can reduce the time to complete a multi-step task by 45% on average. © 2013 Taylor and Francis Group, LLC.},\n bibtype = {article},\n author = {Zhong, Hao and Wachs, Juan P. and Nof, Shimon Y.},\n doi = {10.1080/00207543.2012.756591},\n journal = {International Journal of Production Research},\n number = {15}\n}\n\n Hand gesture control is an efficient modality for telerobot control because of gesture expressiveness and naturalness. This paper discusses a collaborative cybernetic system, where telerobots are controlled simultaneously by a group of distributed operators over the network to accomplish a task. Computer vision algorithms for hand gesture recognition are introduced to facilitate the human-robot interface. The gestures are converted into commands that are delivered to robots for dexterous task completion. Commands from multiple operators are aggregated by a collaboration protocol into a single control stream. The aggregation is updated according to operators performance. A distributed conflict and error detection-prediction network is designed and applied to a case study of collaborative control for a robotic nuclear decommissioning task. Operators use hand gestures to command telerobots to disassemble facilities in a contaminated area. The hypothesis is tested that collaborative control is more effective and less susceptible to conflicts/errors than the standard single-operator control. During collaboration, operators performed gesture commands simultaneously to control a set of robots. The system can reliably recognise operators hands with a 96% accuracy in cluttered backgrounds. Collaboration between expert and novice operators can reduce the time to complete a multi-step task by 45% on average. © 2013 Taylor and Francis Group, LLC.\n
\n\n\n\n \n\n \n \n \n \n \n 3D joystick for robotic arm control by individuals with high level spinal cord injuries.\n \n \n \n\n\n \n Jiang, H.; Wachs, J.; Pendergast, M.; and Duerstock, B.\n\n\n \n\n\n\n In IEEE International Conference on Rehabilitation Robotics, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {3D joystick for robotic arm control by individuals with high level spinal cord injuries},\n type = {inproceedings},\n year = {2013},\n keywords = {3D joystick,Assistive technology,multimodal HCI,quadriplegia,robotic arm,spinal cord injury},\n id = {fe6f4bc1-eb58-314b-84cf-24092e867f33},\n created = {2018-03-14T02:09:56.485Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T19:27:28.435Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Jiang2013a},\n private_publication = {false},\n abstract = {An innovative 3D joystick was developed to enable quadriplegics due to spinal cord injuries (SCIs) to more independently and efficiently operate a robotic arm as an assistive device. The 3D joystick was compared to two different manual input modalities, a keyboard control and a traditional joystick, in performing experimental robotic arm tasks by both subjects without disabilities and those with upper extremity mobility impairments. Fitts's Law targeting and practical pouring tests were conducted to compare the performance and accuracy of the proposed 3D joystick. The Fitts's law measurements showed that the 3D joystick had the best index of performance (IP), though it required an equivalent number of operations and errors as the standard robotic arm joystick. The pouring task demonstrated that the 3D joystick took significantly less task completion time and was more accurate than keyboard control. The 3D joystick also showed a decreased learning curve to the other modalities. © 2013 IEEE.},\n bibtype = {inproceedings},\n author = {Jiang, H. and Wachs, J.P. and Pendergast, M. and Duerstock, B.S.},\n doi = {10.1109/ICORR.2013.6650432},\n booktitle = {IEEE International Conference on Rehabilitation Robotics}\n}\n\n An innovative 3D joystick was developed to enable quadriplegics due to spinal cord injuries (SCIs) to more independently and efficiently operate a robotic arm as an assistive device. The 3D joystick was compared to two different manual input modalities, a keyboard control and a traditional joystick, in performing experimental robotic arm tasks by both subjects without disabilities and those with upper extremity mobility impairments. Fitts's Law targeting and practical pouring tests were conducted to compare the performance and accuracy of the proposed 3D joystick. The Fitts's law measurements showed that the 3D joystick had the best index of performance (IP), though it required an equivalent number of operations and errors as the standard robotic arm joystick. The pouring task demonstrated that the 3D joystick took significantly less task completion time and was more accurate than keyboard control. The 3D joystick also showed a decreased learning curve to the other modalities. © 2013 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n A cyber-physical management system for delivering and monitoring surgical instruments in the or.\n \n \n \n\n\n \n Li, Y.; Jacob, M.; Akingba, G.; and Wachs, J.\n\n\n \n\n\n\n Surgical Innovation, 20(4). 2013.\n \n\n\n\n
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\n\n\n@article{\n title = {A cyber-physical management system for delivering and monitoring surgical instruments in the or},\n type = {article},\n year = {2013},\n keywords = {cyber-physical systems,personal health records,retained instruments,surgical robotics},\n volume = {20},\n id = {e87b3eef-c30c-31f6-94a9-1e5de40c04fa},\n created = {2018-03-14T02:09:56.982Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T19:27:28.832Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Li2013},\n private_publication = {false},\n abstract = {Background. The standard practice in the operating room (OR) is having a surgical technician deliver surgical instruments to the surgeon quickly and inexpensively, as required. This human "in the loop" system may result in mistakes (eg, missing information, ambiguity of instructions, and delays). Objective. Errors can be reduced or eliminated by integrating information technology (IT) and cybernetics into the OR. Gesture and voice automatic acquisition, processing, and interpretation allow interaction with these new systems without disturbing the normal flow of surgery. Methods. This article describes the development of a cyber-physical management system (CPS), including a robotic scrub nurse, to support surgeons by passing surgical instruments during surgery as required and recording counts of surgical instruments into a personal health record (PHR). The robot used responds to hand signals and voice messages detected through sophisticated computer vision and data mining techniques. Results. The CPS was tested during a mock surgery in the OR. The in situ experiment showed that the robot recognized hand gestures reliably (with an accuracy of 97%), it can retrieve instruments as close as 25 mm, and the total delivery time was less than 3 s on average. Conclusions. This online health tool allows the exchange of clinical and surgical information to electronic medical record-based and PHR-based applications among different hospitals, regardless of the style viewer. The CPS has the potential to be adopted in the OR to handle surgical instruments and track them in a safe and accurate manner, releasing the human scrub tech from these tasks. © The Author(s) 2012.},\n bibtype = {article},\n author = {Li, Y.-T. and Jacob, M. and Akingba, G. and Wachs, J.P.},\n doi = {10.1177/1553350612459109},\n journal = {Surgical Innovation},\n number = {4}\n}\n\n Background. The standard practice in the operating room (OR) is having a surgical technician deliver surgical instruments to the surgeon quickly and inexpensively, as required. This human \"in the loop\" system may result in mistakes (eg, missing information, ambiguity of instructions, and delays). Objective. Errors can be reduced or eliminated by integrating information technology (IT) and cybernetics into the OR. Gesture and voice automatic acquisition, processing, and interpretation allow interaction with these new systems without disturbing the normal flow of surgery. Methods. This article describes the development of a cyber-physical management system (CPS), including a robotic scrub nurse, to support surgeons by passing surgical instruments during surgery as required and recording counts of surgical instruments into a personal health record (PHR). The robot used responds to hand signals and voice messages detected through sophisticated computer vision and data mining techniques. Results. The CPS was tested during a mock surgery in the OR. The in situ experiment showed that the robot recognized hand gestures reliably (with an accuracy of 97%), it can retrieve instruments as close as 25 mm, and the total delivery time was less than 3 s on average. Conclusions. This online health tool allows the exchange of clinical and surgical information to electronic medical record-based and PHR-based applications among different hospitals, regardless of the style viewer. The CPS has the potential to be adopted in the OR to handle surgical instruments and track them in a safe and accurate manner, releasing the human scrub tech from these tasks. © The Author(s) 2012.\n
\n\n\n\n \n\n \n \n \n \n \n \"Telementoring\" en el quirófano: Un nuevo enfoque en la formación médica.\n \n \n \n\n\n \n Wachs, J., P.; and Gomez, G.\n\n\n \n\n\n\n Medicina (Argentina), 73(6): 539-542. 2013.\n \n\n\n\n
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\n\n\n@article{\n title = {"Telementoring" en el quirófano: Un nuevo enfoque en la formación médica},\n type = {article},\n year = {2013},\n keywords = {Computer technology,Gestures,Operating room,Surgical instruction,Surgical robotics,Telementoring},\n pages = {539-542},\n volume = {73},\n id = {183cbbd8-af93-3a1d-a323-dc96ff52a4f8},\n created = {2021-06-04T19:22:45.674Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.321Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper discusses the challenges and innovations related to the use of telementoring systems in the operating room. Most of the systems presented leverage on three types of interaction channels: audio, visual and physical. The audio channel enables the mentor to verbally instruct the trainee, and allows the trainee to ask questions. The visual channel is used to deliver annotations, alerts and other messages graphically to the trainee during the surgery. These visual representations are often displayed through a telestrator. The physical channel has been used in laparoscopic procedures by partially controlling the laparoscope through force-feedback. While in face to face instruction, the mentor produces gestures to convey certain aspects of the surgical instruction, there is not equivalent of this form of physical interaction between the mentor and trainee in open surgical procedures in telementoring systems. Even that the trend is to perform more minimally invasive surgery (MIS), trauma surgeries are still necessary, where initial resuscitation and stabilization of the patient in a timely manner is crucial. This paper presents a preliminary study conducted at the Indiana University Medical School and Purdue University, where initial lexicons of surgical instructive gestures (SIGs) were determined through systematic observation when mentor and trainee operate together. The paper concludes with potential ways to convey gestural information through surgical robots.},\n bibtype = {article},\n author = {Wachs, Juan P. and Gomez, Gerardo},\n journal = {Medicina (Argentina)},\n number = {6}\n}\n\n This paper discusses the challenges and innovations related to the use of telementoring systems in the operating room. Most of the systems presented leverage on three types of interaction channels: audio, visual and physical. The audio channel enables the mentor to verbally instruct the trainee, and allows the trainee to ask questions. The visual channel is used to deliver annotations, alerts and other messages graphically to the trainee during the surgery. These visual representations are often displayed through a telestrator. The physical channel has been used in laparoscopic procedures by partially controlling the laparoscope through force-feedback. While in face to face instruction, the mentor produces gestures to convey certain aspects of the surgical instruction, there is not equivalent of this form of physical interaction between the mentor and trainee in open surgical procedures in telementoring systems. Even that the trend is to perform more minimally invasive surgery (MIS), trauma surgeries are still necessary, where initial resuscitation and stabilization of the patient in a timely manner is crucial. This paper presents a preliminary study conducted at the Indiana University Medical School and Purdue University, where initial lexicons of surgical instructive gestures (SIGs) were determined through systematic observation when mentor and trainee operate together. The paper concludes with potential ways to convey gestural information through surgical robots.\n
\n\n\n\n \n\n \n \n \n \n \n 3D joystick for robotic arm control by individuals with high level spinal cord injuries.\n \n \n \n\n\n \n Jiang, H.; Wachs, J., P.; Pendergast, M.; and Duerstock, B., S.\n\n\n \n\n\n\n In IEEE International Conference on Rehabilitation Robotics, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {3D joystick for robotic arm control by individuals with high level spinal cord injuries},\n type = {inproceedings},\n year = {2013},\n keywords = {3D joystick,Assistive technology,multimodal HCI,quadriplegia,robotic arm,spinal cord injury},\n id = {8ece9655-d3a8-38da-8804-49c50185479d},\n created = {2021-06-04T19:22:45.674Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.332Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {An innovative 3D joystick was developed to enable quadriplegics due to spinal cord injuries (SCIs) to more independently and efficiently operate a robotic arm as an assistive device. The 3D joystick was compared to two different manual input modalities, a keyboard control and a traditional joystick, in performing experimental robotic arm tasks by both subjects without disabilities and those with upper extremity mobility impairments. Fitts's Law targeting and practical pouring tests were conducted to compare the performance and accuracy of the proposed 3D joystick. The Fitts's law measurements showed that the 3D joystick had the best index of performance (IP), though it required an equivalent number of operations and errors as the standard robotic arm joystick. The pouring task demonstrated that the 3D joystick took significantly less task completion time and was more accurate than keyboard control. The 3D joystick also showed a decreased learning curve to the other modalities. © 2013 IEEE.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Wachs, Juan P. and Pendergast, Martin and Duerstock, Bradley S.},\n doi = {10.1109/ICORR.2013.6650432},\n booktitle = {IEEE International Conference on Rehabilitation Robotics}\n}\n\n An innovative 3D joystick was developed to enable quadriplegics due to spinal cord injuries (SCIs) to more independently and efficiently operate a robotic arm as an assistive device. The 3D joystick was compared to two different manual input modalities, a keyboard control and a traditional joystick, in performing experimental robotic arm tasks by both subjects without disabilities and those with upper extremity mobility impairments. Fitts's Law targeting and practical pouring tests were conducted to compare the performance and accuracy of the proposed 3D joystick. The Fitts's law measurements showed that the 3D joystick had the best index of performance (IP), though it required an equivalent number of operations and errors as the standard robotic arm joystick. The pouring task demonstrated that the 3D joystick took significantly less task completion time and was more accurate than keyboard control. The 3D joystick also showed a decreased learning curve to the other modalities. © 2013 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n \n Telementoring systems in the operating room: A new approach in medical training.\n \n \n \n \n\n\n \n Wachs, J., J., J., P.; and Gomez, G.\n\n\n \n\n\n\n Medicina (Argentina), 73(6): 539-542. 2013.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Telementoring systems in the operating room: A new approach in medical training},\n type = {article},\n year = {2013},\n keywords = {Computer technology,Gestures,Operating room,Surgical instruction,Surgical robotics,Telementoring},\n pages = {539-542},\n volume = {73},\n websites = {http://www.medicinabuenosaires.com/revistas/vol73-13/6/539-542-MED6-6130-A.pdfcolor.pdf},\n id = {96e0cb7e-19b7-3cc6-8601-6932aa577a3a},\n created = {2021-06-04T19:36:47.485Z},\n accessed = {2015-01-27},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.470Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2013},\n source_type = {article},\n folder_uuids = {ef47f247-b122-4733-9f1b-89eb5113ab66,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper discusses the challenges and innovations related to the use of telementoring systems in the operating room. Most of the systems presented leverage on three types of interaction channels: audio, visual and physical. The audio channel enables the mentor to verbally instruct the trainee, and allows the trainee to ask questions. The visual channel is used to deliver annotations, alerts and other messages graphically to the trainee during the surgery. These visual representations are often displayed through a telestrator. The physical channel has been used in laparoscopic procedures by partially controlling the laparoscope through force-feedback. While in face to face instruction, the mentor produces gestures to convey certain aspects of the surgical instruction, there is not equivalent of this form of physical interaction between the mentor and trainee in open surgical procedures in telementoring systems. Even that the trend is to perform more minimally invasive surgery (MIS), trauma surgeries are still necessary, where initial resuscitation and stabilization of the patient in a timely manner is crucial. This paper presents a preliminary study conducted at the Indiana University Medical School and Purdue University, where initial lexicons of surgical instructive gestures (SIGs) were determined through systematic observation when mentor and trainee operate together. The paper concludes with potential ways to convey gestural information through surgical robots.},\n bibtype = {article},\n author = {Wachs, J.P. JP Juan P. and Gomez, Gerardo},\n journal = {Medicina (Argentina)},\n number = {6}\n}\n\n This paper discusses the challenges and innovations related to the use of telementoring systems in the operating room. Most of the systems presented leverage on three types of interaction channels: audio, visual and physical. The audio channel enables the mentor to verbally instruct the trainee, and allows the trainee to ask questions. The visual channel is used to deliver annotations, alerts and other messages graphically to the trainee during the surgery. These visual representations are often displayed through a telestrator. The physical channel has been used in laparoscopic procedures by partially controlling the laparoscope through force-feedback. While in face to face instruction, the mentor produces gestures to convey certain aspects of the surgical instruction, there is not equivalent of this form of physical interaction between the mentor and trainee in open surgical procedures in telementoring systems. Even that the trend is to perform more minimally invasive surgery (MIS), trauma surgeries are still necessary, where initial resuscitation and stabilization of the patient in a timely manner is crucial. This paper presents a preliminary study conducted at the Indiana University Medical School and Purdue University, where initial lexicons of surgical instructive gestures (SIGs) were determined through systematic observation when mentor and trainee operate together. The paper concludes with potential ways to convey gestural information through surgical robots.\n
\n\n\n\n \n\n \n \n \n \n \n \n Recognizing hand gestures using the weighted elastic graph matching (WEGM) method.\n \n \n \n \n\n\n \n Li, Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n Image and Vision Computing, 31(9): 649-657. 9 2013.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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Paper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n
\n\n\n@article{\n title = {Recognizing hand gestures using the weighted elastic graph matching (WEGM) method},\n type = {article},\n year = {2013},\n keywords = {Elastic bunch graph,Feature weight,Graph matching,Hand gesture recognition},\n pages = {649-657},\n volume = {31},\n websites = {http://www.sciencedirect.com/science/article/pii/S0262885613001030},\n month = {9},\n id = {fdfe0b5d-a1d5-3f6d-880a-7267ac0f331c},\n created = {2021-06-04T19:36:47.797Z},\n accessed = {2015-01-08},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.659Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2013b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {This paper proposes a weighted scheme for elastic graph matching hand posture recognition. Visual features scattered on the elastic graph are assigned corresponding weights according to their relative ability to discriminate between gestures. The weights' values are determined using adaptive boosting. A dictionary representing the variability of each gesture class is expressed in the form of a bunch graph. The positions of the nodes in the bunch graph are determined using three techniques: manually, semi-automatically, and automatically. Experimental results also show that the semi-automatic annotation method is efficient and accurate in terms of three performance measures; assignment cost, accuracy, and transformation error. In terms of the recognition accuracy, our results show that the hierarchical weighting on features has more significant discriminative power than the classic method (uniform weighting). The hierarchical elastic graph matching (WEGM) approach was used to classify a lexicon of ten hand postures, and it was found that the poses were recognized with a recognition accuracy of 97.08% on average. Using the weighted scheme, computing cycles can be decreased by only computing the features for those nodes whose weight is relatively high and ignoring the remaining nodes. It was found that only 30% of the nodes need to be computed to obtain a recognition accuracy of over 90%. © 2013 Elsevier B.V. All rights reserved.},\n bibtype = {article},\n author = {Li, Yu-Ting Ting and Wachs, J.P. Juan P.},\n doi = {10.1016/j.imavis.2013.06.008},\n journal = {Image and Vision Computing},\n number = {9}\n}\n\n This paper proposes a weighted scheme for elastic graph matching hand posture recognition. Visual features scattered on the elastic graph are assigned corresponding weights according to their relative ability to discriminate between gestures. The weights' values are determined using adaptive boosting. A dictionary representing the variability of each gesture class is expressed in the form of a bunch graph. The positions of the nodes in the bunch graph are determined using three techniques: manually, semi-automatically, and automatically. Experimental results also show that the semi-automatic annotation method is efficient and accurate in terms of three performance measures; assignment cost, accuracy, and transformation error. In terms of the recognition accuracy, our results show that the hierarchical weighting on features has more significant discriminative power than the classic method (uniform weighting). The hierarchical elastic graph matching (WEGM) approach was used to classify a lexicon of ten hand postures, and it was found that the poses were recognized with a recognition accuracy of 97.08% on average. Using the weighted scheme, computing cycles can be decreased by only computing the features for those nodes whose weight is relatively high and ignoring the remaining nodes. It was found that only 30% of the nodes need to be computed to obtain a recognition accuracy of over 90%. © 2013 Elsevier B.V. All rights reserved.\n
\n\n\n\n \n\n \n \n \n \n \n The improvement and application of intelligence tracking algorithm for moving logistics objects based on machine vision sensor.\n \n \n \n\n\n \n Zhang, S., S., S.; and Wachs, J., J., P.\n\n\n \n\n\n\n Sensor Letters, 11(5): 862-869. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {The improvement and application of intelligence tracking algorithm for moving logistics objects based on machine vision sensor},\n type = {article},\n year = {2013},\n keywords = {Covariance matrix,Logistics objects,Machine vision sensor,Tracking algorithm},\n pages = {862-869},\n volume = {11},\n id = {5cbe4f2d-1a23-3520-b4e8-c2e03ab3d37a},\n created = {2021-06-04T19:36:48.867Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.885Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhang2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Logistics object tracking is one of the key technologies of intelligence logistics management system (ILMS). Using machine vision sensor technology, the paper introduced the application method of machine vision sensor technology in intelligent logistics system, and proposed an improved and simple covariance matrix algorithm to detect and track moving logistics objects. Against the features and technical difficulties of moving logistics objects detection, the covariance matrix algorithm was applied to detecting and tracking of logistics objects, and against the shortcomings of covariance matrix algorithm in the process of detecting and tracking of moving logistics objects, the paper presented a method of logistics objects path prediction, and template image dynamic selection and adjustment. Experiments show that the method can effectively apply the improved covariance matrix algorithm to the detecting and tracking of moving logistics objects, the method can not only adapt quickly to pose and scale variations of logistics objects, but also track accurately and continuously those temporarily occluded logistics objects, the improved method has good robustness. The method provides a new solution of the detecting and tracking of moving logistics objects. Copyright © 2013 American Scientific Publishers.},\n bibtype = {article},\n author = {Zhang, S.S. Shu Shan and Wachs, J.P. Juan P.},\n doi = {10.1166/sl.2013.2658},\n journal = {Sensor Letters},\n number = {5}\n}\n\n Logistics object tracking is one of the key technologies of intelligence logistics management system (ILMS). Using machine vision sensor technology, the paper introduced the application method of machine vision sensor technology in intelligent logistics system, and proposed an improved and simple covariance matrix algorithm to detect and track moving logistics objects. Against the features and technical difficulties of moving logistics objects detection, the covariance matrix algorithm was applied to detecting and tracking of logistics objects, and against the shortcomings of covariance matrix algorithm in the process of detecting and tracking of moving logistics objects, the paper presented a method of logistics objects path prediction, and template image dynamic selection and adjustment. Experiments show that the method can effectively apply the improved covariance matrix algorithm to the detecting and tracking of moving logistics objects, the method can not only adapt quickly to pose and scale variations of logistics objects, but also track accurately and continuously those temporarily occluded logistics objects, the improved method has good robustness. The method provides a new solution of the detecting and tracking of moving logistics objects. Copyright © 2013 American Scientific Publishers.\n
\n\n\n\n \n\n \n \n \n \n \n \n Surgical instrument handling and retrieval in the operating room with a multimodal robotic assistant.\n \n \n \n \n\n\n \n Jacob, M., M., G.; Li, Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings - IEEE International Conference on Robotics and Automation, pages 2140-2145, 5 2013. IEEE\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Surgical instrument handling and retrieval in the operating room with a multimodal robotic assistant},\n type = {inproceedings},\n year = {2013},\n pages = {2140-2145},\n websites = {http://ieeexplore.ieee.org/document/6630864/},\n month = {5},\n publisher = {IEEE},\n id = {d025e177-4e6e-3997-b1a1-5777514a7dae},\n created = {2021-06-04T19:36:48.962Z},\n accessed = {2018-07-10},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.004Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {A robotic scrub nurse (RSN) designed for safe human-robot collaboration in the operating room (OR) is presented. The RSN assists the surgical staff in the OR by delivering instruments to the surgeon and operates through a multimodal interface allowing instruments to be requested through verbal commands or touchless gestures. A machine vision algorithm was designed to recognize the hand gestures performed by the user. To ensure safe human-robot collaboration, tool-tip trajectories are planned and executed to avoid collisions with the user. Experiments were conducted to test the system when speech and gesture modalities were used to interact with the robot, separately and together. The average system times were compared while performing a mock surgical task for each modality of interaction. The effects of modality training on task completion time were also studied. It was found that training results in a significant drop of 12.92% in task completion time. Experimental results show that 95.96% of the gestures used to interact with the robot were recognized correctly, and collisions with the user were completely avoided when using a new active obstacle avoidance algorithm. © 2013 IEEE.},\n bibtype = {inproceedings},\n author = {Jacob, M.G. Mithun G. and Li, Yu-Ting Ting and Wachs, J.P. Juan P.},\n doi = {10.1109/ICRA.2013.6630864},\n booktitle = {Proceedings - IEEE International Conference on Robotics and Automation}\n}\n\n A robotic scrub nurse (RSN) designed for safe human-robot collaboration in the operating room (OR) is presented. The RSN assists the surgical staff in the OR by delivering instruments to the surgeon and operates through a multimodal interface allowing instruments to be requested through verbal commands or touchless gestures. A machine vision algorithm was designed to recognize the hand gestures performed by the user. To ensure safe human-robot collaboration, tool-tip trajectories are planned and executed to avoid collisions with the user. Experiments were conducted to test the system when speech and gesture modalities were used to interact with the robot, separately and together. The average system times were compared while performing a mock surgical task for each modality of interaction. The effects of modality training on task completion time were also studied. It was found that training results in a significant drop of 12.92% in task completion time. Experimental results show that 95.96% of the gestures used to interact with the robot were recognized correctly, and collisions with the user were completely avoided when using a new active obstacle avoidance algorithm. © 2013 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n Integrated vision-based robotic arm interface for operators with upper limb mobility impairments.\n \n \n \n\n\n \n Jiang, H.; Wachs, J., J., P.; and Duerstock, B., B., S.\n\n\n \n\n\n\n In IEEE International Conference on Rehabilitation Robotics, 2013. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Integrated vision-based robotic arm interface for operators with upper limb mobility impairments},\n type = {inproceedings},\n year = {2013},\n keywords = {gesture recognition,object recognition,spinal cord injuries,wheelchair-mounted robotic arm},\n id = {d847b589-d60f-39a4-a0cd-bb8887c38fe3},\n created = {2021-06-04T19:36:49.048Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.072Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {An integrated, computer vision-based system was developed to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In this paper, a gesture recognition interface system developed specifically for individuals with upper-level spinal cord injuries (SCIs) was combined with object tracking and face recognition systems to be an efficient, hands-free WMRM controller. In this test system, two Kinect cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures to send as commands to control the WMRM and locate the operator's face for object positioning. The other sensor was used to automatically recognize different daily living objects for test subjects to select. The gesture recognition interface incorporated hand detection, tracking and recognition algorithms to obtain a high recognition accuracy of 97.5% for an eight-gesture lexicon. An object recognition module employing Speeded Up Robust Features (SURF) algorithm was performed and recognition results were sent as a command for 'coarse positioning' of the robotic arm near the selected daily living object. Automatic face detection was also provided as a shortcut for the subjects to position the objects to the face by using a WMRM. Completion time tasks were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection and object recognition) WMRM control modes. The use of automatic face and object detection significantly increased the completion times for retrieving a variety of daily living objects. © 2013 IEEE.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Wachs, J.P. Juan P. and Duerstock, B.S. Bradley S.},\n doi = {10.1109/ICORR.2013.6650447},\n booktitle = {IEEE International Conference on Rehabilitation Robotics}\n}\n\n An integrated, computer vision-based system was developed to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In this paper, a gesture recognition interface system developed specifically for individuals with upper-level spinal cord injuries (SCIs) was combined with object tracking and face recognition systems to be an efficient, hands-free WMRM controller. In this test system, two Kinect cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures to send as commands to control the WMRM and locate the operator's face for object positioning. The other sensor was used to automatically recognize different daily living objects for test subjects to select. The gesture recognition interface incorporated hand detection, tracking and recognition algorithms to obtain a high recognition accuracy of 97.5% for an eight-gesture lexicon. An object recognition module employing Speeded Up Robust Features (SURF) algorithm was performed and recognition results were sent as a command for 'coarse positioning' of the robotic arm near the selected daily living object. Automatic face detection was also provided as a shortcut for the subjects to position the objects to the face by using a WMRM. Completion time tasks were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection and object recognition) WMRM control modes. The use of automatic face and object detection significantly increased the completion times for retrieving a variety of daily living objects. © 2013 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n HUB-CI model for collaborative telerobotics in manufacturing.\n \n \n \n\n\n \n Zhong, H.; Wachs, J., J., P.; and Nof, S., S., Y.\n\n\n \n\n\n\n In IFAC Proceedings Volumes (IFAC-PapersOnline), volume 46, pages 63-68, 2013. \n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {HUB-CI model for collaborative telerobotics in manufacturing},\n type = {inproceedings},\n year = {2013},\n keywords = {Collaboration infrastructure,Collaborative intelligence,Collaborative telerobotics,Human-robot interaction},\n pages = {63-68},\n volume = {46},\n issue = {7},\n id = {1219d9cd-fd22-3006-9187-a4bc64f7705a},\n created = {2021-06-04T19:36:49.198Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.239Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Zhong2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper discusses a collaborative cybernetic system, where telerobots are controlled simultaneously by a group of distributed operators to accomplish a manufacturing task. To enhance the collaboration, this work introduces the HUB-CI (HUB system with Collaborative Intelligence) model which represents a cyber hub infrastructure with intelligent agents supporting the collaboration activities. Hand gesture commands from multiple operators are translated and aggregated by a collaboration protocol into a single control stream. The aggregation is updated according to operators' performance so that it is robust to critical errors and conflicting signals. The premise of early conflict/error prevention and co-tolerant scheme can help in reducing the risk of system's damage. Thus, a distributed conflict and error prevention network is designed in the current work. A case study of collaborative control of robotic manufacturing is investigated. Operators command telerobots to work in a remote area. The hypothesis is tested that collaborative control with HUB-CI model is more effective and less susceptible to conflicts/errors than single operator control. During collaboration, operators perform gesture commands in parallel to control the same set of robots with a command aggregation algorithm. Compared to a single operator manipulation, collaboration in HUB-CI can reduce the time to complete a multi-step task and limit the errors. © 2013 IFAC.},\n bibtype = {inproceedings},\n author = {Zhong, Hao and Wachs, J.P. Juan P. and Nof, S.Y. Shimon Y.},\n doi = {10.3182/20130522-3-BR-4036.00059},\n booktitle = {IFAC Proceedings Volumes (IFAC-PapersOnline)}\n}\n\n This paper discusses a collaborative cybernetic system, where telerobots are controlled simultaneously by a group of distributed operators to accomplish a manufacturing task. To enhance the collaboration, this work introduces the HUB-CI (HUB system with Collaborative Intelligence) model which represents a cyber hub infrastructure with intelligent agents supporting the collaboration activities. Hand gesture commands from multiple operators are translated and aggregated by a collaboration protocol into a single control stream. The aggregation is updated according to operators' performance so that it is robust to critical errors and conflicting signals. The premise of early conflict/error prevention and co-tolerant scheme can help in reducing the risk of system's damage. Thus, a distributed conflict and error prevention network is designed in the current work. A case study of collaborative control of robotic manufacturing is investigated. Operators command telerobots to work in a remote area. The hypothesis is tested that collaborative control with HUB-CI model is more effective and less susceptible to conflicts/errors than single operator control. During collaboration, operators perform gesture commands in parallel to control the same set of robots with a command aggregation algorithm. Compared to a single operator manipulation, collaboration in HUB-CI can reduce the time to complete a multi-step task and limit the errors. © 2013 IFAC.\n
\n\n\n\n \n\n \n \n \n \n \n Smart instrumented training ranges: Bringing automated system solutions to support critical domain needs.\n \n \n \n\n\n \n Sadagic, A.; Mathias, K.; Welch, G.; Basu, C.; Darken, C.; Wachs, J., J., P.; Fuchs, H.; Towles, H.; Rowe, N.; Frahm, J., J., M.; Li, G.; Kumar, R.; and Cheng, H.\n\n\n \n\n\n\n Journal of Defense Modeling and Simulation, 10(3): 327-342. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Smart instrumented training ranges: Bringing automated system solutions to support critical domain needs},\n type = {article},\n year = {2013},\n keywords = {After-action-review,Automated behavior analysis,Behavior synthesis,Computer vision,Instrumented training ranges,Multi-sensor systems,Simulations},\n pages = {327-342},\n volume = {10},\n id = {57c9b14d-ca9a-37e7-8aa2-9b9d193d4d17},\n created = {2021-06-04T19:36:49.482Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.547Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Sadagic2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The training objective for urban warfare includes acquisition and perfection of a set of diverse skills in support of kinetic and non-kinetic operations. The US Marines (USMC) employ long-duration acted scenarios with verbal training feedback provided sporadically throughout the training session and at the end in a form of an after-action review (AAR). The inherent characteristic of training ranges for urban warfare is that they are the environments with a high level of physical occlusion, which causes many performances not to be seen by a group of instructors who oversee the training. We describe BASE-IT (Behavioral Analysis and Synthesis for Intelligent Training), a system in development that aims to automate capture of training data and their analysis, performance evaluation, and AAR report generation. The goal of this effort is to greatly increase the amount of observed behavior and improve the quality of the AAR. The system observes training with stationary cameras and personal tracking devices. It then analyzes movement and body postures, measures individual and squad-level performance, and compares it to standards and levels of performance expected in given situations. An interactive visualization component delivers live views augmented with real-time analytics and alerts; it also generates a personalized AAR review in a three-dimensional virtual or mixed reality environment, indexed by automatically extracted salient events and accompanied by summary statistics of unit performance. The approaches presented in the system have the potential to radically change the analysis and performance assessment on physical training ranges and ultimately this type of training itself. © 2013 The Society for Modeling and Simulation International.},\n bibtype = {article},\n author = {Sadagic, Amela and Mathias, Kölsch and Welch, Greg and Basu, Chumki and Darken, Chris and Wachs, J.P. Juan P. and Fuchs, Henry and Towles, Herman and Rowe, Neil and Frahm, J.-M. Jan Michael and Li, Guan and Kumar, Rakesh and Cheng, Hui},\n doi = {10.1177/1548512912472942},\n journal = {Journal of Defense Modeling and Simulation},\n number = {3}\n}\n\n The training objective for urban warfare includes acquisition and perfection of a set of diverse skills in support of kinetic and non-kinetic operations. The US Marines (USMC) employ long-duration acted scenarios with verbal training feedback provided sporadically throughout the training session and at the end in a form of an after-action review (AAR). The inherent characteristic of training ranges for urban warfare is that they are the environments with a high level of physical occlusion, which causes many performances not to be seen by a group of instructors who oversee the training. We describe BASE-IT (Behavioral Analysis and Synthesis for Intelligent Training), a system in development that aims to automate capture of training data and their analysis, performance evaluation, and AAR report generation. The goal of this effort is to greatly increase the amount of observed behavior and improve the quality of the AAR. The system observes training with stationary cameras and personal tracking devices. It then analyzes movement and body postures, measures individual and squad-level performance, and compares it to standards and levels of performance expected in given situations. An interactive visualization component delivers live views augmented with real-time analytics and alerts; it also generates a personalized AAR review in a three-dimensional virtual or mixed reality environment, indexed by automatically extracted salient events and accompanied by summary statistics of unit performance. The approaches presented in the system have the potential to radically change the analysis and performance assessment on physical training ranges and ultimately this type of training itself. © 2013 The Society for Modeling and Simulation International.\n
\n\n\n\n \n\n \n \n \n \n \n Visual analysis and filtering to augment cognition.\n \n \n \n\n\n \n Kölsch, M.; Wachs, J.; and Sadagic, A.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 8027 LNAI, pages 695-702, 2013. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Visual analysis and filtering to augment cognition},\n type = {inproceedings},\n year = {2013},\n keywords = {Augmented cognition,information analysis,training range instrumentation},\n pages = {695-702},\n volume = {8027 LNAI},\n id = {d2ecbe6c-81f5-3852-aa2d-a4fdbb8d45df},\n created = {2021-06-04T19:36:49.784Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.822Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Kolsch2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {We built and demonstrated a system that augments instructors' sensing abilities and augments their cognition through analysis and filtering of visual information. Called BASE-IT, our system helps US Marine instructors provide excellent training despite the challenging environment, hundreds of trainees and high trainee-to-instructor ratios, non-stop action, and diverse training objectives. To accomplish these objectives, BASE-IT widens the sensory input in multiple dimensions and filters relevant information: BASE-IT a) establishes omnipresence in a large training area, b) supplies continuous evaluation during multi-day training, c) pays specific attention to every individual, d) is specially equipped to identify dangerous situations, and e) maintains virtual vantage points for improved situational awareness. BASE-IT also augments and personalizes the after-action review information available to trainees. This paper focuses on the automated data analysis component, how it supplements the information available to instructors, and how it facilitates understanding of individual and team performances on the training range. © 2013 Springer-Verlag Berlin Heidelberg.},\n bibtype = {inproceedings},\n author = {Kölsch, Mathias and Wachs, Juan and Sadagic, Amela},\n doi = {10.1007/978-3-642-39454-6_74},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n We built and demonstrated a system that augments instructors' sensing abilities and augments their cognition through analysis and filtering of visual information. Called BASE-IT, our system helps US Marine instructors provide excellent training despite the challenging environment, hundreds of trainees and high trainee-to-instructor ratios, non-stop action, and diverse training objectives. To accomplish these objectives, BASE-IT widens the sensory input in multiple dimensions and filters relevant information: BASE-IT a) establishes omnipresence in a large training area, b) supplies continuous evaluation during multi-day training, c) pays specific attention to every individual, d) is specially equipped to identify dangerous situations, and e) maintains virtual vantage points for improved situational awareness. BASE-IT also augments and personalizes the after-action review information available to trainees. This paper focuses on the automated data analysis component, how it supplements the information available to instructors, and how it facilitates understanding of individual and team performances on the training range. © 2013 Springer-Verlag Berlin Heidelberg.\n
\n\n\n\n \n\n \n \n \n \n \n \n Collaboration with a robotic scrub nurse.\n \n \n \n \n\n\n \n Jacob, M., G., M.; Li, Y., T.; Akingba, G., G., A.; and Wachs, J., J., P.\n\n\n \n\n\n\n Communications of the ACM, 56(5): 68-75. 5 2013.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Collaboration with a robotic scrub nurse},\n type = {article},\n year = {2013},\n pages = {68-75},\n volume = {56},\n websites = {http://dl.acm.org/ft_gateway.cfm?id=2447993&type=html},\n month = {5},\n publisher = {ACM},\n day = {1},\n id = {df015741-c78f-3da4-8cb5-80efcb4386a7},\n created = {2021-06-04T19:36:49.901Z},\n accessed = {2014-08-11},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.214Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2013e},\n folder_uuids = {1455c9f7-6f55-4f23-a135-9d827a9f42ed,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The main use of robotics in surgery is not to replace the surgeon or surgical nurses but to work with them during surgery. A significant advantage of gesture-based communication is it requires no special training by the surgeon. Gesturing comes naturally to surgeons since their hands are already their main tools. Moreover, hand signs are the standard method for requesting surgical instruments, and gestures are not affected by ambient noise in the OR. A multimodal solution combining voice and gesture provides redundancy needed to assure proper instrument delivery. Gestures for robotic control have been the focus of much research since the early 1980s. Early work was done with Richard A. Bolt's Put-That-There interface followed by others using magnetic sensors or gloves to encode hand signs. The streaming depth maps captured through the Kinect sensor are processed by the gesture recognition module while a microphone concurrently captures voice commands interpreted by the speech recognition module. Following recognition, a command is transmitted to the robot through an application that controls a Fanuc LR Mate 200iC robotic arm across the network through a Telnet interface.},\n bibtype = {article},\n author = {Jacob, Mithun George M.G. and Li, Yu-Ting Ting and Akingba, G.A. George A. and Wachs, J.P. Juan P.},\n doi = {10.1145/2447976.2447993},\n journal = {Communications of the ACM},\n number = {5}\n}\n\n The main use of robotics in surgery is not to replace the surgeon or surgical nurses but to work with them during surgery. A significant advantage of gesture-based communication is it requires no special training by the surgeon. Gesturing comes naturally to surgeons since their hands are already their main tools. Moreover, hand signs are the standard method for requesting surgical instruments, and gestures are not affected by ambient noise in the OR. A multimodal solution combining voice and gesture provides redundancy needed to assure proper instrument delivery. Gestures for robotic control have been the focus of much research since the early 1980s. Early work was done with Richard A. Bolt's Put-That-There interface followed by others using magnetic sensors or gloves to encode hand signs. The streaming depth maps captured through the Kinect sensor are processed by the gesture recognition module while a microphone concurrently captures voice commands interpreted by the speech recognition module. Following recognition, a command is transmitted to the robot through an application that controls a Fanuc LR Mate 200iC robotic arm across the network through a Telnet interface.\n
\n\n\n\n \n\n \n \n \n \n \n Laser and photonic systems integration: Emerging innovations and framework for research and education.\n \n \n \n\n\n \n Nof, S., S., Y.; Cheng, G., G., J.; Weiner, A., M., A.; Chen, X., X., W.; Bechar, A.; Jones, M., G., M.; Reed, C., C., B.; Donmez, A.; Weldon, T., D., T.; Bermel, P.; Bukkapatnam, S., T.; Cheng, C.; Kumara, S., R., S.; Bement, A.; Koubek, R.; Bidanda, B.; Shin, Y., Y., C.; Capponi, A.; Lee, S.; Lehto, M., R.; Liu, A., L., A.; Nohadani, O.; Dantus, M.; Lorraine, P., W., P.; Nolte, D., D., D.; Proctor, R., W., R.; Sardesai, H., P., H.; Shi, L.; Wachs, J., J., P.; and Zhang, X., C., X.\n\n\n \n\n\n\n Human Factors and Ergonomics In Manufacturing, 23(6): 483-516. 2013.\n \n\n\n\n
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\n\n\n@article{\n title = {Laser and photonic systems integration: Emerging innovations and framework for research and education},\n type = {article},\n year = {2013},\n keywords = {Advanced manufacturing,Healthcare,Laser processing,Optical communication,Precision collaboration},\n pages = {483-516},\n volume = {23},\n id = {f4bf83b3-77b9-3bee-b566-69d8e1118bf3},\n created = {2021-06-04T19:36:49.908Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.982Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Nof2013},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The purpose of this article is to review the key emerging innovations in laser and photonics systems as well as their design and integration, focusing on challenges and opportunities for solutions of societal challenges. Developments, their significance, and frontier challenges are explained in advanced manufacturing, biomedicine and healthcare, and communication. Systems, networks, and integration issues and challenges are then discussed, and an integration framework for networking laser- and photonic-based services and products is proposed. The article concludes with implications and an agenda for education, research and development, and policy needs, with a focus on human, society, science, and technology integration. © 2013 Wiley Periodicals, Inc.},\n bibtype = {article},\n author = {Nof, S.Y. Shimon Y. and Cheng, G.J. Gary J. and Weiner, Andrew M. A.M. and Chen, X.W. Xin W. and Bechar, Avital and Jones, Marshall G. M.G. and Reed, C.B. Claude B. and Donmez, Alkan and Weldon, Thomas D. T.D. and Bermel, Peter and Bukkapatnam, Satish T.S. and Cheng, Changqing and Kumara, Soundar R.T. S.R.T. and Bement, Arden and Koubek, Richard and Bidanda, Bopaya and Shin, Y.C. Yung C. and Capponi, Agostino and Lee, Seokcheon and Lehto, Mark R. and Liu, Andrew L. A.L. and Nohadani, Omid and Dantus, Marcos and Lorraine, Peter W. P.W. and Nolte, D.D. David D. and Proctor, Robert W. R.W. and Sardesai, Harshad P. H.P. and Shi, Leyuan and Wachs, J.P. Juan P. and Zhang, Xi Cheng X.-C.},\n doi = {10.1002/hfm.20555},\n journal = {Human Factors and Ergonomics In Manufacturing},\n number = {6}\n}\n\n The purpose of this article is to review the key emerging innovations in laser and photonics systems as well as their design and integration, focusing on challenges and opportunities for solutions of societal challenges. Developments, their significance, and frontier challenges are explained in advanced manufacturing, biomedicine and healthcare, and communication. Systems, networks, and integration issues and challenges are then discussed, and an integration framework for networking laser- and photonic-based services and products is proposed. The article concludes with implications and an agenda for education, research and development, and policy needs, with a focus on human, society, science, and technology integration. © 2013 Wiley Periodicals, Inc.\n
\n\n\n\n \n\n \n \n \n \n \n \n Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images.\n \n \n \n \n\n\n \n Jacob, M., G.; Wachs, J., P.; and Packer, R., A.\n\n\n \n\n\n\n Journal of the American Medical Informatics Association : JAMIA, 20(e1): e183-6. 6 2013.\n \n\n\n\n
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\n\n\n@article{\n title = {Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images},\n type = {article},\n year = {2013},\n keywords = {Adolescent,Adult,Algorithms,Computers,Cues,Equipment Contamination,Equipment Contamination: prevention & control,Female,Gestures,Hand,Humans,Male,Operating Room Information Systems,Operating Rooms,Radiology Information Systems,Sterilization,User-Computer Interface,Young Adult},\n pages = {e183-6},\n volume = {20},\n websites = {http://jamia.bmj.com/content/20/e1/e183.full,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3715344&tool=pmcentrez&rendertype=abstract},\n month = {6},\n day = {1},\n id = {5d9beee4-d761-3f12-84e4-ab89605416dc},\n created = {2021-06-04T19:36:50.989Z},\n accessed = {2014-08-25},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.138Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2013d},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An image navigation and manipulation task was performed, and the gesture recognition accuracy, false positives and task completion times were computed to evaluate system performance. Experimental results show that gesture interaction and surgeon behavior analysis can be used to accurately navigate, manipulate and access MRI images, and therefore this modality could replace the use of keyboard and mice-based interfaces.},\n bibtype = {article},\n author = {Jacob, Mithun George and Wachs, Juan Pablo and Packer, Rebecca A.},\n doi = {10.1136/amiajnl-2012-001212},\n journal = {Journal of the American Medical Informatics Association : JAMIA},\n number = {e1}\n}\n\n This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An image navigation and manipulation task was performed, and the gesture recognition accuracy, false positives and task completion times were computed to evaluate system performance. Experimental results show that gesture interaction and surgeon behavior analysis can be used to accurately navigate, manipulate and access MRI images, and therefore this modality could replace the use of keyboard and mice-based interfaces.\n
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\n \n \n\n \n \n \n \n \n \n Interaction proxemics and image use in neurosurgery.\n \n \n \n \n\n\n \n Mentis, H., M.; O'Hara, K.; Sellen, A.; and Trivedi, R.\n\n\n \n\n\n\n In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12, pages 927, 5 2012. ACM Press\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Interaction proxemics and image use in neurosurgery},\n type = {inproceedings},\n year = {2012},\n keywords = {gestural,health,imaging,proxemics,space,surgery,touchless interaction},\n pages = {927},\n websites = {http://dl.acm.org/citation.cfm?id=2207676.2208536},\n month = {5},\n publisher = {ACM Press},\n day = {5},\n city = {New York, New York, USA},\n id = {3f030e69-6968-34a0-9584-82953e9950b7},\n created = {2014-10-29T22:13:17.000Z},\n accessed = {2014-10-29},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:57.882Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mentis2012a},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mentis, Helena M. and O'Hara, Kenton and Sellen, Abigail and Trivedi, Rikin},\n doi = {10.1145/2207676.2208536},\n booktitle = {Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12}\n}\n\n \n\n \n \n \n \n \n \n Hierarchical Elastic Graph Matching for Hand Gesture Recognition.\n \n \n \n \n\n\n \n Li, Y.; and Wachs, J., P.\n\n\n \n\n\n\n Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pages 308-315. Springer, 2012.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@inbook{\n type = {inbook},\n year = {2012},\n pages = {308-315},\n websites = {http://link.springer.com/chapter/10.1007/978-3-642-33275-3_38},\n publisher = {Springer},\n id = {c72e60e0-1f6f-3585-8c06-7374bb9d3f95},\n created = {2017-01-29T21:35:50.000Z},\n accessed = {2014-10-15},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:50.352Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {li_hierarchical_2012},\n source_type = {incollection},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,128681a6-ba46-469d-8c4e-cb337bbf0f22,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n bibtype = {inbook},\n author = {Li, Yu-Ting and Wachs, Juan P},\n chapter = {Hierarchical Elastic Graph Matching for Hand Gesture Recognition},\n title = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications}\n}\n\n \n\n \n \n \n \n \n \n Intention, Context and Gesture Recognition for Sterile MRI Navigation in the Operating Room.\n \n \n \n \n\n\n \n Jacob, M.; Cange, C.; Packer, R.; and Wachs, J., P.\n\n\n \n\n\n\n of Lecture Notes in Computer Science. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pages 220-227. Alvarez, L.; Mejail, M.; Gomez, L.; and Jacobo, J., editor(s). Springer Berlin Heidelberg, 1 2012.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inbook{\n type = {inbook},\n year = {2012},\n keywords = {Algorithm Analysis and Problem Complexity,Artificial Intelligence (incl. Robotics),Biometrics,Gesture recognition,Image Processing and Computer Vision,Information Systems Applications (incl. Internet),Pattern Recognition,human computer interaction,operating room},\n pages = {220-227},\n issue = {7441},\n websites = {http://link.springer.com/chapter/10.1007/978-3-642-33275-3_27},\n month = {1},\n publisher = {Springer Berlin Heidelberg},\n series = {Lecture Notes in Computer Science},\n id = {0be7f41d-841c-3494-a190-3ca29ea21530},\n created = {2017-01-29T21:36:05.000Z},\n accessed = {2014-10-14},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:10.777Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {jacob_intention_2012},\n source_type = {incollection},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,128681a6-ba46-469d-8c4e-cb337bbf0f22,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {Human-Computer Interaction (HCI) devices such as the keyboard and the mouse are among the most contaminated regions in an operating room (OR). This paper proposes a sterile, intuitive HCI to navigate MRI images using freehand gestures. The system incorporates contextual cues and intent of the user to strengthen the gesture recognition process. Experimental results showed that while performing an image navigation task, mean intent recognition accuracy was 98.7% and that the false positive rate of gesture recognition dropped from 20.76% to 2.33% with context integration at similar recognition rates.},\n bibtype = {inbook},\n author = {Jacob, Mithun and Cange, Christopher and Packer, Rebecca and Wachs, Juan P},\n editor = {Alvarez, Luis and Mejail, Marta and Gomez, Luis and Jacobo, Julio},\n chapter = {Intention, Context and Gesture Recognition for Sterile MRI Navigation in the Operating Room},\n title = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications}\n}\n\n Human-Computer Interaction (HCI) devices such as the keyboard and the mouse are among the most contaminated regions in an operating room (OR). This paper proposes a sterile, intuitive HCI to navigate MRI images using freehand gestures. The system incorporates contextual cues and intent of the user to strengthen the gesture recognition process. Experimental results showed that while performing an image navigation task, mean intent recognition accuracy was 98.7% and that the false positive rate of gesture recognition dropped from 20.76% to 2.33% with context integration at similar recognition rates.\n
\n\n\n\n \n\n \n \n \n \n \n Integrated gesture recognition based interface for people with upper extremity mobility impairments.\n \n \n \n\n\n \n Jiang, H.; Duerstock, B., S.; and Wachs, J., P.\n\n\n \n\n\n\n Advances in Human Aspects of Healthcare, pages 546-555. 2012.\n \n\n\n\n
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\n\n\n@inbook{\n type = {inbook},\n year = {2012},\n keywords = {Borg scale,Condensation,Gesture recognition,Particle filter},\n pages = {546-555},\n id = {74e4ff34-8cc0-3dba-97ea-1e3197911e29},\n created = {2021-06-04T19:22:35.383Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.032Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Gestures are of particular interest as a HCI modality for navigation because people already use gestures habitually to indicate directions. It only takes a user to learn few customized gestures for a given navigational task, as opposed to other technologies that require changing hardware components and lengthy procedures. We propose an integrated gesture recognition based interface for people with upper extremity mobility impairments to control a service robot. The following procedure was followed to construct the suggested system. Firstly, quadriplegics ranked a set of gestures using a Borg scale. This led to a number of principles for developing a gesture lexicon. Secondly, a particle filter method was used to recognize hands and represent a generalized model for hand motion based on its temporal trajectories. Finally, a CONDENSATION method was employed to classify the hand trajectories into different classes (commands) used, in turn, to control an actuated device-a robot. A validation experiment to control a service robot to negotiate obstacles in a controlled environment was conducted and results were reported.},\n bibtype = {inbook},\n author = {Jiang, Hairong and Duerstock, Bradley S. and Wachs, Juan P.},\n doi = {10.1201/b12318},\n chapter = {Integrated gesture recognition based interface for people with upper extremity mobility impairments},\n title = {Advances in Human Aspects of Healthcare}\n}\n\n Gestures are of particular interest as a HCI modality for navigation because people already use gestures habitually to indicate directions. It only takes a user to learn few customized gestures for a given navigational task, as opposed to other technologies that require changing hardware components and lengthy procedures. We propose an integrated gesture recognition based interface for people with upper extremity mobility impairments to control a service robot. The following procedure was followed to construct the suggested system. Firstly, quadriplegics ranked a set of gestures using a Borg scale. This led to a number of principles for developing a gesture lexicon. Secondly, a particle filter method was used to recognize hands and represent a generalized model for hand motion based on its temporal trajectories. Finally, a CONDENSATION method was employed to classify the hand trajectories into different classes (commands) used, in turn, to control an actuated device-a robot. A validation experiment to control a service robot to negotiate obstacles in a controlled environment was conducted and results were reported.\n
\n\n\n\n \n\n \n \n \n \n \n Hierarchical elastic graph matching for hand gesture recognition.\n \n \n \n\n\n \n Li, Y., Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, pages 308-315, 2012. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Hierarchical elastic graph matching for hand gesture recognition},\n type = {inproceedings},\n year = {2012},\n keywords = {Elastic bunch graph,Feature hierarchy,Graph matching,Hand gesture recognition},\n pages = {308-315},\n volume = {7441 LNCS},\n id = {1058e753-3543-3cb5-8f92-6fd100c5aead},\n created = {2021-06-04T19:36:47.870Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.713Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Li2012},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {This paper proposes a hierarchical scheme for elastic graph matching hand posture recognition. The hierarchy is expressed in terms of weights assigned to visual features scattered over an elastic graph. The weights in graph's nodes are adapted according to their relative ability to enhance the recognition, and determined using adaptive boosting. A dictionary representing the variability of each gesture class is proposed, in the form of a collection of graphs (a bunch graph). Positions of nodes in the bunch graph are created using three techniques: manually, semi-automatic, and automatically. The recognition results show that the hierarchical weighting on features has significant discriminative power compared to the classic method (uniform weighting). Experimental results also show that the semi-automatically annotation method provides efficient and accurate performance in terms of two performance measures; cost function and accuracy. © 2012 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Li, Y.-T. Yu Ting and Wachs, J.P. Juan P.},\n doi = {10.1007/978-3-642-33275-3_38},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n This paper proposes a hierarchical scheme for elastic graph matching hand posture recognition. The hierarchy is expressed in terms of weights assigned to visual features scattered over an elastic graph. The weights in graph's nodes are adapted according to their relative ability to enhance the recognition, and determined using adaptive boosting. A dictionary representing the variability of each gesture class is proposed, in the form of a collection of graphs (a bunch graph). Positions of nodes in the bunch graph are created using three techniques: manually, semi-automatic, and automatically. The recognition results show that the hierarchical weighting on features has significant discriminative power compared to the classic method (uniform weighting). Experimental results also show that the semi-automatically annotation method provides efficient and accurate performance in terms of two performance measures; cost function and accuracy. © 2012 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n \n Facilitated gesture recognition based interfaces for people with upper extremity physical impairments.\n \n \n \n \n\n\n \n Jiang, H.; Wachs, J., J., J., P.; and Duerstock, B., B., B., S.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, pages 228-235, 2012. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Facilitated gesture recognition based interfaces for people with upper extremity physical impairments},\n type = {inproceedings},\n year = {2012},\n keywords = {CONDENSATION,Gesture recognition,dynamic time warping (DTW),particle filter},\n pages = {228-235},\n volume = {7441 LNCS},\n websites = {http://link.springer.com/chapter/10.1007/978-3-642-33275-3_28},\n id = {19aaebc9-bd54-32cd-bd2f-de8b37dfa269},\n created = {2021-06-04T19:36:48.053Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.940Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jiang2012a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {A gesture recognition based interface was developed to facilitate people with upper extremity physical impairments as an alternative way to perform laboratory experiments that require 'physical' manipulation of components. A color, depth and spatial information based particle filter framework was constructed with unique descriptive features for face and hands representation. The same feature encoding policy was subsequently used to detect, track and recognize users' hands. Motion models were created employing dynamic time warping (DTW) method for better observation encoding. Finally, the hand trajectories were classified into different classes (commands) by applying the CONDENSATION method and, in turn, an interface was designed for robot control, with a recognition accuracy of 97.5%. To assess the gesture recognition and control policies, a validation experiment consisting in controlling a mobile service robot and a robotic arm in a laboratory environment was conducted. © 2012 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Jiang, Hairong and Wachs, J.P. JP Juan P. and Duerstock, BS B.S. Bradley S.},\n doi = {10.1007/978-3-642-33275-3_28},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n A gesture recognition based interface was developed to facilitate people with upper extremity physical impairments as an alternative way to perform laboratory experiments that require 'physical' manipulation of components. A color, depth and spatial information based particle filter framework was constructed with unique descriptive features for face and hands representation. The same feature encoding policy was subsequently used to detect, track and recognize users' hands. Motion models were created employing dynamic time warping (DTW) method for better observation encoding. Finally, the hand trajectories were classified into different classes (commands) by applying the CONDENSATION method and, in turn, an interface was designed for robot control, with a recognition accuracy of 97.5%. To assess the gesture recognition and control policies, a validation experiment consisting in controlling a mobile service robot and a robotic arm in a laboratory environment was conducted. © 2012 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n \n Gestonurse: A robotic surgical nurse for handling surgical instruments in the operating room.\n \n \n \n \n\n\n \n Jacob, M.; Li, Y., Y., Y., T.; Akingba, G.; and Wachs, J., J., J., P.\n\n\n \n\n\n\n Journal of Robotic Surgery, 6(1): 53-63. 11 2012.\n \n\n\n\n
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\n\n\n@article{\n title = {Gestonurse: A robotic surgical nurse for handling surgical instruments in the operating room},\n type = {article},\n year = {2012},\n keywords = {Gesture recognition,Human robot interfaces,Surgical robot},\n pages = {53-63},\n volume = {6},\n websites = {http://link.springer.com/10.1007/s11701-011-0325-0,http://link.springer.com/article/10.1007/s11701-011-0325-0},\n month = {11},\n day = {27},\n id = {b2d13371-87eb-3766-a3d2-4d08ef40197c},\n created = {2021-06-04T19:36:48.166Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.093Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2011b},\n folder_uuids = {9aad1ac8-ca90-4531-a464-3d13cdf32594,b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {While surgeon-scrub nurse collaboration provides a fast, straightforward and inexpensive method of delivering surgical instruments to the surgeon, it often results in "mistakes" (e. g. missing information, ambiguity of instructions and delays). It has been shown that these errors can have a negative impact on the outcome of the surgery. These errors could potentially be reduced or eliminated by introducing robotics into the operating room. Gesture control is a natural and fundamentally sound alternative that allows interaction without disturbing the normal flow of surgery. This paper describes the development of a robotic scrub nurse Gestonurse to support surgeons by passing surgical instruments during surgery as required. The robot responds to recognized hand signals detected through sophisticated computer vision and pattern recognition techniques. Experimental results show that 95% of the gestures were recognized correctly. The gesture recognition algorithm presented is robust to changes in scale and rotation of the hand gestures. The system was compared to human task performance and was found to be only 0.83 s slower on average. © 2011 Springer-Verlag London Ltd.},\n bibtype = {article},\n author = {Jacob, Mithun and Li, Yu-Ting YT Yu Ting and Akingba, George and Wachs, J.P. JP Juan P.},\n doi = {10.1007/s11701-011-0325-0},\n journal = {Journal of Robotic Surgery},\n number = {1}\n}\n\n While surgeon-scrub nurse collaboration provides a fast, straightforward and inexpensive method of delivering surgical instruments to the surgeon, it often results in \"mistakes\" (e. g. missing information, ambiguity of instructions and delays). It has been shown that these errors can have a negative impact on the outcome of the surgery. These errors could potentially be reduced or eliminated by introducing robotics into the operating room. Gesture control is a natural and fundamentally sound alternative that allows interaction without disturbing the normal flow of surgery. This paper describes the development of a robotic scrub nurse Gestonurse to support surgeons by passing surgical instruments during surgery as required. The robot responds to recognized hand signals detected through sophisticated computer vision and pattern recognition techniques. Experimental results show that 95% of the gestures were recognized correctly. The gesture recognition algorithm presented is robust to changes in scale and rotation of the hand gestures. The system was compared to human task performance and was found to be only 0.83 s slower on average. © 2011 Springer-Verlag London Ltd.\n
\n\n\n\n \n\n \n \n \n \n \n \n Gestonurse: a multimodal robotic scrub nurse.\n \n \n \n \n\n\n \n Jacob, M., G., M.; Li, Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, pages 153-154, 2012. ACM\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Gestonurse: a multimodal robotic scrub nurse},\n type = {inproceedings},\n year = {2012},\n keywords = {gesture recognition,human-robot interaction,multimodal interfaces,robotic scrub nurse},\n pages = {153-154},\n websites = {http://dl.acm.org/citation.cfm?id=2157731},\n publisher = {ACM},\n id = {77d5ecbc-b170-306c-abf4-661025c434ca},\n created = {2021-06-04T19:36:48.580Z},\n accessed = {2015-08-04},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.596Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {jacob_gestonurse:_2012},\n source_type = {inproceedings},\n short_title = {Gestonurse},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,128681a6-ba46-469d-8c4e-cb337bbf0f22,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {A novel multimodal robotic scrub nurse (RSN) system for the operating room (OR) is presented. The RSN assists the main surgeon by passing surgical instruments. Experiments were conducted to test the system with speech and gesture modalities and average instrument acquisition times were compared. Experimental results showed that 97% of the gestures were recognized correctly under changes in scale and rotation and that the multimodal system responded faster than the unimodal systems. A relationship similar in form to Fitts's law for instrument picking accuracy is also presented. © 2012 Authors.},\n bibtype = {inproceedings},\n author = {Jacob, Mithun George M.G. and Li, Yu-Ting Ting and Wachs, J.P. Juan P.},\n doi = {10.1145/2157689.2157731},\n booktitle = {Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction}\n}\n\n A novel multimodal robotic scrub nurse (RSN) system for the operating room (OR) is presented. The RSN assists the main surgeon by passing surgical instruments. Experiments were conducted to test the system with speech and gesture modalities and average instrument acquisition times were compared. Experimental results showed that 97% of the gestures were recognized correctly under changes in scale and rotation and that the multimodal system responded faster than the unimodal systems. A relationship similar in form to Fitts's law for instrument picking accuracy is also presented. © 2012 Authors.\n
\n\n\n\n \n\n \n \n \n \n \n Web-based facility monitoring by facility sensor networks.\n \n \n \n\n\n \n Ko, H., S., H.; Wachs, J., J., P.; and Nof, S., S., Y.\n\n\n \n\n\n\n In 62nd IIE Annual Conference and Expo 2012, pages 2852-2860, 2012. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Web-based facility monitoring by facility sensor networks},\n type = {inproceedings},\n year = {2012},\n keywords = {Facility sensor network,Real-time task allocation,Web-based monitoring,Wireless sensor network},\n pages = {2852-2860},\n id = {59202cd2-ca16-391a-8023-532cd0a3ccc8},\n created = {2021-06-04T19:36:48.938Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.954Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ko2012},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Wireless sensor networks (WSNs) provide an efficient solution for real-time monitoring of remote facilities. Due to their capability of enabling both temporally and spatially dense data-rich environments and low installation and maintenance cost, WSNs are becoming more feasible alternatives for facility monitoring. This study presents the steps in developing a reliable web-based facility monitoring system using a WSN, called Facility Sensor Network (FSN). In deployment of WSNs, there are challenges, including lifetime maximization, Quality of Service (QoS), and interference, which are addressed throughout this study. In addition, a mechanism to deal with service allocation is devised to handle service requests from multiple users, since this work aims at developing a web-based monitoring system on top of WSNs. To overcome these challenges, the FSN is developed with applications and protocols residing in three layers: 1) WSN layer containing fundamental hardware and software for mesh networking and their deployment; 2) Server layer handling data collection, data processing and service allocation; and 3) Client layer providing user interface and presentation service. It is anticipated that the proposed FSN will provide an easily accessible and reliable monitoring service that delivers timely and comprehensible information to end users over the Internet.},\n bibtype = {inproceedings},\n author = {Ko, Hoo Sang H.S. and Wachs, J.P. Juan P. and Nof, S.Y. Shimon Y.},\n booktitle = {62nd IIE Annual Conference and Expo 2012}\n}\n\n Wireless sensor networks (WSNs) provide an efficient solution for real-time monitoring of remote facilities. Due to their capability of enabling both temporally and spatially dense data-rich environments and low installation and maintenance cost, WSNs are becoming more feasible alternatives for facility monitoring. This study presents the steps in developing a reliable web-based facility monitoring system using a WSN, called Facility Sensor Network (FSN). In deployment of WSNs, there are challenges, including lifetime maximization, Quality of Service (QoS), and interference, which are addressed throughout this study. In addition, a mechanism to deal with service allocation is devised to handle service requests from multiple users, since this work aims at developing a web-based monitoring system on top of WSNs. To overcome these challenges, the FSN is developed with applications and protocols residing in three layers: 1) WSN layer containing fundamental hardware and software for mesh networking and their deployment; 2) Server layer handling data collection, data processing and service allocation; and 3) Client layer providing user interface and presentation service. It is anticipated that the proposed FSN will provide an easily accessible and reliable monitoring service that delivers timely and comprehensible information to end users over the Internet.\n
\n\n\n\n \n\n \n \n \n \n \n \n Does a robotic scrub nurse improve economy of movements?.\n \n \n \n \n\n\n \n Wachs, J., J., P.; Jacob, M.; Li, Y.; and Akingba, G.\n\n\n \n\n\n\n In Holmes III, D., R.; and Wong, K., H., editor(s), SPIE Medical Imaging, volume 8316, pages 83160E, 2 2012. International Society for Optics and Photonics\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Does a robotic scrub nurse improve economy of movements?},\n type = {inproceedings},\n year = {2012},\n keywords = {Robotics,computer vision,gesture recognition},\n pages = {83160E},\n volume = {8316},\n websites = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1346031},\n month = {2},\n publisher = {International Society for Optics and Photonics},\n day = {23},\n id = {5907d618-57d1-356e-901a-f3596073a217},\n created = {2021-06-04T19:36:49.177Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.224Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2012b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {Objective: Robotic assistance during surgery has been shown to be a useful resource to both augment the surgical skills of the surgeon through tele-operation, and to assist the surgeon handling the surgical instruments to the surgeon, similar to a surgical tech. We evaluated the performance and effect of a gesture driven surgical robotic nurse in the context of economy of movements, during an abdominal incision and closure exercise with a simulator. Methods: A longitudinal midline incision (100 mm) was performed on the simulated abdominal wall to enter the peritoneal cavity without damaging the internal organs. The wound was then closed using a blunt needle ensuring that no tissue is caught up by the suture material. All the instruments required to complete this task were delivered by a robotic surgical manipulator directly to the surgeon. The instruments were requested through voice and gesture recognition. The robotic system used a low end range sensor camera to extract the hand poses and for recognizing the gestures. The instruments were delivered to the vicinity of the patient, at chest height and at a reachable distance to the surgeon. Task performance measures for each of three abdominal incision and closure exercises were measured and compared to a human scrub nurse instrument delivery action. Picking instrument position variance, completion time and trajectory of the hand were recorded for further analysis. Results: The variance of the position of the robotic tip when delivering the surgical instrument is compared to the same position when a human delivers the instrument. The variance was found to be 88.86% smaller compared to the human delivery group. The mean task completion time to complete the surgical exercise was 162.7± 10.1 secs for the human assistant and 191.6± 3.3 secs (P < .01) when using the robotic standard display group. Conclusion: Multimodal robotic scrub nurse assistant improves the surgical procedure by reducing the number of movements (lower variance in the picking position). The variance of the picking point is closely related to the concept of economy of movements in the operating room. Improving the effectiveness of the operating room can potentially enhance the safety of surgical interventions without affecting the performance time. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).},\n bibtype = {inproceedings},\n author = {Wachs, J.P. Juan P. and Jacob, Mithun and Li, Yu-Ting and Akingba, George},\n editor = {Holmes III, David R. and Wong, Kenneth H.},\n doi = {10.1117/12.911930},\n booktitle = {SPIE Medical Imaging}\n}\n\n Objective: Robotic assistance during surgery has been shown to be a useful resource to both augment the surgical skills of the surgeon through tele-operation, and to assist the surgeon handling the surgical instruments to the surgeon, similar to a surgical tech. We evaluated the performance and effect of a gesture driven surgical robotic nurse in the context of economy of movements, during an abdominal incision and closure exercise with a simulator. Methods: A longitudinal midline incision (100 mm) was performed on the simulated abdominal wall to enter the peritoneal cavity without damaging the internal organs. The wound was then closed using a blunt needle ensuring that no tissue is caught up by the suture material. All the instruments required to complete this task were delivered by a robotic surgical manipulator directly to the surgeon. The instruments were requested through voice and gesture recognition. The robotic system used a low end range sensor camera to extract the hand poses and for recognizing the gestures. The instruments were delivered to the vicinity of the patient, at chest height and at a reachable distance to the surgeon. Task performance measures for each of three abdominal incision and closure exercises were measured and compared to a human scrub nurse instrument delivery action. Picking instrument position variance, completion time and trajectory of the hand were recorded for further analysis. Results: The variance of the position of the robotic tip when delivering the surgical instrument is compared to the same position when a human delivers the instrument. The variance was found to be 88.86% smaller compared to the human delivery group. The mean task completion time to complete the surgical exercise was 162.7± 10.1 secs for the human assistant and 191.6± 3.3 secs (P < .01) when using the robotic standard display group. Conclusion: Multimodal robotic scrub nurse assistant improves the surgical procedure by reducing the number of movements (lower variance in the picking position). The variance of the picking point is closely related to the concept of economy of movements in the operating room. Improving the effectiveness of the operating room can potentially enhance the safety of surgical interventions without affecting the performance time. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).\n
\n\n\n\n \n\n \n \n \n \n \n Intention, context and gesture recognition for sterile MRI navigation in the operating room.\n \n \n \n\n\n \n Jacob, M.; Cange, C.; Packer, R.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, pages 220-227, 2012. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Intention, context and gesture recognition for sterile MRI navigation in the operating room},\n type = {inproceedings},\n year = {2012},\n keywords = {Gesture recognition,human computer interaction,operating room},\n pages = {220-227},\n volume = {7441 LNCS},\n id = {b03bd1b9-5403-3e86-8149-95cc1b456b69},\n created = {2021-06-04T19:36:49.526Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.607Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2012b},\n folder_uuids = {46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {Human-Computer Interaction (HCI) devices such as the keyboard and the mouse are among the most contaminated regions in an operating room (OR). This paper proposes a sterile, intuitive HCI to navigate MRI images using freehand gestures. The system incorporates contextual cues and intent of the user to strengthen the gesture recognition process. Experimental results showed that while performing an image navigation task, mean intent recognition accuracy was 98.7% and that the false positive rate of gesture recognition dropped from 20.76% to 2.33% with context integration at similar recognition rates. © 2012 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Jacob, Mithun and Cange, Christopher and Packer, Rebecca and Wachs, J.P. Juan P.},\n doi = {10.1007/978-3-642-33275-3_27},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n Human-Computer Interaction (HCI) devices such as the keyboard and the mouse are among the most contaminated regions in an operating room (OR). This paper proposes a sterile, intuitive HCI to navigate MRI images using freehand gestures. The system incorporates contextual cues and intent of the user to strengthen the gesture recognition process. Experimental results showed that while performing an image navigation task, mean intent recognition accuracy was 98.7% and that the false positive rate of gesture recognition dropped from 20.76% to 2.33% with context integration at similar recognition rates. © 2012 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n Robot, pass me the scissors! How robots can assist us in the operating room.\n \n \n \n\n\n \n Wachs, J., J., P.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7441 LNCS, pages 46-57, 2012. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Robot, pass me the scissors! How robots can assist us in the operating room},\n type = {inproceedings},\n year = {2012},\n keywords = {Gesture recognition,human computer interaction,operating room},\n pages = {46-57},\n volume = {7441 LNCS},\n id = {166d2107-6a56-31c6-bf92-98c1b51bc958},\n created = {2021-06-04T19:36:50.047Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.205Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2012a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The inclusion of robotics and automation to support and augment surgical performance offers the promise of shorter operating times, higher accuracy and fewer risks compared with traditional, human-only surgery. This paper discusses current research in the area of surgical robotics and human-robot collaboration. A multimodal robotic scrub nurse (Gestonurse) for the operating room (OR) is presented as a case study. Gestonurse assists the main surgeon by passing surgical instruments, thereby releasing the surgical technician to perform other tasks. Such a robotic system has the potential to reduce miscommunication and compensate for understaffing. The implications of the introduction of surgical robots, as assistants rather than autonomous agents, are discussed in terms of the societal and technological requirements. Quantitative and qualitative findings are presented as evidence to support the guidelines discussed. © 2012 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Wachs, J.P. Juan P.},\n doi = {10.1007/978-3-642-33275-3_5},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n The inclusion of robotics and automation to support and augment surgical performance offers the promise of shorter operating times, higher accuracy and fewer risks compared with traditional, human-only surgery. This paper discusses current research in the area of surgical robotics and human-robot collaboration. A multimodal robotic scrub nurse (Gestonurse) for the operating room (OR) is presented as a case study. Gestonurse assists the main surgeon by passing surgical instruments, thereby releasing the surgical technician to perform other tasks. Such a robotic system has the potential to reduce miscommunication and compensate for understaffing. The implications of the introduction of surgical robots, as assistants rather than autonomous agents, are discussed in terms of the societal and technological requirements. Quantitative and qualitative findings are presented as evidence to support the guidelines discussed. © 2012 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n \n Gestonurse.\n \n \n \n \n\n\n \n Jacob, M., G.; Li, Y.; and Wachs, J., P.\n\n\n \n\n\n\n In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction - HRI '12, pages 153, 3 2012. ACM Press\n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {Gestonurse},\n type = {inproceedings},\n year = {2012},\n keywords = {gesture recognition,human-robot interaction,multimodal interfaces,robotic scrub nurse},\n pages = {153},\n websites = {http://dl.acm.org/citation.cfm?id=2157689.2157731},\n month = {3},\n publisher = {ACM Press},\n day = {5},\n city = {New York, New York, USA},\n id = {69e5e599-498b-3d52-9bd1-e5667913fa5c},\n created = {2021-06-04T19:36:51.933Z},\n accessed = {2014-09-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:09.290Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2012d},\n source_type = {inproceedings},\n folder_uuids = {6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Jacob, Mithun George and Li, Yu-Ting and Wachs, Juan P.},\n doi = {10.1145/2157689.2157731},\n booktitle = {Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction - HRI '12}\n}\n\n
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\n \n \n\n \n \n \n \n \n Vision-based hand-gesture applications.\n \n \n \n\n\n \n Wachs, J.; Kölsch, M.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n Communications of the ACM, 54(2). 2011.\n \n\n\n\n
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\n\n\n@article{\n title = {Vision-based hand-gesture applications},\n type = {article},\n year = {2011},\n volume = {54},\n id = {9a96f183-892f-38c0-9571-b93baea29b02},\n created = {2018-03-14T02:09:56.629Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2022-11-23T02:13:19.403Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Wachs2011a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing. © 2011 ACM.},\n bibtype = {article},\n author = {Wachs, J.P. and Kölsch, M. and Stern, H. and Edan, Y.},\n doi = {10.1145/1897816.1897838},\n journal = {Communications of the ACM},\n number = {2}\n}\n\n Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing. © 2011 ACM.\n
\n\n\n\n \n\n \n \n \n \n \n \n Vision-based hand-gesture applications.\n \n \n \n \n\n\n \n Wachs, J., J., J., P.; Kölsch, M.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n Communications of the ACM, 54(2): 60-71. 2 2011.\n \n\n\n\n
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\n\n\n@article{\n title = {Vision-based hand-gesture applications},\n type = {article},\n year = {2011},\n pages = {60-71},\n volume = {54},\n websites = {http://dl.acm.org/citation.cfm?id=1897838,http://portal.acm.org/citation.cfm?doid=1897816.1897838,https://dl.acm.org/doi/10.1145/1897816.1897838},\n month = {2},\n publisher = {ACM},\n day = {1},\n id = {e7f4fb77-dc5b-302e-8033-e52f8e731353},\n created = {2021-06-04T19:36:48.421Z},\n accessed = {2014-08-25},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.448Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2011d},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,0f5aa25e-250b-450c-a560-2626eae40317,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing. © 2011 ACM.},\n bibtype = {article},\n author = {Wachs, J.P. JP Juan Pablo and Kölsch, Mathias and Stern, Helman and Edan, Yael},\n doi = {10.1145/1897816.1897838},\n journal = {Communications of the ACM},\n number = {2}\n}\n\n Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing. © 2011 ACM.\n
\n\n\n\n \n\n \n \n \n \n \n \n A gesture driven robotic scrub nurse.\n \n \n \n \n\n\n \n Jacob, M., G., M.; Li, Y., T.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pages 2039-2044, 10 2011. IEEE\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A gesture driven robotic scrub nurse},\n type = {inproceedings},\n year = {2011},\n keywords = {Accuracy,GRSN,Gesture recognition,Grippers,Humans,Instruments,Robots,Surgery,gesture driven robotic scrub nurse,gesture recognition,hand gesture recognition algorithm,health care,healthcare robotics,human robot interaction,human scrub nurse,human-robot interaction,medical robotics,operating room,robotic scrub nurse,surgery,surgical instruments,surgical robotics,video stream,video streaming,workload reduction},\n pages = {2039-2044},\n websites = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6083972},\n month = {10},\n publisher = {IEEE},\n id = {1c8d196d-a764-388b-ad49-249e64a315f7},\n created = {2021-06-04T19:36:48.990Z},\n accessed = {2014-10-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.063Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Jacob2011a},\n language = {English},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {A gesture driven robotic scrub nurse (GRSN) for the operating room (OR) is presented. The GRSN passes surgical instruments to the surgeon during surgery which reduces the workload of a human scrub nurse. This system offers several advantages such as freeing human nurses to perform concurrent tasks, and reducing errors in the OR due to miscommunication or absence of surgical staff. Hand gestures are recognized from a video stream, converted to instructions, and sent to a robotic arm which passes the required surgical instruments to the surgeon. Experimental results show that 95% of the gestures were recognized correctly. The gesture recognition algorithm presented is robust to changes in scale and rotation of the hand gestures. The system was compared to human task performance and was found to be only 0.83 seconds slower on average. © 2011 IEEE.},\n bibtype = {inproceedings},\n author = {Jacob, Mithun George M.G. and Li, Yu-Ting Ting and Wachs, J.P. Juan P.},\n doi = {10.1109/ICSMC.2011.6083972},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n A gesture driven robotic scrub nurse (GRSN) for the operating room (OR) is presented. The GRSN passes surgical instruments to the surgeon during surgery which reduces the workload of a human scrub nurse. This system offers several advantages such as freeing human nurses to perform concurrent tasks, and reducing errors in the OR due to miscommunication or absence of surgical staff. Hand gestures are recognized from a video stream, converted to instructions, and sent to a robotic arm which passes the required surgical instruments to the surgeon. Experimental results show that 95% of the gestures were recognized correctly. The gesture recognition algorithm presented is robust to changes in scale and rotation of the hand gestures. The system was compared to human task performance and was found to be only 0.83 seconds slower on average. © 2011 IEEE.\n
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\n \n \n\n \n \n \n \n \n \n Gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system.\n \n \n \n \n\n\n \n Wachs, J.\n\n\n \n\n\n\n International journal of computers,communications & control, 5(1): 106-124. 2010.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@article{\n title = {Gaze, posture and gesture recognition to minimize focus shifts for intelligent operating rooms in a collaborative support system},\n type = {article},\n year = {2010},\n pages = {106-124},\n volume = {5},\n websites = {http://www.journal.univagora.ro/download/pdf/396.pdf},\n id = {6e525642-6c9b-3db3-96fe-a31f027aad91},\n created = {2014-08-25T15:31:51.000Z},\n accessed = {2014-08-25},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:46.699Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2010e},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n bibtype = {article},\n author = {Wachs, JP},\n journal = {International journal of computers,communications & control},\n number = {1}\n}\n\n \n\n \n \n \n \n \n Low and high-level visual feature-based apple detection from multi-modal images.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; Burks, T.; and Alchanatis, V.\n\n\n \n\n\n\n Precision Agriculture, 11(6). 2010.\n \n\n\n\n
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\n\n\n@article{\n title = {Low and high-level visual feature-based apple detection from multi-modal images},\n type = {article},\n year = {2010},\n keywords = {Apple detection,Haar detector,Multi-modal registration,Mutual information,Sensor fusion},\n volume = {11},\n id = {7a5d8951-0c35-305e-abb1-2d0a2851f0e6},\n created = {2018-03-14T02:09:55.911Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:13:11.654Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Wachs2010c},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of "green" apples within scenes of "green leaves", shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased. © 2010 Springer Science+Business Media, LLC.},\n bibtype = {article},\n author = {Wachs, J.P. and Stern, H.I. and Burks, T. and Alchanatis, V.},\n doi = {10.1007/s11119-010-9198-x},\n journal = {Precision Agriculture},\n number = {6}\n}\n\n Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of \"green\" apples within scenes of \"green leaves\", shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased. © 2010 Springer Science+Business Media, LLC.\n
\n\n\n\n \n\n \n \n \n \n \n \n \"A window on tissue\" - Using facial orientation to control endoscopic views of tissue depth.\n \n \n \n \n\n\n \n Wachs, J., P.; Vujjeni, K.; Matson, E., T.; and Adams, S.\n\n\n \n\n\n\n In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pages 935-938, 8 2010. IEEE\n \n\n\n\n
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\n\n\n\n@inproceedings{\n title = {"A window on tissue" - Using facial orientation to control endoscopic views of tissue depth},\n type = {inproceedings},\n year = {2010},\n pages = {935-938},\n websites = {http://ieeexplore.ieee.org/document/5627538/},\n month = {8},\n publisher = {IEEE},\n id = {e13c707c-920b-36d0-a68e-e40a4ae8cd89},\n created = {2020-09-01T01:39:12.357Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-17T19:13:08.264Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Wachs, J P and Vujjeni, K. and Matson, E T and Adams, S.},\n doi = {10.1109/IEMBS.2010.5627538},\n booktitle = {2010 Annual International Conference of the IEEE Engineering in Medicine and Biology}\n}\n\n \n\n \n \n \n \n \n \"A window on tissue\" - Using facial orientation to control endoscopic views of tissue depth.\n \n \n \n\n\n \n Wachs, J., P.; Vujjeni, K.; Matson, E., T.; and Adams, S.\n\n\n \n\n\n\n In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, pages 935-938, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {"A window on tissue" - Using facial orientation to control endoscopic views of tissue depth},\n type = {inproceedings},\n year = {2010},\n pages = {935-938},\n id = {a43a7ddf-6800-3fd2-8c38-6cbaaa51af8e},\n created = {2021-06-04T19:22:45.676Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.344Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {An endoscope is an invaluable tool to interpret conditions within a body. Flexible endoscopes are controlled by a set of rotational knobs requiring a doctor's hands to guide and locate the view. This research uses a combination of a camera, facial recognition techniques and software to create a handsfree gesture recognition application for use by a physician to probe the internals of a human body. The physician will utilize the head movements to move the endoscopic camera freeing their hands to perform a procedure or other functions.},\n bibtype = {inproceedings},\n author = {Wachs, Juan P. and Vujjeni, Kausheek and Matson, Eric T. and Adams, Stephen},\n doi = {10.1109/IEMBS.2010.5627538},\n booktitle = {2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10}\n}\n\n An endoscope is an invaluable tool to interpret conditions within a body. Flexible endoscopes are controlled by a set of rotational knobs requiring a doctor's hands to guide and locate the view. This research uses a combination of a camera, facial recognition techniques and software to create a handsfree gesture recognition application for use by a physician to probe the internals of a human body. The physician will utilize the head movements to move the endoscopic camera freeing their hands to perform a procedure or other functions.\n
\n\n\n\n \n\n \n \n \n \n \n Using autonomous robots to enable self-organizing broadband networks.\n \n \n \n\n\n \n Matson, E., T., E.; Leong, B.; Nguyen, C., Q., C.; Smith, A.; and Wachs, J., J., P.\n\n\n \n\n\n\n In ICCAS 2010 - International Conference on Control, Automation and Systems, pages 605-610, 2010. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Using autonomous robots to enable self-organizing broadband networks},\n type = {inproceedings},\n year = {2010},\n keywords = {Agent organizations,Autonomous robots,Multiagent systems,Self-organization,Wireless broadband},\n pages = {605-610},\n id = {247d6dde-e69b-35a7-932d-848c741e6cb0},\n created = {2021-06-04T19:36:48.837Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.865Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Matson2010},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Broadband is a ubiquitous technology for connecting people together via a basic communications medium. Incorporating mobility into broadband antennas provides for the creation of movable nodes which can seek out other nodes. In contrast, stationary broadband nodes are incapable of finding new nodes if they are out of range or have an impediment. Supplying a broadband node with mobility and the capability to adapt to its environment, as well as, to other broadband nodes, is the central aim of this effort. This research enables self-organizing mobile broadband networks with the integration of broadband radios, autonomous robotic platforms and multiagent organizations. The broadband network acts as an infrastructure for external users and connects all robotic instances. The integration of these technologies furthers the ability to communicate where mobility and adaptation are critical. ©ICROS.},\n bibtype = {inproceedings},\n author = {Matson, Eric T. E.T. and Leong, Benny and Nguyen, Cory Q. C.Q. and Smith, Anthony and Wachs, J.P. Juan P.},\n doi = {10.1109/iccas.2010.5669825},\n booktitle = {ICCAS 2010 - International Conference on Control, Automation and Systems}\n}\n\n Broadband is a ubiquitous technology for connecting people together via a basic communications medium. Incorporating mobility into broadband antennas provides for the creation of movable nodes which can seek out other nodes. In contrast, stationary broadband nodes are incapable of finding new nodes if they are out of range or have an impediment. Supplying a broadband node with mobility and the capability to adapt to its environment, as well as, to other broadband nodes, is the central aim of this effort. This research enables self-organizing mobile broadband networks with the integration of broadband radios, autonomous robotic platforms and multiagent organizations. The broadband network acts as an infrastructure for external users and connects all robotic instances. The integration of these technologies furthers the ability to communicate where mobility and adaptation are critical. ©ICROS.\n
\n\n\n\n \n\n \n \n \n \n \n AWARE: Autonomous wireless agent robotic exchange.\n \n \n \n\n\n \n Nguyen, C., Q., C.; Leong, B.; Matson, E., T., E.; Smith, A.; and Wachs, J., J., P.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6424 LNAI, pages 276-287, 2010. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {AWARE: Autonomous wireless agent robotic exchange},\n type = {inproceedings},\n year = {2010},\n pages = {276-287},\n volume = {6424 LNAI},\n issue = {PART 1},\n id = {1e4d7b9f-0310-3ac4-a1dc-3631027b36d6},\n created = {2021-06-04T19:36:49.599Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.637Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Nguyen2010},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {The Autonomous Wireless Robotic Agent Exchange (AWARE) system revolves around autonomous self-organizing wireless networks that provide end-to-end communication via a wireless medium. It provides an immediate communication solution to disaster stricken regions and to inaccessible geographical areas where wireless communication is the only viable option,but most current systems suffer due to their static nature. AWARE has many real-world applications related to search and rescue, disaster recovery, military and commercial industries. © 2010 Springer-Verlag.},\n bibtype = {inproceedings},\n author = {Nguyen, Cory Q. C.Q. and Leong, Benny and Matson, Eric T. E.T. and Smith, Anthony and Wachs, J.P. Juan P.},\n doi = {10.1007/978-3-642-16584-9_26},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n The Autonomous Wireless Robotic Agent Exchange (AWARE) system revolves around autonomous self-organizing wireless networks that provide end-to-end communication via a wireless medium. It provides an immediate communication solution to disaster stricken regions and to inaccessible geographical areas where wireless communication is the only viable option,but most current systems suffer due to their static nature. AWARE has many real-world applications related to search and rescue, disaster recovery, military and commercial industries. © 2010 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n \n Human posture recognition for intelligent vehicles.\n \n \n \n \n\n\n \n Wachs, J., J., P.; Kölsch, M.; and Goshorn, D.\n\n\n \n\n\n\n Journal of Real-Time Image Processing, 5(4): 231-244. 2 2010.\n \n\n\n\n
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\n\n\n@article{\n title = {Human posture recognition for intelligent vehicles},\n type = {article},\n year = {2010},\n keywords = {Articulated body posture recognition,Computer vision,Error correction,Gesture recognition,Pose detection,Syntactical behavior classifiers},\n pages = {231-244},\n volume = {5},\n websites = {http://link.springer.com/10.1007/s11554-010-0150-0},\n month = {2},\n day = {12},\n id = {81edf8fb-fe25-3fc5-b420-e7164ce92d2d},\n created = {2021-06-04T19:36:50.173Z},\n accessed = {2014-10-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.342Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2010d},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Pedestrian detection systems are finding their way into many modern "intelligent" vehicles. The body posture could reveal further insight about the pedestrian's intent and her awareness of the oncoming car. This article details the algorithms and implementation of a library for real-time body posture recognition. It requires prior person detection and then calculates overall stance, torso orientation in four increments, and head location and orientation, all based on individual frames. A syntactic post-processing module takes temporal information into account and smoothes the results over time while correcting improbable configurations. We show accuracy and timing measurements for the library and its utilization in a training application. © 2010 Springer-Verlag.},\n bibtype = {article},\n author = {Wachs, J.P. Juan P. and Kölsch, Mathias and Goshorn, Deborah},\n doi = {10.1007/s11554-010-0150-0},\n journal = {Journal of Real-Time Image Processing},\n number = {4}\n}\n\n Pedestrian detection systems are finding their way into many modern \"intelligent\" vehicles. The body posture could reveal further insight about the pedestrian's intent and her awareness of the oncoming car. This article details the algorithms and implementation of a library for real-time body posture recognition. It requires prior person detection and then calculates overall stance, torso orientation in four increments, and head location and orientation, all based on individual frames. A syntactic post-processing module takes temporal information into account and smoothes the results over time while correcting improbable configurations. We show accuracy and timing measurements for the library and its utilization in a training application. © 2010 Springer-Verlag.\n
\n\n\n\n \n\n \n \n \n \n \n An Analytical Framework to Measure Effective Human Machine Interaction.\n \n \n \n\n\n \n Wachs, J.; and Duerstock, B.\n\n\n \n\n\n\n pages 611-621. 2010.\n \n\n\n\n
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\n\n\n\n@inbook{\n type = {inbook},\n year = {2010},\n pages = {611-621},\n id = {32ac5b55-3718-3384-9d2a-7e1973541319},\n created = {2021-06-04T19:36:52.126Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:03.155Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {inbook},\n author = {Wachs, Juan and Duerstock, Brad},\n doi = {10.1201/ebk1439834978-c68},\n chapter = {An Analytical Framework to Measure Effective Human Machine Interaction}\n}\n\n
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\n \n \n\n \n \n \n \n \n Apple detection in natural tree canopies from multimodal images.\n \n \n \n\n\n \n Wachs, J.; Stern, H., I.; Burks, T.; Alchanatis, V.; and Bet-Dagan, I.\n\n\n \n\n\n\n In Proceeding of the Citrus fruit identification and size determination using machine vision and ultrasonic sensors JIAC, pages 293-302, 2009. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Apple detection in natural tree canopies from multimodal images},\n type = {inproceedings},\n year = {2009},\n pages = {293-302},\n id = {df45e5cb-8ffe-3658-b949-0ad2027c2d93},\n created = {2014-08-12T15:59:23.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:26.446Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2009b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this work we develop a real time system that recognizes occluded green apples within a tree canopy using infra-red and color images in order to achieve automated harvesting. Infra-red provides clues regarding the physical structure and location of the apples based on their temperature (leaves accumulate less heat and radiate faster than apples), while color images provide evidence of circular shape. Initially the optimal registration parameters are obtained using maximization of mutual information. Haar features are then applied separately to color and infra-red images through a process called Boosting, to detect apples from the background. A contribution reported in this work, is the voting scheme added to the output of the RGB Haar detector which reduces false alarms without affecting the recognition rate. The resulting classifiers alone can partially recognize the on-trees apples however when combined together the recognition accuracy is increased.},\n bibtype = {inproceedings},\n author = {Wachs, J and Stern, H I and Burks, T and Alchanatis, V and Bet-Dagan, Israel},\n booktitle = {Proceeding of the Citrus fruit identification and size determination using machine vision and ultrasonic sensors JIAC}\n}\n\n In this work we develop a real time system that recognizes occluded green apples within a tree canopy using infra-red and color images in order to achieve automated harvesting. Infra-red provides clues regarding the physical structure and location of the apples based on their temperature (leaves accumulate less heat and radiate faster than apples), while color images provide evidence of circular shape. Initially the optimal registration parameters are obtained using maximization of mutual information. Haar features are then applied separately to color and infra-red images through a process called Boosting, to detect apples from the background. A contribution reported in this work, is the voting scheme added to the output of the RGB Haar detector which reduces false alarms without affecting the recognition rate. The resulting classifiers alone can partially recognize the on-trees apples however when combined together the recognition accuracy is increased.\n
\n\n\n\n \n\n \n \n \n \n \n Recognizing human postures and poses in monocular still images.\n \n \n \n\n\n \n Wachs, J.; Goshorn, D.; and Kölsch, M.\n\n\n \n\n\n\n In Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, volume 2, 2009. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Recognizing human postures and poses in monocular still images},\n type = {inproceedings},\n year = {2009},\n keywords = {Adaboost,Multi-class detectors,Part-based detectors,Pose detection,Posture recognition},\n volume = {2},\n id = {43b5cc37-7de6-33ac-9968-ec3a34d55f2f},\n created = {2018-03-14T02:09:56.647Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:30.723Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n citation_key = {Wachs2009},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, person detection with simultaneous or subsequent human body posture recognition is achieved using parts-based models, since the search space for typical poses is much smaller than the kinematics space. Posture recovery is carried out by detecting the human body, its posture and orientation at the same time. Since features of different human postures can be expected to have some shared subspace against the non-person class, detection and classification simultaneously is tenable. Contrary to many related efforts, we focus on postures that cannot be easily distinguished after segmentation by their aspect ratio or silhouette, but rather require a texture-based feature vector. The approaches presented do not rely on explicit models nor on labeling individual body parts. Both the detection and classification are performed in one pass on the image, where the score of the detection is an ensemble of votes from parts patches.},\n bibtype = {inproceedings},\n author = {Wachs, J.P. and Goshorn, D. and Kölsch, M.},\n booktitle = {Proceedings of the 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009}\n}\n\n In this paper, person detection with simultaneous or subsequent human body posture recognition is achieved using parts-based models, since the search space for typical poses is much smaller than the kinematics space. Posture recovery is carried out by detecting the human body, its posture and orientation at the same time. Since features of different human postures can be expected to have some shared subspace against the non-person class, detection and classification simultaneously is tenable. Contrary to many related efforts, we focus on postures that cannot be easily distinguished after segmentation by their aspect ratio or silhouette, but rather require a texture-based feature vector. The approaches presented do not rely on explicit models nor on labeling individual body parts. Both the detection and classification are performed in one pass on the image, where the score of the detection is an ensemble of votes from parts patches.\n
\n\n\n\n \n\n \n \n \n \n \n \n A method for selection of optimal hand gesture vocabularies.\n \n \n \n \n\n\n \n Stern, H.; Wachs, J.; and Edan, Y.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5085 LNAI, pages 57-68, 2009. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A method for selection of optimal hand gesture vocabularies},\n type = {inproceedings},\n year = {2009},\n keywords = {Feature selection,Fuzzy c-means,Gesture interfaces,Hand gesture recognition,Hand gesture vocabulary design,Human-computer interaction,Multiobjective optimization},\n pages = {57-68},\n volume = {5085 LNAI},\n websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-60349087343&partnerID=40&md5=5321791f020c3c701ceb6d3950db7a96},\n id = {6eddae2e-5a46-3d4f-8304-6f80bd553983},\n created = {2021-06-04T19:36:49.112Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.186Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2009},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This work presents an analytical approach to design a gesture vocabulary (GV) using multiobjectives for psycho-physiological and gesture recognition factors. Previous works dealt only with selection of hand gestures vocabularies using rule based or ad-hoc methods. The analytical formulation in our research is a demonstration of the future need defined by previous research. A meta-heuristic approach is taken by decomposing the problem into two sub-problems: (i) finding the subsets of gestures that meet a minimal accuracy requirement, and (ii) matching gestures to commands to maximize the human factors objective. The result is a set of solutions from which a Pareto optimal subset is selected. An example solution from the Pareto set is exhibited using prioritized objectives. © 2009 Springer Berlin Heidelberg.},\n bibtype = {inproceedings},\n author = {Stern, Helman and Wachs, Juan and Edan, Yael},\n doi = {10.1007/978-3-540-92865-2_6},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n This work presents an analytical approach to design a gesture vocabulary (GV) using multiobjectives for psycho-physiological and gesture recognition factors. Previous works dealt only with selection of hand gestures vocabularies using rule based or ad-hoc methods. The analytical formulation in our research is a demonstration of the future need defined by previous research. A meta-heuristic approach is taken by decomposing the problem into two sub-problems: (i) finding the subsets of gestures that meet a minimal accuracy requirement, and (ii) matching gestures to commands to maximize the human factors objective. The result is a set of solutions from which a Pareto optimal subset is selected. An example solution from the Pareto set is exhibited using prioritized objectives. © 2009 Springer Berlin Heidelberg.\n
\n\n\n\n \n\n \n \n \n \n \n Multi-modal registration using a combined similarity measure.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; Burks, T.; and Alchanatis, V.\n\n\n \n\n\n\n Advances in Soft Computing, 52: 159-168. 2009.\n \n\n\n\n
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\n\n\n@article{\n title = {Multi-modal registration using a combined similarity measure},\n type = {article},\n year = {2009},\n keywords = {Multi-modal registration,Mutual information,Sensor fusion,Similarity measures},\n pages = {159-168},\n volume = {52},\n id = {aadeb2a2-bf0d-38c9-9979-988b06843d96},\n created = {2021-06-04T19:36:49.561Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.622Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2009a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper we compare similarity measures used for multi-modal registration, and suggest an approach that combines those measures in a way that the registration parameters are weighted according to the strength of each measure. The measures used are: (1) cross correlation normalized, (2) correlation coefficient, (3) correlation coefficient normalized, (4) the Bhattacharyya coefficient, and (5) the mutual information index. The approach is tested on fruit tree registration using multiple sensors (RGB and infra-red). The combination method finds the optimal transformation parameters for each new pair of images to be registered. The method uses a convex linear combination of weighted similarity measures in its objective function. In the future, we plan to use this methodology for an on-tree fruit recognition system in the scope of robotic fruit picking. © 2009 Springer-Verlag Berlin Heidelberg.},\n bibtype = {article},\n author = {Wachs, Juan and Stern, Helman and Burks, Tom and Alchanatis, Victor},\n doi = {10.1007/978-3-540-88079-0_16},\n journal = {Advances in Soft Computing}\n}\n\n In this paper we compare similarity measures used for multi-modal registration, and suggest an approach that combines those measures in a way that the registration parameters are weighted according to the strength of each measure. The measures used are: (1) cross correlation normalized, (2) correlation coefficient, (3) correlation coefficient normalized, (4) the Bhattacharyya coefficient, and (5) the mutual information index. The approach is tested on fruit tree registration using multiple sensors (RGB and infra-red). The combination method finds the optimal transformation parameters for each new pair of images to be registered. The method uses a convex linear combination of weighted similarity measures in its objective function. In the future, we plan to use this methodology for an on-tree fruit recognition system in the scope of robotic fruit picking. © 2009 Springer-Verlag Berlin Heidelberg.\n
\n\n\n\n \n\n \n \n \n \n \n The multi-level learning and classification of multi-class parts-based representations of U.S. Marine postures.\n \n \n \n\n\n \n Goshorn, D.; Wachs, J.; and Kölsch, M.\n\n\n \n\n\n\n In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5856 LNCS, pages 505-512, 2009. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {The multi-level learning and classification of multi-class parts-based representations of U.S. Marine postures},\n type = {inproceedings},\n year = {2009},\n pages = {505-512},\n volume = {5856 LNCS},\n id = {4a0b22a7-29a4-3190-ba8a-ebb48abab9eb},\n created = {2021-06-04T19:36:49.709Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.816Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Goshorn2009},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises. To do so, the paper discusses two approaches to learning parts-based representations for each posture needed. The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric spatial frequency distribution of the clusters that represents one posture type. The second approach uses a two-level learning method which involves convolving interest patches with filters and in addition performing joint boosting on the spatial locations of the first level of learned parts in order to create a global set of parts that the various postures share in representation. Experimental results on video from actual US Marine training exercises are included. © 2009 Springer-Verlag Berlin Heidelberg.},\n bibtype = {inproceedings},\n author = {Goshorn, Deborah and Wachs, Juan and Kölsch, Mathias},\n doi = {10.1007/978-3-642-10268-4_59},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}\n\n This paper primarily investigates the possibility of using multi-level learning of sparse parts-based representations of US Marine postures in an outside and often crowded environment for training exercises. To do so, the paper discusses two approaches to learning parts-based representations for each posture needed. The first approach uses a two-level learning method which consists of simple clustering of interest patches extracted from a set of training images for each posture, in addition to learning the nonparametric spatial frequency distribution of the clusters that represents one posture type. The second approach uses a two-level learning method which involves convolving interest patches with filters and in addition performing joint boosting on the spatial locations of the first level of learned parts in order to create a global set of parts that the various postures share in representation. Experimental results on video from actual US Marine training exercises are included. © 2009 Springer-Verlag Berlin Heidelberg.\n
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\n \n \n\n \n \n \n \n \n A Holistic Framework for Hand Gestures Design.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n 2008.\n \n\n\n\n
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\n\n\n@misc{\n title = {A Holistic Framework for Hand Gestures Design},\n type = {misc},\n year = {2008},\n source = {2nd Annual Visual and Iconic Language Conference},\n pages = {pp. 24-34},\n id = {25b65914-4f78-3eca-afd7-10e95fc056dd},\n created = {2014-08-12T15:59:21.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:50.624Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2008d},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Hand gesture based interfaces are a proliferating area for immersive and augmented reality systems due to the rich interaction provided by this type of modality. Even though proper design of such interfaces requires accurate recognition, usability, ergonomic design and comfort. In most of the interfaces being developed the primary focus is on accurate gesture recognition.},\n bibtype = {misc},\n author = {Wachs, J and Stern, H and Edan, Y}\n}\n\n Hand gesture based interfaces are a proliferating area for immersive and augmented reality systems due to the rich interaction provided by this type of modality. Even though proper design of such interfaces requires accurate recognition, usability, ergonomic design and comfort. In most of the interfaces being developed the primary focus is on accurate gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n \n Designing hand gesture vocabularies for natural interactions by combining psychi-psysiological and recognition factors.\n \n \n \n \n\n\n \n Stern, H., I.; Wachs, J., P.; and Edan, Y.\n\n\n \n\n\n\n International Journal, 2: 137-160. 2008.\n \n\n\n\n
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\n\n\n@article{\n title = {Designing hand gesture vocabularies for natural interactions by combining psychi-psysiological and recognition factors},\n type = {article},\n year = {2008},\n keywords = {faces,gesture inter,hand gesture recognition,hand gesture vocabulary design,human computer interaction,multiobjective optimization,psycho,semantic behavior},\n pages = {137-160},\n volume = {2},\n websites = {http://www.worldscinet.com/abstract?id=pii:S1793351X08000385},\n id = {60d8eb1b-c20e-346f-877e-301e0c1e7a1f},\n created = {2014-10-29T22:40:28.000Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:09.236Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2008b},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {A need exists for intuitive hand gesture machine interaction in which the machine not only recognizes gestures, but also the human feels comfortable and natural in their execu- tion. The gesture vocabulary design problem is rigorously formulated as amulti-objective optimization problem. Psycho-physiological measures (intuitiveness, comfort) and ges- ture recognition accuracy are taken as the multi-objective factors. The hand gestures are static and recognized by a vision based fuzzy c-means classifier. A meta-heuristic approach decomposes the problem into two sub-problems: finding the subsets of gestures that meet a minimal accuracy requirement, and matching gestures to commands to max- imize the human factors objective. The result is a set of Pareto optimal solutions in which no objective may be increased without a concomitant decrease in another. Several solu- tions from the Pareto set are selected by the user using prioritized objectives. Software programs are developed to automate the collection of intuitive and stress indices. The method is tested for a simulated car maze navigation task. Validation tests were con- ducted to substantiate the claimthat solutions that maximize intuitiveness, comfort, and recognition accuracy performance measures can be used as proxies for the minimization task time objective. Learning and memorability were also tested.},\n bibtype = {article},\n author = {Stern, Helman I and Wachs, Juan P and Edan, Yael},\n doi = {10.1142/S1793351X08000385},\n journal = {International Journal}\n}\n\n A need exists for intuitive hand gesture machine interaction in which the machine not only recognizes gestures, but also the human feels comfortable and natural in their execu- tion. The gesture vocabulary design problem is rigorously formulated as amulti-objective optimization problem. Psycho-physiological measures (intuitiveness, comfort) and ges- ture recognition accuracy are taken as the multi-objective factors. The hand gestures are static and recognized by a vision based fuzzy c-means classifier. A meta-heuristic approach decomposes the problem into two sub-problems: finding the subsets of gestures that meet a minimal accuracy requirement, and matching gestures to commands to max- imize the human factors objective. The result is a set of Pareto optimal solutions in which no objective may be increased without a concomitant decrease in another. Several solu- tions from the Pareto set are selected by the user using prioritized objectives. Software programs are developed to automate the collection of intuitive and stress indices. The method is tested for a simulated car maze navigation task. Validation tests were con- ducted to substantiate the claimthat solutions that maximize intuitiveness, comfort, and recognition accuracy performance measures can be used as proxies for the minimization task time objective. Learning and memorability were also tested.\n
\n\n\n\n \n\n \n \n \n \n \n \n A Real-Time Hand Gesture Interface for a Medical Image Guided System.\n \n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; and Feied, C.\n\n\n \n\n\n\n International Journal of Intelligent Computing in Medical Sciences and Image Processing, 1(3): 175-185. 2008.\n \n\n\n\n
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\n\n\n\n@article{\n title = {A Real-Time Hand Gesture Interface for a Medical Image Guided System},\n type = {article},\n year = {2008},\n pages = {175-185},\n volume = {1},\n websites = {http://scholar.google.com/scholar?q=Wachs+A+Real-Time+Hand+Gesture+Interface+for+a+Medical+Image+Guided+System&btnG=&hl=en&as_sdt=0%2C15#6},\n id = {96ec721f-b761-392a-a4f8-e040108bcde4},\n created = {2014-10-29T22:56:22.000Z},\n accessed = {2014-10-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:12:15.231Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2008},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n bibtype = {article},\n author = {Wachs, J and Stern, H and Edan, Y and Gillam, M and Feied, C},\n journal = {International Journal of Intelligent Computing in Medical Sciences and Image Processing},\n number = {3}\n}\n\n \n\n \n \n \n \n \n \n Optimal consensus intuitive hand gesture vocabulary design.\n \n \n \n \n\n\n \n Stem, H., H., I.; Wachs, J., J., J., P.; Edan, Y.; Stern, H., I.; Wachs, J., J., J., P.; Edan, Y.; Stem, H., H., I.; Wachs, J., J., J., P.; and Edan, Y.\n\n\n \n\n\n\n In Proceedings - IEEE International Conference on Semantic Computing 2008, ICSC 2008, pages 96-103, 8 2008. \n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Optimal consensus intuitive hand gesture vocabulary design},\n type = {inproceedings},\n year = {2008},\n pages = {96-103},\n websites = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4597179},\n month = {8},\n id = {68724326-3ca9-3e2f-95d3-282ca440ea70},\n created = {2021-06-04T19:36:47.179Z},\n accessed = {2014-11-29},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.179Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2008a},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Gesture interfaces are needed for natural intuitive communication with machine devices. Hand gesture intuitiveness is the cognitive association between a command or intent, and its physical gestural expression. Using an automated tool we quantified intuitive indices for static gesture commands for a car navigation task. A small number of gestures were selected to express most of the commands with 1/3 used only by single individuals. This followed a power function analogous to Zipf's Law for languages. We found gesture preferences to be highly individualized, providing evidence to refute the hypothesis of the universality of gestures. A mathematical program was formulated to obtain a consensus gesture vocabulary for a car navigation system with the objective of maximizing total intuitiveness. We also introduced the notion of complex consensus gesture vocabularies in which multi-gestures are associated with single commands and multi-commands are associated with single gestures. We recommend hybrid gesture vocabularies, decided by consensus with several gestures selected individually. © 2008 IEEE.},\n bibtype = {inproceedings},\n author = {Stem, H.I. Helman I. and Wachs, J.P. JP Juan P. and Edan, Yael and Stern, HI I. and Wachs, J.P. JP Juan P. and Edan, Yael and Stem, H.I. Helman I. and Wachs, J.P. JP Juan P. and Edan, Yael},\n doi = {10.1109/ICSC.2008.29},\n booktitle = {Proceedings - IEEE International Conference on Semantic Computing 2008, ICSC 2008}\n}\n\n Gesture interfaces are needed for natural intuitive communication with machine devices. Hand gesture intuitiveness is the cognitive association between a command or intent, and its physical gestural expression. Using an automated tool we quantified intuitive indices for static gesture commands for a car navigation task. A small number of gestures were selected to express most of the commands with 1/3 used only by single individuals. This followed a power function analogous to Zipf's Law for languages. We found gesture preferences to be highly individualized, providing evidence to refute the hypothesis of the universality of gestures. A mathematical program was formulated to obtain a consensus gesture vocabulary for a car navigation system with the objective of maximizing total intuitiveness. We also introduced the notion of complex consensus gesture vocabularies in which multi-gestures are associated with single commands and multi-commands are associated with single gestures. We recommend hybrid gesture vocabularies, decided by consensus with several gestures selected individually. © 2008 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n \n A gesture-based tool for sterile browsing of radiology images.\n \n \n \n \n\n\n \n Wachs, J., P.; Stern, H., I.; Edan, Y.; Gillam, M.; Handler, J.; Feied, C.; and Smith, M.\n\n\n \n\n\n\n Journal of the American Medical Informatics Association : JAMIA, 15(3): 321-3. 2008.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A gesture-based tool for sterile browsing of radiology images.},\n type = {article},\n year = {2008},\n keywords = {Computerized,Consumer Satisfaction,Equipment Contamination,Equipment Contamination: prevention & control,Gestures,Humans,Imaging,Magnetic Resonance Imaging,Man-Machine Systems,Medical Records Systems,Neurosurgery,Neurosurgery: instrumentation,Radiology,Radiology Information Systems,Radiology: instrumentation,Three-Dimensional,User-Computer Interface},\n pages = {321-3},\n volume = {15},\n websites = {http://jamia.bmj.com/content/15/3/321.short,http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2410001&tool=pmcentrez&rendertype=abstract},\n id = {ca23fa15-651f-3f40-b5a5-45b3f5fbf690},\n created = {2021-06-04T19:36:50.821Z},\n accessed = {2014-08-25},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.944Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2008a},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,46c7f883-fd91-49c9-a15c-94741f9ecd8c,b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80,32e8a974-a5fd-4cd0-a2f7-95e0c6f9d4b1},\n private_publication = {false},\n abstract = {The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents "Gestix," a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. "Gestix" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities.},\n bibtype = {article},\n author = {Wachs, Juan Pablo and Stern, Helman I. and Edan, Yael and Gillam, Michael and Handler, Jon and Feied, Craig and Smith, Mark},\n doi = {10.1197/jamia.M241},\n journal = {Journal of the American Medical Informatics Association : JAMIA},\n number = {3}\n}\n\n The use of doctor-computer interaction devices in the operation room (OR) requires new modalities that support medical imaging manipulation while allowing doctors' hands to remain sterile, supporting their focus of attention, and providing fast response times. This paper presents \"Gestix,\" a vision-based hand gesture capture and recognition system that interprets in real-time the user's gestures for navigation and manipulation of images in an electronic medical record (EMR) database. Navigation and other gestures are translated to commands based on their temporal trajectories, through video capture. \"Gestix\" was tested during a brain biopsy procedure. In the in vivo experiment, this interface prevented the surgeon's focus shift and change of location while achieving a rapid intuitive reaction and easy interaction. Data from two usability tests provide insights and implications regarding human-computer interaction based on nonverbal conversational modalities.\n
\n\n\n\n \n\n \n \n \n \n \n \n Real-time hand gesture interface for browsing medical images.\n \n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; Feied, C.; Smithd, M.; and Handler, J.\n\n\n \n\n\n\n International Journal of Intelligent Computing in Medical Sciences & Image Processing, 2(1): 15-25. 1 2008.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Real-time hand gesture interface for browsing medical images},\n type = {article},\n year = {2008},\n keywords = {Browsing,Hand gesture recognition,Medical databases,Sterile interface,image visualization},\n pages = {15-25},\n volume = {2},\n websites = {http://www.tandfonline.com/doi/abs/10.1080/1931308X.2008.10644149},\n month = {1},\n id = {c43271b8-7d64-3509-adb7-54b38d27ecad},\n created = {2021-06-04T19:36:50.988Z},\n accessed = {2014-08-25},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.081Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2008b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {A gesture interface is developed for users, such as doctors/surgeons, to browse medical images in a sterile medical environment. Avision-based gesture capture system interprets user's gestures in real-time to manipulate objects in an image visualization environment. A color distribution model of the gamut of colors of the users hand or glove is built at the start of each session resulting in an independent system. The gesture system relies on real-time robust tracking of the user's hand based on acolor-motion fusion model, in which the relative weight applied to the motion and color cues are adaptively determined according to the state of the system. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. A state machine switches between other gestures such as zoom and rotate, as well as a sleep state. Performance evaluation included gesture recognition accuracy, task learning, and rotation accuracy. Fast task learning rates were found with convergence after ten trials. A beta test of a system prototype was conducted during a live brain biopsy operation, where neurosurgeons were able to browse through MRI images of the patient's brain using the sterile hand gesture interface. The surgeons indicated the system was easy to use and fast with high overall satisfaction. © 2008, Taylor & Francis Group, LLC.},\n bibtype = {article},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael and Gillam, Michael and Feied, Craig and Smithd, Mark and Handler, Jon},\n doi = {10.1080/1931308X.2008.10644149},\n journal = {International Journal of Intelligent Computing in Medical Sciences & Image Processing},\n number = {1}\n}\n\n A gesture interface is developed for users, such as doctors/surgeons, to browse medical images in a sterile medical environment. Avision-based gesture capture system interprets user's gestures in real-time to manipulate objects in an image visualization environment. A color distribution model of the gamut of colors of the users hand or glove is built at the start of each session resulting in an independent system. The gesture system relies on real-time robust tracking of the user's hand based on acolor-motion fusion model, in which the relative weight applied to the motion and color cues are adaptively determined according to the state of the system. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. A state machine switches between other gestures such as zoom and rotate, as well as a sleep state. Performance evaluation included gesture recognition accuracy, task learning, and rotation accuracy. Fast task learning rates were found with convergence after ten trials. A beta test of a system prototype was conducted during a live brain biopsy operation, where neurosurgeons were able to browse through MRI images of the patient's brain using the sterile hand gesture interface. The surgeons indicated the system was easy to use and fast with high overall satisfaction. © 2008, Taylor & Francis Group, LLC.\n
\n\n\n\n \n\n \n \n \n \n \n DESIGNING HAND GESTURE VOCABULARIES for NATURAL INTERACTION by COMBINING PSYCHO-PHYSIOLOGICAL and RECOGNITION FACTORS.\n \n \n \n\n\n \n Stern, H., I.; Wachs, J., P.; and Edan, Y.\n\n\n \n\n\n\n International Journal of Semantic Computing, 2(1): 137-160. 2008.\n \n\n\n\n
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\n\n\n@article{\n title = {DESIGNING HAND GESTURE VOCABULARIES for NATURAL INTERACTION by COMBINING PSYCHO-PHYSIOLOGICAL and RECOGNITION FACTORS},\n type = {article},\n year = {2008},\n keywords = {Hand gesture vocabulary design,comfort,gesture interfaces,hand gesture recognition,human-computer interaction,intuitiveness,learning,memory,multiobjective optimization,psycho-physiological,semantic behavior},\n pages = {137-160},\n volume = {2},\n id = {8b90689d-4402-3e26-9df9-bea3d0139c3a},\n created = {2021-06-04T19:36:51.566Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:09.482Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {A need exists for intuitive hand gesture machine interaction in which the machine not only recognizes gestures, but also the human feels comfortable and natural in their execution. The gesture vocabulary design problem is rigorously formulated as a multi-objective optimization problem. Psycho-physiological measures (intuitiveness, comfort) and gesture recognition accuracy are taken as the multi-objective factors. The hand gestures are static and recognized by a vision based fuzzy c-means classifier. A meta-heuristic approach decomposes the problem into two sub-problems: finding the subsets of gestures that meet a minimal accuracy requirement, and matching gestures to commands to maximize the human factors objective. The result is a set of Pareto optimal solutions in which no objective may be increased without a concomitant decrease in another. Several solutions from the Pareto set are selected by the user using prioritized objectives. Software programs are developed to automate the collection of intuitive and stress indices. The method is tested for a simulated car - maze navigation task. Validation tests were conducted to substantiate the claim that solutions that maximize intuitiveness, comfort, and recognition accuracy performance measures can be used as proxies for the minimization task time objective. Learning and memorability were also tested.},\n bibtype = {article},\n author = {Stern, Helman I. and Wachs, Juan P. and Edan, Yael},\n doi = {10.1142/S1793351X08000385},\n journal = {International Journal of Semantic Computing},\n number = {1}\n}\n\n A need exists for intuitive hand gesture machine interaction in which the machine not only recognizes gestures, but also the human feels comfortable and natural in their execution. The gesture vocabulary design problem is rigorously formulated as a multi-objective optimization problem. Psycho-physiological measures (intuitiveness, comfort) and gesture recognition accuracy are taken as the multi-objective factors. The hand gestures are static and recognized by a vision based fuzzy c-means classifier. A meta-heuristic approach decomposes the problem into two sub-problems: finding the subsets of gestures that meet a minimal accuracy requirement, and matching gestures to commands to maximize the human factors objective. The result is a set of Pareto optimal solutions in which no objective may be increased without a concomitant decrease in another. Several solutions from the Pareto set are selected by the user using prioritized objectives. Software programs are developed to automate the collection of intuitive and stress indices. The method is tested for a simulated car - maze navigation task. Validation tests were conducted to substantiate the claim that solutions that maximize intuitiveness, comfort, and recognition accuracy performance measures can be used as proxies for the minimization task time objective. Learning and memorability were also tested.\n
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\n \n \n\n \n \n \n \n \n \n Doctor-Computer Interface using Gestures.\n \n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; and Gillam, M.\n\n\n \n\n\n\n 7th European Gesture Workshop. 2007.\n \n\n\n\n
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\n\n\n\n@article{\n title = {Doctor-Computer Interface using Gestures},\n type = {article},\n year = {2007},\n websites = {http://www.adetti.pt/events/GW2007/proceedings/papers/proceedings posters gw2007_small.pdf#page=16},\n id = {543794bb-869f-3dbe-8826-07b45204d070},\n created = {2014-08-25T15:31:36.000Z},\n accessed = {2014-08-25},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:13:06.564Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2007a},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n bibtype = {article},\n author = {Wachs, J and Stern, H and Edan, Y and Gillam, M},\n journal = {7th European Gesture Workshop}\n}\n\n \n\n \n \n \n \n \n \n Gestix: A doctor-computer sterile gesture interface for dynamic environments.\n \n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; Feied, C.; Smith, M.; and Handler, J.\n\n\n \n\n\n\n Soft Computing in Industrial Applications, 39: 30-39. 2007.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Gestix: A doctor-computer sterile gesture interface for dynamic environments},\n type = {article},\n year = {2007},\n keywords = {Browsing,Hand gesture recognition,Image visualization,Medical databases,Sterile interface},\n pages = {30-39},\n volume = {39},\n websites = {http://link.springer.com/chapter/10.1007/978-3-540-70706-6_3},\n publisher = {Springer, Berlin, Heidelberg},\n id = {f5c09454-30c2-36fd-a93e-cd03c6b09b8c},\n created = {2021-06-04T19:36:48.085Z},\n accessed = {2014-08-25},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.028Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2007b},\n language = {en},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {In this paper, we design a sterile gesture interface for users, such as doctors/surgeons, to browse medical images in a dynamic medical environment. A vision-based gesture capture system interprets user's gestures in real-time to navigate through and manipulate an image and data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. The gesture system relies on tracking of the user's hand based on color-motion cues. A state machine switches from navigation gestures to others such as zoom and rotate. A prototype of the gesture interface was tested in an operating room by neurosurgeons conducting a live operation. Surgeon's feedback was very positive. © 2007 Springer-Verlag Berlin Heidelberg.},\n bibtype = {article},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael and Gillam, Michael and Feied, Craig and Smith, Mark and Handler, Jon},\n doi = {10.1007/978-3-540-70706-6_3},\n journal = {Soft Computing in Industrial Applications}\n}\n\n In this paper, we design a sterile gesture interface for users, such as doctors/surgeons, to browse medical images in a dynamic medical environment. A vision-based gesture capture system interprets user's gestures in real-time to navigate through and manipulate an image and data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. The gesture system relies on tracking of the user's hand based on color-motion cues. A state machine switches from navigation gestures to others such as zoom and rotate. A prototype of the gesture interface was tested in an operating room by neurosurgeons conducting a live operation. Surgeon's feedback was very positive. © 2007 Springer-Verlag Berlin Heidelberg.\n
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\n\n \n \n 2006\n \n \n (7)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Applications of Soft Computing.\n \n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; Feied, C.; Smith, M.; and Handler, J.\n\n\n \n\n\n\n Volume 36 of Advances in Intelligent and Soft ComputingSpringer Berlin Heidelberg, 2006.\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@book{\n title = {Applications of Soft Computing},\n type = {book},\n year = {2006},\n source = {Advances in Soft Computing},\n keywords = {Computerized databases,Fuzzy c-means,Haar features,Hand gesture recognition,Neighborhood search},\n pages = {153-162},\n volume = {36},\n websites = {http://www.springerlink.com/index/10.1007/978-3-540-36266-1,http://link.springer.com/10.1007/978-3-540-36266-1},\n publisher = {Springer Berlin Heidelberg},\n city = {Berlin, Heidelberg},\n series = {Advances in Intelligent and Soft Computing},\n id = {39fffa5c-6b42-3306-8a6d-dd2b5917eff7},\n created = {2014-08-25T16:03:11.000Z},\n accessed = {2014-10-30},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-04T18:13:26.471Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2006d},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accomplished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features, and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.},\n bibtype = {book},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael and Gillam, Michael and Feied, Craig and Smith, Mark and Handler, Jon},\n editor = {Tiwari, Ashutosh and Roy, Rajkumar and Knowles, Joshua and Avineri, Erel and Dahal, Keshav},\n doi = {10.1007/978-3-540-36266-1}\n}\n\n In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accomplished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features, and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n \n Optimal hand gesture vocabulary design methodology for virtual robotic control.\n \n \n \n \n\n\n \n Wachs, J., (., U., o., t., N.\n\n\n \n\n\n\n Ph.D. Thesis, 2006.\n \n\n\n\n
\n\n\n\n \n \nPaper\n \n \n \n Website\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n\n@phdthesis{\n title = {Optimal hand gesture vocabulary design methodology for virtual robotic control},\n type = {phdthesis},\n year = {2006},\n websites = {http://www.movesinstitute.org/~jpwachs/papers/PHD_JUAN_JW.pdf,http://web.ics.purdue.edu/~jpwachs/papers/PHD_JUAN_JW.pdf},\n institution = {Ben-Gurion University of the Negev},\n id = {89ad8cc6-571a-3041-9827-6f1c06488221},\n created = {2021-06-04T19:36:47.985Z},\n accessed = {2014-10-29},\n file_attached = {true},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:10.459Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2006c},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Wachs, J (Ben-Gurion University of the Negev)}\n}\n\n \n\n \n \n \n \n \n \n Human Factors for Design of Hand Gesture Human - Machine Interaction.\n \n \n \n \n\n\n \n Stern, H., H., I.; Wachs, J., J., P.; and Edan, Y.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 5, pages 4052-4056, 10 2006. IEEE\n \n\n\n\n
\n\n\n\n \n \nWebsite\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Human Factors for Design of Hand Gesture Human - Machine Interaction},\n type = {inproceedings},\n year = {2006},\n keywords = {Anthropometry,Classification algorithms,Computer interfaces,Cybernetics,Design methodology,Hand gesture,Human factors,Human robot interaction,Intuitive interfaces,Man machine systems,Man-machine interaction,Muscles,Optimal vocabulary,Vocabulary,corresponding author,factors,gesture recognition,hand gesture,hand gesture human machine interaction,hand gesture vocabulary design,human,human computer interaction,human factors,intuitive interfaces,man machine interaction,man-machine interaction,man-machine systems,optimal vocabulary,preconceived command vocabularies,vocabulary},\n pages = {4052-4056},\n volume = {5},\n websites = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4274532,http://web.ics.purdue.edu/~jpwachs/papers/smc06_human.pdf},\n month = {10},\n publisher = {IEEE},\n id = {bec2c9a9-1d0e-3ffe-a6dd-f92294afcd54},\n created = {2021-06-04T19:36:48.213Z},\n accessed = {2014-08-25},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.132Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2006c},\n language = {English},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015,6afacd88-21b2-42fe-ba51-7da836be4a80},\n private_publication = {false},\n abstract = {A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The method of selecting gestures for preconceived command vocabularies has not been addressed in a systematic manner. Present methods are ad hoc. In an analytical approach technological factors of gesture recognition accuracy are easily obtained and well studied. Conversely, it is difficult to obtain measures of human centered desires (intuitiveness, comfort), These factors, being subjective, are costly and time consuming to obtain, and hence we have developed automated methods for acquisition of these data through specially designed applications. Results of the intuitiveness experiments showed when commands are presented as stimuli the gestural responses vary widely over a population of subjects. This result refutes the hypothesis that there exist universal common gestures to express user intentions or commands. © 2006 IEEE.},\n bibtype = {inproceedings},\n author = {Stern, H.I. Helman I. and Wachs, J.P. Juan P. and Edan, Yael},\n doi = {10.1109/ICSMC.2006.384767},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The method of selecting gestures for preconceived command vocabularies has not been addressed in a systematic manner. Present methods are ad hoc. In an analytical approach technological factors of gesture recognition accuracy are easily obtained and well studied. Conversely, it is difficult to obtain measures of human centered desires (intuitiveness, comfort), These factors, being subjective, are costly and time consuming to obtain, and hence we have developed automated methods for acquisition of these data through specially designed applications. Results of the intuitiveness experiments showed when commands are presented as stimuli the gestural responses vary widely over a population of subjects. This result refutes the hypothesis that there exist universal common gestures to express user intentions or commands. © 2006 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n A method to enhance the 'Possibilistic C-Means with repulsion' algorithm based on cluster validity index.\n \n \n \n\n\n \n Wachs, J.; Shapira, O.; and Stern, H.\n\n\n \n\n\n\n Advances in Soft Computing, 34: 77-87. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A method to enhance the 'Possibilistic C-Means with repulsion' algorithm based on cluster validity index},\n type = {article},\n year = {2006},\n pages = {77-87},\n volume = {34},\n id = {665556ac-9517-3b65-9a0c-caf314cf02af},\n created = {2021-06-04T19:36:49.276Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.355Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2006b},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. Recently, an extension of the PCM has been presented by Timm et al., by introducing a repulsion term. This approach combines the partitioning property of the FCM with the robust noise insensibility of the PCM. We illustrate the advantages of both the possibilistic and probabilistic families of algorithms with several examples and discuss the PCM with cluster repulsion. We provide a cluster valid-ity function evaluation algorithm to solve the problem of parameter optimization. The algorithm is especially useful for the unsupervised case, when labeled data is unavailable. © 2006 Springer.},\n bibtype = {article},\n author = {Wachs, Juan and Shapira, Oren and Stern, Helman},\n doi = {10.1007/3-540-31662-0_6},\n journal = {Advances in Soft Computing}\n}\n\n In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. Recently, an extension of the PCM has been presented by Timm et al., by introducing a repulsion term. This approach combines the partitioning property of the FCM with the robust noise insensibility of the PCM. We illustrate the advantages of both the possibilistic and probabilistic families of algorithms with several examples and discuss the PCM with cluster repulsion. We provide a cluster valid-ity function evaluation algorithm to solve the problem of parameter optimization. The algorithm is especially useful for the unsupervised case, when labeled data is unavailable. © 2006 Springer.\n
\n\n\n\n \n\n \n \n \n \n \n Optimal hand gesture vocabulary design using psycho-physiological and technical factors.\n \n \n \n\n\n \n Stern, H., H., I.; Wachs, J., J., P.; and Edan, Y.\n\n\n \n\n\n\n In FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, volume 2006, pages 257-262, 2006. \n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Optimal hand gesture vocabulary design using psycho-physiological and technical factors},\n type = {inproceedings},\n year = {2006},\n keywords = {Complementary gestures,Hand gesture,Human computer interfaces,Metaheurisitic,Multiobjective,Optimal vocabulary design,Psychophysiological},\n pages = {257-262},\n volume = {2006},\n id = {11dd6631-fb3b-32aa-9407-2463205df4a6},\n created = {2021-06-04T19:36:49.855Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:00.137Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2006},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The human centered desires of multiple users are implicitly represented through indices obtained from ergonomic studies representing the psychophysiological aspects of users. The main technical aspect considered is that of machine recognition of gestures. We review and classify three approaches to this problem: Ad hoc, Rule based, and Analytical. It is believed that this is the first conceptualization of the optimal hand gesture design problem in analytical form. A mathematical dual objective model is developed, which reflects the psychophysiological and technical performance measures upon which a gesture control system is judged. The mathematical program solves a quadratic assignment problem embedded within a heuristic search tree. The quadratic problem, whose solution is a gesture vocabulary GV, (a command-gesture matching) is solved through simulated annealing. A useful feature, included in the formulation, is the priorities given to the matching of complementary pairs of gestures (say thumb up - thumb down) to complementary pairs of commands (say up down). To validate the procedure an example is solved for the design of a medium size robot command GV. © 2006 IEEE.},\n bibtype = {inproceedings},\n author = {Stern, H.I. Helman I. and Wachs, J.P. Juan P. and Edan, Yael},\n doi = {10.1109/FGR.2006.84},\n booktitle = {FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition}\n}\n\n A global approach to hand gesture vocabulary design is proposed which includes human as well as technical design factors. The human centered desires of multiple users are implicitly represented through indices obtained from ergonomic studies representing the psychophysiological aspects of users. The main technical aspect considered is that of machine recognition of gestures. We review and classify three approaches to this problem: Ad hoc, Rule based, and Analytical. It is believed that this is the first conceptualization of the optimal hand gesture design problem in analytical form. A mathematical dual objective model is developed, which reflects the psychophysiological and technical performance measures upon which a gesture control system is judged. The mathematical program solves a quadratic assignment problem embedded within a heuristic search tree. The quadratic problem, whose solution is a gesture vocabulary GV, (a command-gesture matching) is solved through simulated annealing. A useful feature, included in the formulation, is the priorities given to the matching of complementary pairs of gestures (say thumb up - thumb down) to complementary pairs of commands (say up down). To validate the procedure an example is solved for the design of a medium size robot command GV. © 2006 IEEE.\n
\n\n\n\n \n\n \n \n \n \n \n A real-time hand gesture interface for medical visualization applications.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; Feied, C.; Smith, M.; and Handler, J.\n\n\n \n\n\n\n In Advances in Soft Computing, volume 36, pages 153-162, 2006. \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A real-time hand gesture interface for medical visualization applications},\n type = {inproceedings},\n year = {2006},\n keywords = {Computerized databases,Fuzzy c-means,Haar features,Hand gesture recognition,Neighborhood search},\n pages = {153-162},\n volume = {36},\n id = {a301b700-44ec-3cf7-b044-087e4bf3bf47},\n created = {2021-06-04T19:36:50.308Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.488Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2006e},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accom-plished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features, and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.},\n bibtype = {inproceedings},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael and Gillam, Michael and Feied, Craig and Smith, Mark and Handler, Jon},\n doi = {10.1007/978-3-540-36266-1_15},\n booktitle = {Advances in Soft Computing}\n}\n\n In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accom-plished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features, and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n A real-time gesture interface for hands-free control of electronic medical records.\n \n \n \n\n\n \n Feied, C.; Gillam, M.; Wachs, J.; Handler, J.; Stern, H.; and Smith, M.\n\n\n \n\n\n\n AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium,920. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{\n title = {A real-time gesture interface for hands-free control of electronic medical records.},\n type = {article},\n year = {2006},\n pages = {920},\n institution = {Medical Media Lab: National Institute for Medical Informatics, MedStar Health, Washington, DC, USA.},\n id = {4c040517-2653-38f1-8ac6-bed085e9c6f0},\n created = {2021-06-04T19:36:51.873Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.968Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Feied2006},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {Whether attempting to review digital radiologic images during a procedure or reviewing labs on a clinical ward, computer keyboards and mice are potential sources for contamination of clinicians during sterile and non-sterile activities related to clinical care. The authors describe and demonstrate a live system prototype for hands-free, gesture-based control of an electronic medical record (EMR) system.},\n bibtype = {article},\n author = {Feied, Craig and Gillam, Michael and Wachs, Juan and Handler, Jonathan and Stern, Helman and Smith, Mark},\n journal = {AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium}\n}\n\n Whether attempting to review digital radiologic images during a procedure or reviewing labs on a clinical ward, computer keyboards and mice are potential sources for contamination of clinicians during sterile and non-sterile activities related to clinical care. The authors describe and demonstrate a live system prototype for hands-free, gesture-based control of an electronic medical record (EMR) system.\n
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\n\n \n \n 2005\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n A real-time hand gesture system based on evolutionary search.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; Edan, Y.; Gillam, M.; Feied, C.; Smith, M.; and Handler, J.\n\n\n \n\n\n\n In Technical Paper - Society of Manufacturing Engineers, volume TP05PUB201, 2005. \n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {A real-time hand gesture system based on evolutionary search},\n type = {inproceedings},\n year = {2005},\n keywords = {Computerized medical equipment,Fuzzy c-means,Haar-like features,Hand gesture recognition,Neighborhood search},\n volume = {TP05PUB201},\n id = {b4a7fff9-4666-3d1b-9c8f-f2a19020855c},\n created = {2021-06-04T19:36:49.098Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.157Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2005},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accomplished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features. and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.},\n bibtype = {inproceedings},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael and Gillam, Michael and Feied, Craig and Smith, Mark and Handler, Jon},\n booktitle = {Technical Paper - Society of Manufacturing Engineers}\n}\n\n In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate objects within a medical data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. Static gesture poses are identified to execute non-directional commands. This is accomplished by using Haar-like features to represent the shape of the hand. These features are then input to a Fuzzy C-Means Clustering algorithm for pose classification. A probabilistic neighborhood search algorithm is employed to automatically select a small number of Haar features. and to tune the fuzzy c-means classification algorithm. The gesture recognition system was implemented in a sterile medical data-browser environment. Test results on four interface tasks showed that the use of a few Haar features with the supervised FCM yielded successful performance rates of 95 to 100%. In addition a small exploratory test of the Adaboost Haar system was made to detect a single hand gesture, and assess its suitability for hand gesture recognition.\n
\n\n\n\n \n\n \n \n \n \n \n Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system.\n \n \n \n\n\n \n Wachs, J., J., P.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 35(6): 932-944. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@article{\n title = {Cluster labeling and parameter estimation for the automated setup of a hand-gesture recognition system},\n type = {article},\n year = {2005},\n keywords = {Automated setup,Cluster labeling,Fuzzy c-means,Gesture recognition,Hand gestures,Neighborhood search,Supervised clustering,Telerobotics},\n pages = {932-944},\n volume = {35},\n id = {3e8d5be8-eca5-3f18-b50c-1d1d7daebc8a},\n created = {2021-06-04T19:36:49.744Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.808Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2005b},\n folder_uuids = {af4e07b9-db07-412a-bffe-81543dbaae58,b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on. © 2005 IEEE.},\n bibtype = {article},\n author = {Wachs, J.P. Juan P. and Stern, Helman and Edan, Yael},\n doi = {10.1109/TSMCA.2005.851332},\n journal = {IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans},\n number = {6}\n}\n\n In this work, we address the issue of reconfigurability of a hand-gesture recognition system. The calibration or setup of the operational parameters of such a system is a time-consuming effort, usually performed by trial and error, and often causing system performance to suffer because of designer impatience. In this work, we suggest a methodology using a neighborhood-search algorithm for tuning system parameters. Thus, the design of hand-gesture recognition systems is transformed into an optimization problem. To test the methodology, we address the difficult problem of simultaneous calibration of the parameters of the image processing/fuzzy C-means (FCM) components of a hand-gesture recognition system. In addition, we proffer a method for supervising the FCM algorithm using linear programming and heuristic labeling. Resulting solutions exhibited fast convergence (in the order of ten iterations) to reach recognition accuracies within several percent of the optimal. Comparative performance testing using three gesture databases (BGU, American Sign Language and Gripsee), and a real-time implementation (Tele-Gest) are reported on. © 2005 IEEE.\n
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\n\n \n \n 2004\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Hand gesture vocabulary design: A multicriteria optimization.\n \n \n \n\n\n \n Stern, H., H., I.; Wachs, J., J., P.; and Edan, Y.\n\n\n \n\n\n\n In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, volume 1, pages 19-23, 2004. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Hand gesture vocabulary design: A multicriteria optimization},\n type = {inproceedings},\n year = {2004},\n keywords = {Hand gesture,Human interfaces,Intuitive interfaces,Man-machine interaction,Multicriteria optimization,Multiobjective decision,Optimal vocabulary},\n pages = {19-23},\n volume = {1},\n id = {c62ae7a6-d4ad-3ba2-a467-43089a01a911},\n created = {2021-06-04T19:36:49.355Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:59.419Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Stern2004},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {A global approach to hand gesture vocabulary (GV) design is proposed which includes human as well as technical design factors. The human centered desires (intuitiveness, comfort) of multiple users are implicitly represented through indices obtained from ergonomic studies representing the psycho-physiological aspects of users. The main technical aspect considered is that of machine recognition of gestures. We believe this is the first conceptualization of the optimal hand gesture design problem in analytical form. The problem is formulated as a multicriteria optimization problem (MCOP) for which a 3D representation of the solution space is used to display candidate solutions, as well as Pareto optimal ones. A computational example is given for the design of a small robot command GV using the MCOP procedure. © 2004 IEEE.},\n bibtype = {inproceedings},\n author = {Stern, H.I. Helman I. and Wachs, J.P. Juan P. and Edan, Yael},\n doi = {10.1109/icsmc.2004.1398266},\n booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}\n}\n\n A global approach to hand gesture vocabulary (GV) design is proposed which includes human as well as technical design factors. The human centered desires (intuitiveness, comfort) of multiple users are implicitly represented through indices obtained from ergonomic studies representing the psycho-physiological aspects of users. The main technical aspect considered is that of machine recognition of gestures. We believe this is the first conceptualization of the optimal hand gesture design problem in analytical form. The problem is formulated as a multicriteria optimization problem (MCOP) for which a 3D representation of the solution space is used to display candidate solutions, as well as Pareto optimal ones. A computational example is given for the design of a small robot command GV using the MCOP procedure. © 2004 IEEE.\n
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\n\n \n \n 2003\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Parameter search for an image processing fuzzy C-means hand gesture recognition system.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n In Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), volume 3, pages 341-344, 2003. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Parameter search for an image processing fuzzy C-means hand gesture recognition system},\n type = {inproceedings},\n year = {2003},\n pages = {341-344},\n volume = {3},\n id = {30cba04e-0511-3558-8905-653d62008b17},\n created = {2021-06-04T19:36:48.517Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:16:58.502Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs2003},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This work describes a hand gesture recognition system using an optimized Image Processing-Fuzzy C-Means (FCM) algorithm. The parameters of the image processing and clustering algorithm were simultaneously found using a neighborhood parameter search routine, resulting in solutions within 1-2% of optimal. Comparison of user dependent and user independent systems, when tested with their own trainers, resulted in recognition accuracies of 98.9% and 98.2%, respectively. For experienced users, the opposite was true, testing recognition accuracies where better for user independent than user dependent systems (98.2% over 96.0%). These results are statistically significant at the .007 level.},\n bibtype = {inproceedings},\n author = {Wachs, Juan and Stern, Helman and Edan, Yael},\n doi = {10.1109/ICIP.2003.1247251},\n booktitle = {Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)}\n}\n\n This work describes a hand gesture recognition system using an optimized Image Processing-Fuzzy C-Means (FCM) algorithm. The parameters of the image processing and clustering algorithm were simultaneously found using a neighborhood parameter search routine, resulting in solutions within 1-2% of optimal. Comparison of user dependent and user independent systems, when tested with their own trainers, resulted in recognition accuracies of 98.9% and 98.2%, respectively. For experienced users, the opposite was true, testing recognition accuracies where better for user independent than user dependent systems (98.2% over 96.0%). These results are statistically significant at the .007 level.\n
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\n\n \n \n 2002\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n \n Real-time hand gesture telerobotic system using fuzzy c-means clustering.\n \n \n \n \n\n\n \n Wachs, J.; Kartoun, U.; Stern, H.; and Edan, Y.\n\n\n \n\n\n\n In Proceedings of the 5th Biannual World Automation Congress, volume 13, pages 403-409, 2002. TSI Press\n \n\n\n\n
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\n\n\n@inproceedings{\n title = {Real-time hand gesture telerobotic system using fuzzy c-means clustering},\n type = {inproceedings},\n year = {2002},\n keywords = {Fuzzy c-means,Gesture recognition,Hand gesture,Telerobotics},\n pages = {403-409},\n volume = {13},\n websites = {http://ieeexplore.ieee.org/document/1049576/},\n publisher = {TSI Press},\n id = {52f4f6cb-36fb-3feb-b2b3-002dfadd53a5},\n created = {2021-06-04T19:36:50.114Z},\n accessed = {2018-07-16},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:01.310Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Wachs},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Internet. A Fuzzy C-Means clustering method is used to classify hand postures as "gesture commands". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.},\n bibtype = {inproceedings},\n author = {Wachs, Juan and Kartoun, Uri and Stern, Helman and Edan, Yael},\n doi = {10.1109/WAC.2002.1049576},\n booktitle = {Proceedings of the 5th Biannual World Automation Congress}\n}\n\n This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Internet. A Fuzzy C-Means clustering method is used to classify hand postures as \"gesture commands\". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.\n
\n\n\n\n \n\n \n \n \n \n \n COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK.\n \n \n \n\n\n \n Wachs, J.; Stern, H.; and Last, M.\n\n\n \n\n\n\n International Journal of Image and Graphics, 2(4): 587-601. 2002.\n \n\n\n\n
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\n\n\n@article{\n title = {COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK},\n type = {article},\n year = {2002},\n keywords = {Skin segmentation,color recognition,face detection,fuzzy clustering,fuzzy logic,fuzzy neural networks,pattern recognition},\n pages = {587-601},\n volume = {2},\n id = {28340ac7-59a9-3538-97f7-8a826900361e},\n created = {2021-06-04T19:36:51.694Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2021-06-07T19:17:02.878Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n folder_uuids = {b43d1b86-b425-4322-b575-14547700e015},\n private_publication = {false},\n abstract = {This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.},\n bibtype = {article},\n author = {Wachs, Juan and Stern, Helman and Last, Mark},\n doi = {10.1142/S021946780200086X},\n journal = {International Journal of Image and Graphics},\n number = {4}\n}\n\n This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.\n
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\n \n \n\n \n \n \n \n \n \n Inside Indiana Business.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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\n\n\n\n@misc{\n title = {Inside Indiana Business},\n type = {misc},\n websites = {https://www.insideindianabusiness.com/articles/surgical-technology-aims-to-mimic-teleporting},\n id = {b28aeeb2-3807-3837-ae8f-1f484db9f7d9},\n created = {2023-11-17T22:36:29.939Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-11-17T22:36:29.939Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {}\n}\n\n \n\n \n \n \n \n \n \n How Technology is Helping Surgeons Collaborate from Across the World.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n WIRED Magazine. .\n \n\n\n\n
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\n\n\n\n@article{\n title = {How Technology is Helping Surgeons Collaborate from Across the World},\n type = {article},\n websites = {https://www.wired.com/brandlab/2018/07/technology-helping-surgeons-collaborate-across-world/},\n id = {9c94122e-30cf-3408-98d1-975f7fe0ce51},\n created = {2023-11-17T22:36:29.946Z},\n file_attached = {false},\n profile_id = {f6c02e5e-2d2f-3786-8fa8-871d32fc2b9b},\n last_modified = {2023-11-17T22:36:29.946Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {},\n journal = {WIRED Magazine}\n}\n