@inproceedings{elkholy_general_2017,
	title = {A general descriptor for detecting abnormal action performance from skeletal data},
	rights = {All rights reserved},
	doi = {10.1109/EMBC.2017.8037095},
	abstract = {We propose an action-independent descriptor for detecting abnormality in motion, based on medically-inspired skeletal features. The descriptor is tested on four actions/motions captured using a single depth camera: sit-to-stand, stand-to-sit, flat-walk, and climbing-stairs. For each action, a Gaussian Mixture Model ({GMM}) trained on normal motions is built using the action-independent feature descriptor. Test sequences are evaluated based on their fitness to the normal motion models, with a threshold over the likelihood, to assess abnormality. Results show that the descriptor is able to detect abnormality with accuracy ranging from 0.97 to 1 for the various motions.},
	eventtitle = {2017 39th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society ({EMBC})},
	pages = {1401--1404},
	booktitle = {2017 39th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society ({EMBC})},
	author = {Elkholy, A. and Hussein, M. E. and Gomaa, W. and Damen, D. and Saba, E.},
	year = {2017},
	keywords = {abnormal action performance detection, action-independent feature descriptor, cameras, Cameras, climbing-stairs motion, Feature extraction, flat-walk motion, gait analysis, Gaussian mixture model, Gaussian processes, Legged locomotion, medically-inspired skeletal features, Motion, Movement, Musculoskeletal System, Normal Distribution, Sensors, single depth camera, sit-to-stand motion, skeletal data, Skeleton, stand-to-sit motion, Testing, Three-dimensional displays, Walking},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\VBHX3VCD\\8037095.html:text/html}
}

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Test sequences are evaluated based on their fitness to the normal motion models, with a threshold over the likelihood, to assess abnormality. Results show that the descriptor is able to detect abnormality with accuracy ranging from 0.97 to 1 for the various motions.","eventtitle":"2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","pages":"1401–1404","booktitle":"2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":[{"propositions":[],"lastnames":["Elkholy"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Hussein"],"firstnames":["M.","E."],"suffixes":[]},{"propositions":[],"lastnames":["Gomaa"],"firstnames":["W."],"suffixes":[]},{"propositions":[],"lastnames":["Damen"],"firstnames":["D."],"suffixes":[]},{"propositions":[],"lastnames":["Saba"],"firstnames":["E."],"suffixes":[]}],"year":"2017","keywords":"abnormal action performance detection, action-independent feature descriptor, cameras, Cameras, climbing-stairs motion, Feature extraction, flat-walk motion, gait analysis, Gaussian mixture model, Gaussian processes, Legged locomotion, medically-inspired skeletal features, Motion, Movement, Musculoskeletal System, Normal Distribution, Sensors, single depth camera, sit-to-stand motion, skeletal data, Skeleton, stand-to-sit motion, Testing, Three-dimensional displays, Walking","file":"IEEE Xplore Abstract Record:C\\:\\\\Users\\\\Mohamed Hussein\\\\Zotero\\\\storage\\\\VBHX3VCD\\\\8037095.html:text/html","bibtex":"@inproceedings{elkholy_general_2017,\n\ttitle = {A general descriptor for detecting abnormal action performance from skeletal data},\n\trights = {All rights reserved},\n\tdoi = {10.1109/EMBC.2017.8037095},\n\tabstract = {We propose an action-independent descriptor for detecting abnormality in motion, based on medically-inspired skeletal features. The descriptor is tested on four actions/motions captured using a single depth camera: sit-to-stand, stand-to-sit, flat-walk, and climbing-stairs. For each action, a Gaussian Mixture Model ({GMM}) trained on normal motions is built using the action-independent feature descriptor. Test sequences are evaluated based on their fitness to the normal motion models, with a threshold over the likelihood, to assess abnormality. Results show that the descriptor is able to detect abnormality with accuracy ranging from 0.97 to 1 for the various motions.},\n\teventtitle = {2017 39th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society ({EMBC})},\n\tpages = {1401--1404},\n\tbooktitle = {2017 39th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society ({EMBC})},\n\tauthor = {Elkholy, A. and Hussein, M. 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