var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques\",\"type\":\"inproceedings\",\"year\":\"2021\",\"pages\":\"55-67\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5\",\"publisher\":\"Springer International Publishing\",\"city\":\"Cham\",\"id\":\"e77821ff-b41a-304c-8ccb-cc74a3c124ad\",\"created\":\"2022-02-02T07:25:42.987Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:42.987Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-030-89654-6_5\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\\\% rating yield using the proposed methodology with the Fast Tree algorithm.\",\"bibtype\":\"inproceedings\",\"author\":\"López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D\",\"editor\":\"Florez, Hector and Pollo-Cattaneo, Ma Florencia\",\"booktitle\":\"Applied Informatics\",\"bibtex\":\"@inproceedings{\\n title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},\\n type = {inproceedings},\\n year = {2021},\\n pages = {55-67},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},\\n publisher = {Springer International Publishing},\\n city = {Cham},\\n id = {e77821ff-b41a-304c-8ccb-cc74a3c124ad},\\n created = {2022-02-02T07:25:42.987Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:42.987Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-030-89654-6_5},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\\\% rating yield using the proposed methodology with the Fast Tree algorithm.},\\n bibtype = {inproceedings},\\n author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},\\n editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},\\n booktitle = {Applied Informatics}\\n}\",\"author_short\":[\"López-Albán,  D.\",\"López-Barrera,  A.\",\"Mayorca-Torres,  D.\",\"Peluffo-Ordóñez,  D.\"],\"editor_short\":[\"Florez,  H.\",\"Pollo-Cattaneo,  M., F.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"title\":\"BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control\",\"type\":\"article\",\"year\":\"2021\",\"keywords\":\"Bio-signals analysis,Brain Computer Interface,Embedded Systems,FPGA,Neural Networks\",\"pages\":\"364-369\",\"volume\":\"54\",\"websites\":\"https://www.sciencedirect.com/science/article/pii/S2405896321016876\",\"id\":\"19c1b4bf-e5af-3508-bea8-6087ba4d3f97\",\"created\":\"2022-02-02T07:25:43.282Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:43.282Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"CONSTANTINE2021364\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.\",\"bibtype\":\"article\",\"author\":\"Constantine, Alisson and Asanza, Víctor and Loayza, Francis R and Peláez, Enrique and Peluffo-Ordóñez, Diego\",\"doi\":\"https://doi.org/10.1016/j.ifacol.2021.10.283\",\"journal\":\"IFAC-PapersOnLine\",\"number\":\"15\",\"bibtex\":\"@article{\\n title = {BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control},\\n type = {article},\\n year = {2021},\\n keywords = {Bio-signals analysis,Brain Computer Interface,Embedded Systems,FPGA,Neural Networks},\\n pages = {364-369},\\n volume = {54},\\n websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016876},\\n id = {19c1b4bf-e5af-3508-bea8-6087ba4d3f97},\\n created = {2022-02-02T07:25:43.282Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:43.282Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {CONSTANTINE2021364},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.},\\n bibtype = {article},\\n author = {Constantine, Alisson and Asanza, Víctor and Loayza, Francis R and Peláez, Enrique and Peluffo-Ordóñez, Diego},\\n doi = {https://doi.org/10.1016/j.ifacol.2021.10.283},\\n journal = {IFAC-PapersOnLine},\\n number = {15}\\n}\",\"author_short\":[\"Constantine,  A.\",\"Asanza,  V.\",\"Loayza,  F., R.\",\"Peláez,  E.\",\"Peluffo-Ordóñez,  D.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/pii/S2405896321016876\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021\",\"role\":\"author\",\"keyword\":[\"Bio-signals analysis\",\"Brain Computer Interface\",\"Embedded Systems\",\"FPGA\",\"Neural Networks\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset\",\"type\":\"article\",\"year\":\"2021\",\"keywords\":\"Classification,Finger Tapping,Machine Learning,Parkinson’s disease\",\"pages\":\"376-381\",\"volume\":\"54\",\"websites\":\"https://www.sciencedirect.com/science/article/pii/S2405896321016906\",\"id\":\"a0809ce4-85c2-3631-b319-abf677f9d692\",\"created\":\"2022-02-02T07:25:43.665Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:43.665Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ASANZA2021376\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.\",\"bibtype\":\"article\",\"author\":\"Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique\",\"doi\":\"https://doi.org/10.1016/j.ifacol.2021.10.285\",\"journal\":\"IFAC-PapersOnLine\",\"number\":\"15\",\"bibtex\":\"@article{\\n title = {Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset},\\n type = {article},\\n year = {2021},\\n keywords = {Classification,Finger Tapping,Machine Learning,Parkinson’s disease},\\n pages = {376-381},\\n volume = {54},\\n websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016906},\\n id = {a0809ce4-85c2-3631-b319-abf677f9d692},\\n created = {2022-02-02T07:25:43.665Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:43.665Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ASANZA2021376},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.},\\n bibtype = {article},\\n author = {Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique},\\n doi = {https://doi.org/10.1016/j.ifacol.2021.10.285},\\n journal = {IFAC-PapersOnLine},\\n number = {15}\\n}\",\"author_short\":[\"Asanza,  V.\",\"Sánchez-Pozo,  N., N.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\",\"Loayza,  F., R.\",\"Peláez,  E.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/pii/S2405896321016906\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021\",\"role\":\"author\",\"keyword\":[\"Classification\",\"Finger Tapping\",\"Machine Learning\",\"Parkinson’s disease\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis\",\"type\":\"article\",\"year\":\"2020\",\"keywords\":\"Gait analysis,IMU,Kalman filter,Knee-joint angle,Motion analysis\",\"websites\":\"http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10814\",\"id\":\"f9bdd20a-54e8-348c-a457-82751a93f902\",\"created\":\"2022-02-02T07:25:43.940Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:43.940Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Mayorca-Torres2020\",\"private_publication\":false,\"abstract\":\"Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient's knee-joint kinematics during a human gait.\",\"bibtype\":\"article\",\"author\":\"Mayorca-Torres, Dagoberto and Caicedo-Eraso, Julio C. and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.18517/ijaseit.10.2.10814\",\"journal\":\"International Journal on Advanced Science, Engineering and Information Technology\",\"bibtex\":\"@article{\\n title = {Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis},\\n type = {article},\\n year = {2020},\\n keywords = {Gait analysis,IMU,Kalman filter,Knee-joint angle,Motion analysis},\\n websites = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10814},\\n id = {f9bdd20a-54e8-348c-a457-82751a93f902},\\n created = {2022-02-02T07:25:43.940Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:43.940Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Mayorca-Torres2020},\\n private_publication = {false},\\n abstract = {Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient's knee-joint kinematics during a human gait.},\\n bibtype = {article},\\n author = {Mayorca-Torres, Dagoberto and Caicedo-Eraso, Julio C. and Peluffo-Ordóñez, Diego H.},\\n doi = {10.18517/ijaseit.10.2.10814},\\n journal = {International Journal on Advanced Science, Engineering and Information Technology}\\n}\",\"author_short\":[\"Mayorca-Torres,  D.\",\"Caicedo-Eraso,  J., C.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10814\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020\",\"role\":\"author\",\"keyword\":[\"Gait analysis\",\"IMU\",\"Kalman filter\",\"Knee-joint angle\",\"Motion analysis\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":3,\"html\":\"\"},{\"title\":\"Data fusion and information quality for biometric identification from multimodal signals\",\"type\":\"article\",\"year\":\"2020\",\"keywords\":\"Biometry,Data fusion,Information quality,Signal processing\",\"websites\":\"https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&fromopenview=true\",\"id\":\"dc99d2ec-10d5-34ea-b53a-882ff1aae863\",\"created\":\"2022-02-02T07:25:44.209Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:44.209Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Becerra2020a\",\"private_publication\":false,\"abstract\":\"Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.\",\"bibtype\":\"article\",\"author\":\"Becerra, Miguel A. and Lasso-Arciniegas, Laura and Viveros, Andrés and Serna-Guarín, Leonardo and Peluffo-Ordóñez, Diego and Tobón, Catalina\",\"journal\":\"RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao\",\"bibtex\":\"@article{\\n title = {Data fusion and information quality for biometric identification from multimodal signals},\\n type = {article},\\n year = {2020},\\n keywords = {Biometry,Data fusion,Information quality,Signal processing},\\n websites = {https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&fromopenview=true},\\n id = {dc99d2ec-10d5-34ea-b53a-882ff1aae863},\\n created = {2022-02-02T07:25:44.209Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:44.209Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Becerra2020a},\\n private_publication = {false},\\n abstract = {Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.},\\n bibtype = {article},\\n author = {Becerra, Miguel A. and Lasso-Arciniegas, Laura and Viveros, Andrés and Serna-Guarín, Leonardo and Peluffo-Ordóñez, Diego and Tobón, Catalina},\\n journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}\\n}\",\"author_short\":[\"Becerra,  M., A.\",\"Lasso-Arciniegas,  L.\",\"Viveros,  A.\",\"Serna-Guarín,  L.\",\"Peluffo-Ordóñez,  D.\",\"Tobón,  C.\"],\"urls\":{\"Website\":\"https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&fromopenview=true\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020\",\"role\":\"author\",\"keyword\":[\"Biometry\",\"Data fusion\",\"Information quality\",\"Signal processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2020\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7\",\"id\":\"b6b9da88-86d3-3cd4-86ae-b66d5e3ba59e\",\"created\":\"2022-02-02T07:25:44.514Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:44.514Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Patino-Alarcon2020\",\"private_publication\":false,\"abstract\":\"This research presents an application of the clustering based on Thinking Based - Learning methodology (TBL), which offers guidelines to promote students’ reflective thinking. Within this methodology, the Intelligence Execution Theory (IET) tool will be used to encourage this kind of thinking in the classroom. Having in mind that, in any educational process, methodologies and pedagogical tools have a pivotal role as they are one of the bases for optimizing cognitive intelligence. In this case, it was given a priority to the potential development of a specific linguistic skill. This study presented a mixed methodology with an exploratory and descriptive scope. The main objective of this research was the clustering of the variables of functioning of the reading ability in the English language based on the TBL methodology and its behavior in the left hemisphere of the brain, specifically to analyze the improvement of the reading ability in the English language of the participants of this case study. With the expectation of generating sustainability of adequate levels of performance, instruction and learning of the English language of students at all levels.\",\"bibtype\":\"inbook\",\"author\":\"Patiño-Alarcón, Delio R. and Patiño-Alarcón, Fernando A. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1007/978-3-030-62833-8_7\",\"chapter\":\"Clustering of Reading Ability Performance Variables in the English Language Based on TBL Methodology and Behavior in the Left Hemisphere of the Brain\",\"title\":\"Communications in Computer and Information Science\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2020},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7},\\n id = {b6b9da88-86d3-3cd4-86ae-b66d5e3ba59e},\\n created = {2022-02-02T07:25:44.514Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:44.514Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Patino-Alarcon2020},\\n private_publication = {false},\\n abstract = {This research presents an application of the clustering based on Thinking Based - Learning methodology (TBL), which offers guidelines to promote students’ reflective thinking. Within this methodology, the Intelligence Execution Theory (IET) tool will be used to encourage this kind of thinking in the classroom. Having in mind that, in any educational process, methodologies and pedagogical tools have a pivotal role as they are one of the bases for optimizing cognitive intelligence. In this case, it was given a priority to the potential development of a specific linguistic skill. This study presented a mixed methodology with an exploratory and descriptive scope. The main objective of this research was the clustering of the variables of functioning of the reading ability in the English language based on the TBL methodology and its behavior in the left hemisphere of the brain, specifically to analyze the improvement of the reading ability in the English language of the participants of this case study. With the expectation of generating sustainability of adequate levels of performance, instruction and learning of the English language of students at all levels.},\\n bibtype = {inbook},\\n author = {Patiño-Alarcón, Delio R. and Patiño-Alarcón, Fernando A. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1007/978-3-030-62833-8_7},\\n chapter = {Clustering of Reading Ability Performance Variables in the English Language Based on TBL Methodology and Behavior in the Left Hemisphere of the Brain},\\n title = {Communications in Computer and Information Science}\\n}\",\"author_short\":[\"Patiño-Alarcón,  D., R.\",\"Patiño-Alarcón,  F., A.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"title\":\"Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"Alarm,Artificial intelligence,Drowsiness detection,Human eye,Image processing\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54\",\"id\":\"8e496276-78ed-3f37-bf6f-bbc726f99b3e\",\"created\":\"2022-02-02T07:25:44.943Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:44.943Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Herrera-Granda2019\",\"private_publication\":false,\"abstract\":\"At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.\",\"bibtype\":\"inproceedings\",\"author\":\"Herrera-Granda, Erick P. and Caraguay-Procel, Jorge A. and Granda-Gudiño, Pedro D. and Herrera-Granda, Israel D. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H. and Revelo-Fuelagán, Javier\",\"doi\":\"10.1007/978-3-030-14799-0_54\",\"booktitle\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inproceedings{\\n title = {Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {Alarm,Artificial intelligence,Drowsiness detection,Human eye,Image processing},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54},\\n id = {8e496276-78ed-3f37-bf6f-bbc726f99b3e},\\n created = {2022-02-02T07:25:44.943Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:44.943Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Herrera-Granda2019},\\n private_publication = {false},\\n abstract = {At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.},\\n bibtype = {inproceedings},\\n author = {Herrera-Granda, Erick P. and Caraguay-Procel, Jorge A. and Granda-Gudiño, Pedro D. and Herrera-Granda, Israel D. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H. and Revelo-Fuelagán, Javier},\\n doi = {10.1007/978-3-030-14799-0_54},\\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Herrera-Granda,  E., P.\",\"Caraguay-Procel,  J., A.\",\"Granda-Gudiño,  P., D.\",\"Herrera-Granda,  I., D.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\",\"Revelo-Fuelagán,  J.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019\",\"role\":\"author\",\"keyword\":[\"Alarm\",\"Artificial intelligence\",\"Drowsiness detection\",\"Human eye\",\"Image processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals\",\"type\":\"inproceedings\",\"year\":\"2019\",\"pages\":\"113-116\",\"websites\":\"https://ieeexplore.ieee.org/document/8667585/\",\"month\":\"2\",\"publisher\":\"IEEE\",\"id\":\"4400fe53-d983-3bea-a5f0-8d4c35f5eea9\",\"created\":\"2022-02-02T07:25:45.442Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:45.442Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Ordonez-Bolanos2019\",\"private_publication\":false,\"abstract\":\"Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.\",\"bibtype\":\"inproceedings\",\"author\":\"Ordonez-Bolanos, O. A. and Gomez-Lara, J. F. and Becerra, M. A. and Peluffo-Ordonez, D. H. and Duque-Mejia, C. M. and Medrano-David, D. and Mejia-Arboleda, C.\",\"doi\":\"10.1109/LASCAS.2019.8667585\",\"booktitle\":\"2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)\",\"bibtex\":\"@inproceedings{\\n title = {Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals},\\n type = {inproceedings},\\n year = {2019},\\n pages = {113-116},\\n websites = {https://ieeexplore.ieee.org/document/8667585/},\\n month = {2},\\n publisher = {IEEE},\\n id = {4400fe53-d983-3bea-a5f0-8d4c35f5eea9},\\n created = {2022-02-02T07:25:45.442Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:45.442Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Ordonez-Bolanos2019},\\n private_publication = {false},\\n abstract = {Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.},\\n bibtype = {inproceedings},\\n author = {Ordonez-Bolanos, O. A. and Gomez-Lara, J. F. and Becerra, M. A. and Peluffo-Ordonez, D. H. and Duque-Mejia, C. M. and Medrano-David, D. and Mejia-Arboleda, C.},\\n doi = {10.1109/LASCAS.2019.8667585},\\n booktitle = {2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)}\\n}\",\"author_short\":[\"Ordonez-Bolanos,  O., A.\",\"Gomez-Lara,  J., F.\",\"Becerra,  M., A.\",\"Peluffo-Ordonez,  D., H.\",\"Duque-Mejia,  C., M.\",\"Medrano-David,  D.\",\"Mejia-Arboleda,  C.\"],\"urls\":{\"Website\":\"https://ieeexplore.ieee.org/document/8667585/\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2019\",\"keywords\":\"Emotion recognition,Improved complementary ensemble empirical mode dec,Multimodal,Physiological signals,Signal processing\",\"pages\":\"351-362\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-14799-0_30\",\"id\":\"6cf5ffee-cfb5-370e-81d8-26953a492962\",\"created\":\"2022-02-02T07:25:45.719Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:45.719Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Gomez-Lara2019\",\"private_publication\":false,\"abstract\":\"The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.\",\"bibtype\":\"inbook\",\"author\":\"Gómez-Lara, J. F. and Ordóñez-Bolaños, O. A. and Becerra, M. A. and Castro-Ospina, A. E. and Mejía-Arboleda, C. and Duque-Mejía, C. and Rodriguez, J. and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1007/978-3-030-14799-0_30\",\"chapter\":\"Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2019},\\n keywords = {Emotion recognition,Improved complementary ensemble empirical mode dec,Multimodal,Physiological signals,Signal processing},\\n pages = {351-362},\\n websites = {http://link.springer.com/10.1007/978-3-030-14799-0_30},\\n id = {6cf5ffee-cfb5-370e-81d8-26953a492962},\\n created = {2022-02-02T07:25:45.719Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:45.719Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Gomez-Lara2019},\\n private_publication = {false},\\n abstract = {The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.},\\n bibtype = {inbook},\\n author = {Gómez-Lara, J. F. and Ordóñez-Bolaños, O. A. and Becerra, M. A. and Castro-Ospina, A. E. and Mejía-Arboleda, C. and Duque-Mejía, C. and Rodriguez, J. and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1007/978-3-030-14799-0_30},\\n chapter = {Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Gómez-Lara,  J., F.\",\"Ordóñez-Bolaños,  O., A.\",\"Becerra,  M., A.\",\"Castro-Ospina,  A., E.\",\"Mejía-Arboleda,  C.\",\"Duque-Mejía,  C.\",\"Rodriguez,  J.\",\"Revelo-Fuelagán,  J.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-14799-0_30\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\",\"role\":\"author\",\"keyword\":[\"Emotion recognition\",\"Improved complementary ensemble empirical mode dec\",\"Multimodal\",\"Physiological signals\",\"Signal processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2019\",\"keywords\":\"Electroencephalographic signal,Machine learning,Semantic category,Semantic retrieval,Signal processing\",\"pages\":\"333-342\",\"volume\":\"11466 LNBI\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-17935-9_30\",\"publisher\":\"Springer Verlag\",\"id\":\"1041e065-3f93-3648-93a1-ece26d777002\",\"created\":\"2022-02-02T07:25:46.027Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:46.027Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Becerra2019\",\"private_publication\":false,\"abstract\":\"Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.\",\"bibtype\":\"inbook\",\"author\":\"Becerra, Miguel Alberto and Londoño-Delgado, Edwin and Botero-Henao, Oscar I. and Marín-Castrillón, Diana and Mejia-Arboleda, Cristian and Peluffo-Ordóñez, Diego Hernán\",\"doi\":\"10.1007/978-3-030-17935-9_30\",\"chapter\":\"Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2019},\\n keywords = {Electroencephalographic signal,Machine learning,Semantic category,Semantic retrieval,Signal processing},\\n pages = {333-342},\\n volume = {11466 LNBI},\\n websites = {http://link.springer.com/10.1007/978-3-030-17935-9_30},\\n publisher = {Springer Verlag},\\n id = {1041e065-3f93-3648-93a1-ece26d777002},\\n created = {2022-02-02T07:25:46.027Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:46.027Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Becerra2019},\\n private_publication = {false},\\n abstract = {Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.},\\n bibtype = {inbook},\\n author = {Becerra, Miguel Alberto and Londoño-Delgado, Edwin and Botero-Henao, Oscar I. and Marín-Castrillón, Diana and Mejia-Arboleda, Cristian and Peluffo-Ordóñez, Diego Hernán},\\n doi = {10.1007/978-3-030-17935-9_30},\\n chapter = {Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Becerra,  M., A.\",\"Londoño-Delgado,  E.\",\"Botero-Henao,  O., I.\",\"Marín-Castrillón,  D.\",\"Mejia-Arboleda,  C.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-17935-9_30\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\",\"role\":\"author\",\"keyword\":[\"Electroencephalographic signal\",\"Machine learning\",\"Semantic category\",\"Semantic retrieval\",\"Signal processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results\",\"type\":\"inproceedings\",\"year\":\"2019\",\"keywords\":\"Emotion recognition,Physiological signals,Signal processing\",\"pages\":\"363-373\",\"volume\":\"11466 LNBI\",\"websites\":\"https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33\",\"publisher\":\"Springer Verlag\",\"id\":\"2fd81d28-93a5-3eed-a1ab-49d718fe8de7\",\"created\":\"2022-02-02T07:25:46.321Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:46.321Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.\",\"bibtype\":\"inproceedings\",\"author\":\"Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán\",\"doi\":\"10.1007/978-3-030-17935-9_33\",\"booktitle\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inproceedings{\\n title = {Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results},\\n type = {inproceedings},\\n year = {2019},\\n keywords = {Emotion recognition,Physiological signals,Signal processing},\\n pages = {363-373},\\n volume = {11466 LNBI},\\n websites = {https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33},\\n publisher = {Springer Verlag},\\n id = {2fd81d28-93a5-3eed-a1ab-49d718fe8de7},\\n created = {2022-02-02T07:25:46.321Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:46.321Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.},\\n bibtype = {inproceedings},\\n author = {Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},\\n doi = {10.1007/978-3-030-17935-9_33},\\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Londoño-Delgado,  E.\",\"Becerra,  M., A.\",\"Duque-Mejía,  C., M.\",\"Zapata,  J., C.\",\"Mejía-Arboleda,  C.\",\"Castro-Ospina,  A., E.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019\",\"role\":\"author\",\"keyword\":[\"Emotion recognition\",\"Physiological signals\",\"Signal processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques\",\"type\":\"inproceedings\",\"year\":\"2018\",\"keywords\":\"intelligent glove,knn,prototype selection,sign language\",\"pages\":\"1-5\",\"websites\":\"https://ieeexplore.ieee.org/document/8580268/\",\"month\":\"10\",\"publisher\":\"IEEE\",\"id\":\"88cf6012-fe39-33e4-a203-667c31dad596\",\"created\":\"2022-02-02T07:25:46.595Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:46.595Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Rosero-Montalvo2018a\",\"private_publication\":false,\"abstract\":\"We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.\",\"bibtype\":\"inproceedings\",\"author\":\"Rosero-Montalvo, Paul D. and Godoy-Trujillo, Pamela and Flores-Bosmediano, Edison and Carrascal-Garcia, Jorge and Otero-Potosi, Santiago and Benitez-Pereira, Henry and Peluffo-Ordonez, Diego H.\",\"doi\":\"10.1109/ETCM.2018.8580268\",\"booktitle\":\"2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)\",\"bibtex\":\"@inproceedings{\\n title = {Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques},\\n type = {inproceedings},\\n year = {2018},\\n keywords = {intelligent glove,knn,prototype selection,sign language},\\n pages = {1-5},\\n websites = {https://ieeexplore.ieee.org/document/8580268/},\\n month = {10},\\n publisher = {IEEE},\\n id = {88cf6012-fe39-33e4-a203-667c31dad596},\\n created = {2022-02-02T07:25:46.595Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:46.595Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Rosero-Montalvo2018a},\\n private_publication = {false},\\n abstract = {We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.},\\n bibtype = {inproceedings},\\n author = {Rosero-Montalvo, Paul D. and Godoy-Trujillo, Pamela and Flores-Bosmediano, Edison and Carrascal-Garcia, Jorge and Otero-Potosi, Santiago and Benitez-Pereira, Henry and Peluffo-Ordonez, Diego H.},\\n doi = {10.1109/ETCM.2018.8580268},\\n booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}\\n}\",\"author_short\":[\"Rosero-Montalvo,  P., D.\",\"Godoy-Trujillo,  P.\",\"Flores-Bosmediano,  E.\",\"Carrascal-Garcia,  J.\",\"Otero-Potosi,  S.\",\"Benitez-Pereira,  H.\",\"Peluffo-Ordonez,  D., H.\"],\"urls\":{\"Website\":\"https://ieeexplore.ieee.org/document/8580268/\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018\",\"role\":\"author\",\"keyword\":[\"intelligent glove\",\"knn\",\"prototype selection\",\"sign language\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2018\",\"keywords\":\"Fingertip segmentation,NPR measurement,Thermal hand images,Thermorregulation\",\"pages\":\"455-463\",\"websites\":\"http://link.springer.com/10.1007/978-3-319-92639-1_38\",\"id\":\"b1c9a55d-a816-3a99-84f3-5482a3049548\",\"created\":\"2022-02-02T07:25:46.867Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:46.867Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Castro-Ospina2018\",\"private_publication\":false,\"abstract\":\"Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.\",\"bibtype\":\"inbook\",\"author\":\"Castro-Ospina, A. E. and Correa-Mira, A. M. and Herrera-Granda, I. D. and Peluffo-Ordóñez, D. H. and Fandiño-Toro, H. A.\",\"doi\":\"10.1007/978-3-319-92639-1_38\",\"chapter\":\"Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2018},\\n keywords = {Fingertip segmentation,NPR measurement,Thermal hand images,Thermorregulation},\\n pages = {455-463},\\n websites = {http://link.springer.com/10.1007/978-3-319-92639-1_38},\\n id = {b1c9a55d-a816-3a99-84f3-5482a3049548},\\n created = {2022-02-02T07:25:46.867Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:46.867Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Castro-Ospina2018},\\n private_publication = {false},\\n abstract = {Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.},\\n bibtype = {inbook},\\n author = {Castro-Ospina, A. E. and Correa-Mira, A. M. and Herrera-Granda, I. D. and Peluffo-Ordóñez, D. H. and Fandiño-Toro, H. A.},\\n doi = {10.1007/978-3-319-92639-1_38},\\n chapter = {Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Castro-Ospina,  A., E.\",\"Correa-Mira,  A., M.\",\"Herrera-Granda,  I., D.\",\"Peluffo-Ordóñez,  D., H.\",\"Fandiño-Toro,  H., A.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-319-92639-1_38\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\",\"role\":\"author\",\"keyword\":[\"Fingertip segmentation\",\"NPR measurement\",\"Thermal hand images\",\"Thermorregulation\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2018\",\"keywords\":\"ANN,EMG signals,Feature extraction,KNN,Parzen\",\"pages\":\"368-375\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-01132-1_42\",\"id\":\"c11c79f3-1069-34d7-a120-f55b8bd1e76e\",\"created\":\"2022-02-02T07:25:47.120Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:47.120Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Lasso-Arciniegas2018\",\"private_publication\":false,\"abstract\":\"The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.\",\"bibtype\":\"inbook\",\"author\":\"Lasso-Arciniegas, Laura and Viveros-Melo, Andres and Salazar-Castro, José A. and Becerra, Miguel A. and Castro-Ospina, Andrés Eduardo and Revelo-Fuelagán, E. Javier and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1007/978-3-030-01132-1_42\",\"chapter\":\"Movement Identification in EMG Signals Using Machine Learning: A Comparative Study\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2018},\\n keywords = {ANN,EMG signals,Feature extraction,KNN,Parzen},\\n pages = {368-375},\\n websites = {http://link.springer.com/10.1007/978-3-030-01132-1_42},\\n id = {c11c79f3-1069-34d7-a120-f55b8bd1e76e},\\n created = {2022-02-02T07:25:47.120Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:47.120Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Lasso-Arciniegas2018},\\n private_publication = {false},\\n abstract = {The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.},\\n bibtype = {inbook},\\n author = {Lasso-Arciniegas, Laura and Viveros-Melo, Andres and Salazar-Castro, José A. and Becerra, Miguel A. and Castro-Ospina, Andrés Eduardo and Revelo-Fuelagán, E. Javier and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1007/978-3-030-01132-1_42},\\n chapter = {Movement Identification in EMG Signals Using Machine Learning: A Comparative Study},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Lasso-Arciniegas,  L.\",\"Viveros-Melo,  A.\",\"Salazar-Castro,  J., A.\",\"Becerra,  M., A.\",\"Castro-Ospina,  A., E.\",\"Revelo-Fuelagán,  E., J.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-01132-1_42\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\",\"role\":\"author\",\"keyword\":[\"ANN\",\"EMG signals\",\"Feature extraction\",\"KNN\",\"Parzen\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2018\",\"keywords\":\"Classification,EMG signals,Movements selection,Wavelet\",\"pages\":\"139-149\",\"websites\":\"http://link.springer.com/10.1007/978-3-319-98998-3_11\",\"id\":\"18798fe2-4e14-3b67-9d3a-4302a79a7d02\",\"created\":\"2022-02-02T07:25:47.482Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:47.482Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Viveros-Melo2018\",\"private_publication\":false,\"abstract\":\"Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).\",\"bibtype\":\"inbook\",\"author\":\"Viveros-Melo, A. and Lasso-Arciniegas, L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E. and Revelo-Fuelagán, E. J.\",\"doi\":\"10.1007/978-3-319-98998-3_11\",\"chapter\":\"Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results\",\"title\":\"Communications in Computer and Information Science\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2018},\\n keywords = {Classification,EMG signals,Movements selection,Wavelet},\\n pages = {139-149},\\n websites = {http://link.springer.com/10.1007/978-3-319-98998-3_11},\\n id = {18798fe2-4e14-3b67-9d3a-4302a79a7d02},\\n created = {2022-02-02T07:25:47.482Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:47.482Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Viveros-Melo2018},\\n private_publication = {false},\\n abstract = {Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).},\\n bibtype = {inbook},\\n author = {Viveros-Melo, A. and Lasso-Arciniegas, L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E. and Revelo-Fuelagán, E. J.},\\n doi = {10.1007/978-3-319-98998-3_11},\\n chapter = {Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results},\\n title = {Communications in Computer and Information Science}\\n}\",\"author_short\":[\"Viveros-Melo,  A.\",\"Lasso-Arciniegas,  L.\",\"Salazar-Castro,  J., A.\",\"Peluffo-Ordóñez,  D., H.\",\"Becerra,  M., A.\",\"Castro-Ospina,  A., E.\",\"Revelo-Fuelagán,  E., J.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-319-98998-3_11\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018\",\"role\":\"author\",\"keyword\":[\"Classification\",\"EMG signals\",\"Movements selection\",\"Wavelet\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2018\",\"keywords\":\"Electroencephalographic signal,Sensorial stimulus,Signal processing,Tactile pleasantness\",\"pages\":\"309-316\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-01132-1_35\",\"id\":\"742d67ee-3e64-37d1-b66e-8c3feb2d7b40\",\"created\":\"2022-02-02T07:25:47.802Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:47.802Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Becerra2018b\",\"private_publication\":false,\"abstract\":\"Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.\",\"bibtype\":\"inbook\",\"author\":\"Becerra, Miguel A. and Londoño-Delgado, Edwin and Pelaez-Becerra, Sonia M. and Castro-Ospina, Andrés Eduardo and Mejia-Arboleda, Cristian and Durango, Julián and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1007/978-3-030-01132-1_35\",\"chapter\":\"Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2018},\\n keywords = {Electroencephalographic signal,Sensorial stimulus,Signal processing,Tactile pleasantness},\\n pages = {309-316},\\n websites = {http://link.springer.com/10.1007/978-3-030-01132-1_35},\\n id = {742d67ee-3e64-37d1-b66e-8c3feb2d7b40},\\n created = {2022-02-02T07:25:47.802Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:47.802Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Becerra2018b},\\n private_publication = {false},\\n abstract = {Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.},\\n bibtype = {inbook},\\n author = {Becerra, Miguel A. and Londoño-Delgado, Edwin and Pelaez-Becerra, Sonia M. and Castro-Ospina, Andrés Eduardo and Mejia-Arboleda, Cristian and Durango, Julián and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1007/978-3-030-01132-1_35},\\n chapter = {Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Becerra,  M., A.\",\"Londoño-Delgado,  E.\",\"Pelaez-Becerra,  S., M.\",\"Castro-Ospina,  A., E.\",\"Mejia-Arboleda,  C.\",\"Durango,  J.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-01132-1_35\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\",\"role\":\"author\",\"keyword\":[\"Electroencephalographic signal\",\"Sensorial stimulus\",\"Signal processing\",\"Tactile pleasantness\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2018\",\"keywords\":\"Electroencephalographic signal,Emotion,Odor pleasantness,Sensorial stimuli,Signal processing\",\"pages\":\"128-138\",\"websites\":\"http://link.springer.com/10.1007/978-3-319-98998-3_10\",\"id\":\"c4f9d909-7b5f-338d-af73-b2609bee48ce\",\"created\":\"2022-02-02T07:25:48.229Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-02-02T07:25:48.229Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Becerra2018a\",\"private_publication\":false,\"abstract\":\"Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.\",\"bibtype\":\"inbook\",\"author\":\"Becerra, M. A. and Londoño-Delgado, E. and Pelaez-Becerra, S. M. and Serna-Guarín, L. and Castro-Ospina, A. E. and Marin-Castrillón, D. and Peluffo-Ordóñez, D. H.\",\"doi\":\"10.1007/978-3-319-98998-3_10\",\"chapter\":\"Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States\",\"title\":\"Communications in Computer and Information Science\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2018},\\n keywords = {Electroencephalographic signal,Emotion,Odor pleasantness,Sensorial stimuli,Signal processing},\\n pages = {128-138},\\n websites = {http://link.springer.com/10.1007/978-3-319-98998-3_10},\\n id = {c4f9d909-7b5f-338d-af73-b2609bee48ce},\\n created = {2022-02-02T07:25:48.229Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-02-02T07:25:48.229Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Becerra2018a},\\n private_publication = {false},\\n abstract = {Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.},\\n bibtype = {inbook},\\n author = {Becerra, M. A. and Londoño-Delgado, E. and Pelaez-Becerra, S. M. and Serna-Guarín, L. and Castro-Ospina, A. E. and Marin-Castrillón, D. and Peluffo-Ordóñez, D. H.},\\n doi = {10.1007/978-3-319-98998-3_10},\\n chapter = {Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States},\\n title = {Communications in Computer and Information Science}\\n}\",\"author_short\":[\"Becerra,  M., A.\",\"Londoño-Delgado,  E.\",\"Pelaez-Becerra,  S., M.\",\"Serna-Guarín,  L.\",\"Castro-Ospina,  A., E.\",\"Marin-Castrillón,  D.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-319-98998-3_10\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018\",\"role\":\"author\",\"keyword\":[\"Electroencephalographic signal\",\"Emotion\",\"Odor pleasantness\",\"Sensorial stimuli\",\"Signal processing\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview\",\"type\":\"article\",\"year\":\"2022\",\"volume\":\"22\",\"websites\":\"https://www.mdpi.com/1424-8220/22/5/2028\",\"id\":\"d7d54a6d-055f-32d0-b9cb-8fd937f6d931\",\"created\":\"2022-03-05T01:20:50.477Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-03-05T01:20:50.477Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"s22052028\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Recent engineering and neuroscience applications have led to the development of brain&ndash;computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.\",\"bibtype\":\"article\",\"author\":\"Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H\",\"doi\":\"10.3390/s22052028\",\"journal\":\"Sensors\",\"number\":\"5\",\"bibtex\":\"@article{\\n title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview},\\n type = {article},\\n year = {2022},\\n volume = {22},\\n websites = {https://www.mdpi.com/1424-8220/22/5/2028},\\n id = {d7d54a6d-055f-32d0-b9cb-8fd937f6d931},\\n created = {2022-03-05T01:20:50.477Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-03-05T01:20:50.477Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {s22052028},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Recent engineering and neuroscience applications have led to the development of brain&ndash;computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},\\n bibtype = {article},\\n author = {Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\\n doi = {10.3390/s22052028},\\n journal = {Sensors},\\n number = {5}\\n}\",\"author_short\":[\"Asanza,  V.\",\"Peláez,  E.\",\"Loayza,  F.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://www.mdpi.com/1424-8220/22/5/2028\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":5,\"html\":\"\"},{\"title\":\"Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks\",\"type\":\"article\",\"year\":\"2022\",\"volume\":\"12\",\"websites\":\"https://www.mdpi.com/2076-3417/12/12/6258\",\"id\":\"99fceab7-8a81-396f-bf1b-b4c6f422df69\",\"created\":\"2022-06-22T00:17:33.358Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-06-22T00:17:33.358Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"app12126258\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging &ldquo;hands-on&rdquo; training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.\",\"bibtype\":\"article\",\"author\":\"Alpala, Luis Omar and Quiroga-Parra, Darío J and Torres, Juan Carlos and Peluffo-Ordóñez, Diego H\",\"doi\":\"10.3390/app12126258\",\"journal\":\"Applied Sciences\",\"number\":\"12\",\"bibtex\":\"@article{\\n title = {Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks},\\n type = {article},\\n year = {2022},\\n volume = {12},\\n websites = {https://www.mdpi.com/2076-3417/12/12/6258},\\n id = {99fceab7-8a81-396f-bf1b-b4c6f422df69},\\n created = {2022-06-22T00:17:33.358Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-06-22T00:17:33.358Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {app12126258},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging &ldquo;hands-on&rdquo; training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.},\\n bibtype = {article},\\n author = {Alpala, Luis Omar and Quiroga-Parra, Darío J and Torres, Juan Carlos and Peluffo-Ordóñez, Diego H},\\n doi = {10.3390/app12126258},\\n journal = {Applied Sciences},\\n number = {12}\\n}\",\"author_short\":[\"Alpala,  L., O.\",\"Quiroga-Parra,  D., J.\",\"Torres,  J., C.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://www.mdpi.com/2076-3417/12/12/6258\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2019\",\"keywords\":\"Electroencephalogram (EEG),Epilepsy diagnosis,K-Nearest Neighbors (KNN),Linear Discriminant Analysis (LDA),Quadratic Discriminant Analysis (QDA),Support Vector Machine (SVM)\",\"pages\":\"189-198\",\"volume\":\"11976 LNAI\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-37078-7_19\",\"id\":\"e6a1062d-c1eb-3698-b271-4811a1de0b2e\",\"created\":\"2022-08-27T03:33:14.408Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-08-27T03:33:14.408Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.\",\"bibtype\":\"inbook\",\"author\":\"Vega-Gualán, Emil and Vargas, Andrés and Becerra, Miguel and Umaquinga, Ana and Riascos, Jaime A. and Peluffo, Diego\",\"doi\":\"10.1007/978-3-030-37078-7_19\",\"chapter\":\"Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2019},\\n keywords = {Electroencephalogram (EEG),Epilepsy diagnosis,K-Nearest Neighbors (KNN),Linear Discriminant Analysis (LDA),Quadratic Discriminant Analysis (QDA),Support Vector Machine (SVM)},\\n pages = {189-198},\\n volume = {11976 LNAI},\\n websites = {http://link.springer.com/10.1007/978-3-030-37078-7_19},\\n id = {e6a1062d-c1eb-3698-b271-4811a1de0b2e},\\n created = {2022-08-27T03:33:14.408Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-08-27T03:33:14.408Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.},\\n bibtype = {inbook},\\n author = {Vega-Gualán, Emil and Vargas, Andrés and Becerra, Miguel and Umaquinga, Ana and Riascos, Jaime A. and Peluffo, Diego},\\n doi = {10.1007/978-3-030-37078-7_19},\\n chapter = {Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Vega-Gualán,  E.\",\"Vargas,  A.\",\"Becerra,  M.\",\"Umaquinga,  A.\",\"Riascos,  J., A.\",\"Peluffo,  D.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-37078-7_19\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\",\"role\":\"author\",\"keyword\":[\"Electroencephalogram (EEG)\",\"Epilepsy diagnosis\",\"K-Nearest Neighbors (KNN)\",\"Linear Discriminant Analysis (LDA)\",\"Quadratic Discriminant Analysis (QDA)\",\"Support Vector Machine (SVM)\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Electrooculography Signals Classification for FPGA-based Human-Computer Interaction\",\"type\":\"inproceedings\",\"year\":\"2022\",\"pages\":\"1-7\",\"websites\":\"https://ieeexplore.ieee.org/document/9989664\",\"id\":\"32da4701-f45e-3ed3-bd93-6a874cfca39f\",\"created\":\"2022-12-28T23:00:00.348Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2022-12-28T23:00:00.348Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"9989664\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.\",\"bibtype\":\"inproceedings\",\"author\":\"Asanza, Víctor and Miranda, Jesús and Miranda, Jocelyn and Rivas, Leiber and Hernan Peluffo-Ordóñez, Diego and Pelaez, Enrique and Loayza, Francis and Alejandro, Otilia\",\"doi\":\"10.1109/ANDESCON56260.2022.9989664\",\"booktitle\":\"2022 IEEE ANDESCON\",\"bibtex\":\"@inproceedings{\\n title = {Electrooculography Signals Classification for FPGA-based Human-Computer Interaction},\\n type = {inproceedings},\\n year = {2022},\\n pages = {1-7},\\n websites = {https://ieeexplore.ieee.org/document/9989664},\\n id = {32da4701-f45e-3ed3-bd93-6a874cfca39f},\\n created = {2022-12-28T23:00:00.348Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2022-12-28T23:00:00.348Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {9989664},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.},\\n bibtype = {inproceedings},\\n author = {Asanza, Víctor and Miranda, Jesús and Miranda, Jocelyn and Rivas, Leiber and Hernan Peluffo-Ordóñez, Diego and Pelaez, Enrique and Loayza, Francis and Alejandro, Otilia},\\n doi = {10.1109/ANDESCON56260.2022.9989664},\\n booktitle = {2022 IEEE ANDESCON}\\n}\",\"author_short\":[\"Asanza,  V.\",\"Miranda,  J.\",\"Miranda,  J.\",\"Rivas,  L.\",\"Hernan Peluffo-Ordóñez,  D.\",\"Pelaez,  E.\",\"Loayza,  F.\",\"Alejandro,  O.\"],\"urls\":{\"Website\":\"https://ieeexplore.ieee.org/document/9989664\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia\",\"type\":\"article\",\"year\":\"2023\",\"keywords\":\"Internet of Things,Scrum,agile methodologies,digital twins,virtual simulation\",\"pages\":\"0-2\",\"volume\":\"32\",\"websites\":\"https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254\",\"id\":\"3a7d4687-e775-3697-8faa-4c4e5841439b\",\"created\":\"2023-02-23T09:38:19.689Z\",\"file_attached\":\"true\",\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2023-02-23T09:38:37.406Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Digital Twins (DTs) have the potential to revolutionize the manufacturing, construction, maintenance, and control of industrial processes due to their ability to bridge the physical and digital realms. However, creating complex digital concepts requires carefully implementing appropriate methodologies and processes. This study presents a prototype digital twin of the art and technology laboratories at the Departmental Institute of Fine Arts in Cali, Colombia. We employ the Scrum agile development methodology to streamline the design and development process of the IoT-ATL prototype. A three-layer IoT architecture is established; it facilitates mapping the components of the digital twin and the physical elements to be simulated. The IoT-ATL prototype allows students and teachers to interact and learn about the physical state of the laboratory in a digital environment, thus increasing visibility of the availability and use of technological elements in the space. Keywords:\",\"bibtype\":\"article\",\"author\":\"Ordóñez-Bolaños, Oswaldo Andrés and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán\",\"journal\":\"Revista de Facultad de Ingeniería UPTC\",\"number\":\"March\",\"bibtex\":\"@article{\\n title = {IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia},\\n type = {article},\\n year = {2023},\\n keywords = {Internet of Things,Scrum,agile methodologies,digital twins,virtual simulation},\\n pages = {0-2},\\n volume = {32},\\n websites = {https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254},\\n id = {3a7d4687-e775-3697-8faa-4c4e5841439b},\\n created = {2023-02-23T09:38:19.689Z},\\n file_attached = {true},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2023-02-23T09:38:37.406Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Digital Twins (DTs) have the potential to revolutionize the manufacturing, construction, maintenance, and control of industrial processes due to their ability to bridge the physical and digital realms. However, creating complex digital concepts requires carefully implementing appropriate methodologies and processes. This study presents a prototype digital twin of the art and technology laboratories at the Departmental Institute of Fine Arts in Cali, Colombia. We employ the Scrum agile development methodology to streamline the design and development process of the IoT-ATL prototype. A three-layer IoT architecture is established; it facilitates mapping the components of the digital twin and the physical elements to be simulated. The IoT-ATL prototype allows students and teachers to interact and learn about the physical state of the laboratory in a digital environment, thus increasing visibility of the availability and use of technological elements in the space. Keywords:},\\n bibtype = {article},\\n author = {Ordóñez-Bolaños, Oswaldo Andrés and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán},\\n journal = {Revista de Facultad de Ingeniería UPTC},\\n number = {March}\\n}\",\"author_short\":[\"Ordóñez-Bolaños,  O., A.\",\"Sierra-Martínez,  L., M.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/6cadbe30-0682-5d3b-7a2b-cb9d21fc88dc/2023_RevistaFI_UPTC_IoT_ATLPrototypeOfADigitalTwin.pdf.pdf\",\"Website\":\"https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023\",\"role\":\"author\",\"keyword\":[\"Internet of Things\",\"Scrum\",\"agile methodologies\",\"digital twins\",\"virtual simulation\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network\",\"type\":\"article\",\"year\":\"2023\",\"volume\":\"8\",\"websites\":\"https://www.mdpi.com/2313-7673/8/2/255\",\"id\":\"3d3573b3-2a8f-3e53-aa22-47342ea079a5\",\"created\":\"2023-06-17T22:55:15.947Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2023-06-17T22:55:15.947Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"biomimetics8020255\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.\",\"bibtype\":\"article\",\"author\":\"Avilés-Mendoza, Karla and Gaibor-León, Neil George and Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H\",\"doi\":\"10.3390/biomimetics8020255\",\"journal\":\"Biomimetics\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network},\\n type = {article},\\n year = {2023},\\n volume = {8},\\n websites = {https://www.mdpi.com/2313-7673/8/2/255},\\n id = {3d3573b3-2a8f-3e53-aa22-47342ea079a5},\\n created = {2023-06-17T22:55:15.947Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2023-06-17T22:55:15.947Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {biomimetics8020255},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.},\\n bibtype = {article},\\n author = {Avilés-Mendoza, Karla and Gaibor-León, Neil George and Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\\n doi = {10.3390/biomimetics8020255},\\n journal = {Biomimetics},\\n number = {2}\\n}\",\"author_short\":[\"Avilés-Mendoza,  K.\",\"Gaibor-León,  N., G.\",\"Asanza,  V.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://www.mdpi.com/2313-7673/8/2/255\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks\",\"type\":\"article\",\"year\":\"2023\",\"keywords\":\"Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI\",\"pages\":\"109540\",\"volume\":\"50\",\"websites\":\"https://www.sciencedirect.com/science/article/pii/S2352340923006406\",\"id\":\"85847825-d177-389b-a562-2690e6cd35a1\",\"created\":\"2023-09-22T18:07:54.476Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2023-09-22T18:07:54.476Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ASANZA2023109540\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.\",\"bibtype\":\"article\",\"author\":\"Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber\",\"doi\":\"https://doi.org/10.1016/j.dib.2023.109540\",\"journal\":\"Data in Brief\",\"bibtex\":\"@article{\\n title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks},\\n type = {article},\\n year = {2023},\\n keywords = {Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI},\\n pages = {109540},\\n volume = {50},\\n websites = {https://www.sciencedirect.com/science/article/pii/S2352340923006406},\\n id = {85847825-d177-389b-a562-2690e6cd35a1},\\n created = {2023-09-22T18:07:54.476Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2023-09-22T18:07:54.476Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {ASANZA2023109540},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},\\n bibtype = {article},\\n author = {Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber},\\n doi = {https://doi.org/10.1016/j.dib.2023.109540},\\n journal = {Data in Brief}\\n}\",\"author_short\":[\"Asanza,  V.\",\"Lorente-Leyva,  L., L.\",\"Peluffo-Ordóñez,  D., H.\",\"Montoya,  D.\",\"Gonzalez,  K.\"],\"urls\":{\"Website\":\"https://www.sciencedirect.com/science/article/pii/S2352340923006406\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023\",\"role\":\"author\",\"keyword\":[\"Brain–computer interface\",\"Electroencephalography\",\"Experimental methodology\",\"Motor imagery task\",\"Motor task\",\"OpenBCI\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals\",\"type\":\"article\",\"year\":\"2023\",\"keywords\":\"artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion\",\"pages\":\"641-649\",\"volume\":\"56\",\"websites\":\"https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413\",\"id\":\"5d5be7d5-7828-38be-87a8-5f74e3afb5fd\",\"created\":\"2023-10-02T16:29:47.648Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"55483c42-1c6e-3138-8b55-8d557318ba00\",\"last_modified\":\"2023-10-02T16:29:47.648Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023\",\"private_publication\":false,\"abstract\":\"Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.\",\"bibtype\":\"article\",\"author\":\"Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.\",\"journal\":\"Journal Européen des Systèmes Automatisés\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals},\\n type = {article},\\n year = {2023},\\n keywords = {artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion},\\n pages = {641-649},\\n volume = {56},\\n websites = {https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413},\\n id = {5d5be7d5-7828-38be-87a8-5f74e3afb5fd},\\n created = {2023-10-02T16:29:47.648Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\\n last_modified = {2023-10-02T16:29:47.648Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023},\\n private_publication = {false},\\n abstract = {Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.},\\n bibtype = {article},\\n author = {Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.},\\n journal = {Journal Européen des Systèmes Automatisés},\\n number = {4}\\n}\",\"author_short\":[\"Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez,  D.\"],\"urls\":{\"Website\":\"https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023\",\"role\":\"author\",\"keyword\":[\"artificial\",\"bioelectric signal\",\"electromyography\",\"intelligence\",\"myoelectric prosthesis\",\"neural network\",\"sensor fusion\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@inproceedings{
 title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},
 type = {inproceedings},
 year = {2021},
 pages = {55-67},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {e77821ff-b41a-304c-8ccb-cc74a3c124ad},
 created = {2022-02-02T07:25:42.987Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:42.987Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-030-89654-6_5},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\% rating yield using the proposed methodology with the Fast Tree algorithm.},
 bibtype = {inproceedings},
 author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},
 editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},
 booktitle = {Applied Informatics}
}

@article{
 title = {BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control},
 type = {article},
 year = {2021},
 keywords = {Bio-signals analysis,Brain Computer Interface,Embedded Systems,FPGA,Neural Networks},
 pages = {364-369},
 volume = {54},
 websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016876},
 id = {19c1b4bf-e5af-3508-bea8-6087ba4d3f97},
 created = {2022-02-02T07:25:43.282Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:43.282Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {CONSTANTINE2021364},
 source_type = {article},
 private_publication = {false},
 abstract = {This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.},
 bibtype = {article},
 author = {Constantine, Alisson and Asanza, Víctor and Loayza, Francis R and Peláez, Enrique and Peluffo-Ordóñez, Diego},
 doi = {https://doi.org/10.1016/j.ifacol.2021.10.283},
 journal = {IFAC-PapersOnLine},
 number = {15}
}

@article{
 title = {Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset},
 type = {article},
 year = {2021},
 keywords = {Classification,Finger Tapping,Machine Learning,Parkinson’s disease},
 pages = {376-381},
 volume = {54},
 websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016906},
 id = {a0809ce4-85c2-3631-b319-abf677f9d692},
 created = {2022-02-02T07:25:43.665Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:43.665Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {ASANZA2021376},
 source_type = {article},
 private_publication = {false},
 abstract = {Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.},
 bibtype = {article},
 author = {Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique},
 doi = {https://doi.org/10.1016/j.ifacol.2021.10.285},
 journal = {IFAC-PapersOnLine},
 number = {15}
}

@article{
 title = {Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis},
 type = {article},
 year = {2020},
 keywords = {Gait analysis,IMU,Kalman filter,Knee-joint angle,Motion analysis},
 websites = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10814},
 id = {f9bdd20a-54e8-348c-a457-82751a93f902},
 created = {2022-02-02T07:25:43.940Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:43.940Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Mayorca-Torres2020},
 private_publication = {false},
 abstract = {Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient's knee-joint kinematics during a human gait.},
 bibtype = {article},
 author = {Mayorca-Torres, Dagoberto and Caicedo-Eraso, Julio C. and Peluffo-Ordóñez, Diego H.},
 doi = {10.18517/ijaseit.10.2.10814},
 journal = {International Journal on Advanced Science, Engineering and Information Technology}
}

@article{
 title = {Data fusion and information quality for biometric identification from multimodal signals},
 type = {article},
 year = {2020},
 keywords = {Biometry,Data fusion,Information quality,Signal processing},
 websites = {https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&fromopenview=true},
 id = {dc99d2ec-10d5-34ea-b53a-882ff1aae863},
 created = {2022-02-02T07:25:44.209Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:44.209Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2020a},
 private_publication = {false},
 abstract = {Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.},
 bibtype = {article},
 author = {Becerra, Miguel A. and Lasso-Arciniegas, Laura and Viveros, Andrés and Serna-Guarín, Leonardo and Peluffo-Ordóñez, Diego and Tobón, Catalina},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}
}

@inbook{
 type = {inbook},
 year = {2020},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7},
 id = {b6b9da88-86d3-3cd4-86ae-b66d5e3ba59e},
 created = {2022-02-02T07:25:44.514Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:44.514Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Patino-Alarcon2020},
 private_publication = {false},
 abstract = {This research presents an application of the clustering based on Thinking Based - Learning methodology (TBL), which offers guidelines to promote students’ reflective thinking. Within this methodology, the Intelligence Execution Theory (IET) tool will be used to encourage this kind of thinking in the classroom. Having in mind that, in any educational process, methodologies and pedagogical tools have a pivotal role as they are one of the bases for optimizing cognitive intelligence. In this case, it was given a priority to the potential development of a specific linguistic skill. This study presented a mixed methodology with an exploratory and descriptive scope. The main objective of this research was the clustering of the variables of functioning of the reading ability in the English language based on the TBL methodology and its behavior in the left hemisphere of the brain, specifically to analyze the improvement of the reading ability in the English language of the participants of this case study. With the expectation of generating sustainability of adequate levels of performance, instruction and learning of the English language of students at all levels.},
 bibtype = {inbook},
 author = {Patiño-Alarcón, Delio R. and Patiño-Alarcón, Fernando A. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-62833-8_7},
 chapter = {Clustering of Reading Ability Performance Variables in the English Language Based on TBL Methodology and Behavior in the Left Hemisphere of the Brain},
 title = {Communications in Computer and Information Science}
}

@inproceedings{
 title = {Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye},
 type = {inproceedings},
 year = {2019},
 keywords = {Alarm,Artificial intelligence,Drowsiness detection,Human eye,Image processing},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54},
 id = {8e496276-78ed-3f37-bf6f-bbc726f99b3e},
 created = {2022-02-02T07:25:44.943Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:44.943Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Herrera-Granda2019},
 private_publication = {false},
 abstract = {At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.},
 bibtype = {inproceedings},
 author = {Herrera-Granda, Erick P. and Caraguay-Procel, Jorge A. and Granda-Gudiño, Pedro D. and Herrera-Granda, Israel D. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H. and Revelo-Fuelagán, Javier},
 doi = {10.1007/978-3-030-14799-0_54},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inproceedings{
 title = {Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals},
 type = {inproceedings},
 year = {2019},
 pages = {113-116},
 websites = {https://ieeexplore.ieee.org/document/8667585/},
 month = {2},
 publisher = {IEEE},
 id = {4400fe53-d983-3bea-a5f0-8d4c35f5eea9},
 created = {2022-02-02T07:25:45.442Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:45.442Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ordonez-Bolanos2019},
 private_publication = {false},
 abstract = {Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.},
 bibtype = {inproceedings},
 author = {Ordonez-Bolanos, O. A. and Gomez-Lara, J. F. and Becerra, M. A. and Peluffo-Ordonez, D. H. and Duque-Mejia, C. M. and Medrano-David, D. and Mejia-Arboleda, C.},
 doi = {10.1109/LASCAS.2019.8667585},
 booktitle = {2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)}
}

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Emotion recognition,Improved complementary ensemble empirical mode dec,Multimodal,Physiological signals,Signal processing},
 pages = {351-362},
 websites = {http://link.springer.com/10.1007/978-3-030-14799-0_30},
 id = {6cf5ffee-cfb5-370e-81d8-26953a492962},
 created = {2022-02-02T07:25:45.719Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:45.719Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gomez-Lara2019},
 private_publication = {false},
 abstract = {The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.},
 bibtype = {inbook},
 author = {Gómez-Lara, J. F. and Ordóñez-Bolaños, O. A. and Becerra, M. A. and Castro-Ospina, A. E. and Mejía-Arboleda, C. and Duque-Mejía, C. and Rodriguez, J. and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-14799-0_30},
 chapter = {Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Electroencephalographic signal,Machine learning,Semantic category,Semantic retrieval,Signal processing},
 pages = {333-342},
 volume = {11466 LNBI},
 websites = {http://link.springer.com/10.1007/978-3-030-17935-9_30},
 publisher = {Springer Verlag},
 id = {1041e065-3f93-3648-93a1-ece26d777002},
 created = {2022-02-02T07:25:46.027Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:46.027Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2019},
 private_publication = {false},
 abstract = {Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.},
 bibtype = {inbook},
 author = {Becerra, Miguel Alberto and Londoño-Delgado, Edwin and Botero-Henao, Oscar I. and Marín-Castrillón, Diana and Mejia-Arboleda, Cristian and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-17935-9_30},
 chapter = {Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inproceedings{
 title = {Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results},
 type = {inproceedings},
 year = {2019},
 keywords = {Emotion recognition,Physiological signals,Signal processing},
 pages = {363-373},
 volume = {11466 LNBI},
 websites = {https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33},
 publisher = {Springer Verlag},
 id = {2fd81d28-93a5-3eed-a1ab-49d718fe8de7},
 created = {2022-02-02T07:25:46.321Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:46.321Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.},
 bibtype = {inproceedings},
 author = {Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-17935-9_33},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inproceedings{
 title = {Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques},
 type = {inproceedings},
 year = {2018},
 keywords = {intelligent glove,knn,prototype selection,sign language},
 pages = {1-5},
 websites = {https://ieeexplore.ieee.org/document/8580268/},
 month = {10},
 publisher = {IEEE},
 id = {88cf6012-fe39-33e4-a203-667c31dad596},
 created = {2022-02-02T07:25:46.595Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:46.595Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rosero-Montalvo2018a},
 private_publication = {false},
 abstract = {We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.},
 bibtype = {inproceedings},
 author = {Rosero-Montalvo, Paul D. and Godoy-Trujillo, Pamela and Flores-Bosmediano, Edison and Carrascal-Garcia, Jorge and Otero-Potosi, Santiago and Benitez-Pereira, Henry and Peluffo-Ordonez, Diego H.},
 doi = {10.1109/ETCM.2018.8580268},
 booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}
}

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Fingertip segmentation,NPR measurement,Thermal hand images,Thermorregulation},
 pages = {455-463},
 websites = {http://link.springer.com/10.1007/978-3-319-92639-1_38},
 id = {b1c9a55d-a816-3a99-84f3-5482a3049548},
 created = {2022-02-02T07:25:46.867Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:46.867Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Castro-Ospina2018},
 private_publication = {false},
 abstract = {Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.},
 bibtype = {inbook},
 author = {Castro-Ospina, A. E. and Correa-Mira, A. M. and Herrera-Granda, I. D. and Peluffo-Ordóñez, D. H. and Fandiño-Toro, H. A.},
 doi = {10.1007/978-3-319-92639-1_38},
 chapter = {Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {ANN,EMG signals,Feature extraction,KNN,Parzen},
 pages = {368-375},
 websites = {http://link.springer.com/10.1007/978-3-030-01132-1_42},
 id = {c11c79f3-1069-34d7-a120-f55b8bd1e76e},
 created = {2022-02-02T07:25:47.120Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:47.120Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lasso-Arciniegas2018},
 private_publication = {false},
 abstract = {The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.},
 bibtype = {inbook},
 author = {Lasso-Arciniegas, Laura and Viveros-Melo, Andres and Salazar-Castro, José A. and Becerra, Miguel A. and Castro-Ospina, Andrés Eduardo and Revelo-Fuelagán, E. Javier and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-01132-1_42},
 chapter = {Movement Identification in EMG Signals Using Machine Learning: A Comparative Study},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Classification,EMG signals,Movements selection,Wavelet},
 pages = {139-149},
 websites = {http://link.springer.com/10.1007/978-3-319-98998-3_11},
 id = {18798fe2-4e14-3b67-9d3a-4302a79a7d02},
 created = {2022-02-02T07:25:47.482Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:47.482Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Viveros-Melo2018},
 private_publication = {false},
 abstract = {Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).},
 bibtype = {inbook},
 author = {Viveros-Melo, A. and Lasso-Arciniegas, L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E. and Revelo-Fuelagán, E. J.},
 doi = {10.1007/978-3-319-98998-3_11},
 chapter = {Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results},
 title = {Communications in Computer and Information Science}
}

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Electroencephalographic signal,Sensorial stimulus,Signal processing,Tactile pleasantness},
 pages = {309-316},
 websites = {http://link.springer.com/10.1007/978-3-030-01132-1_35},
 id = {742d67ee-3e64-37d1-b66e-8c3feb2d7b40},
 created = {2022-02-02T07:25:47.802Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:47.802Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2018b},
 private_publication = {false},
 abstract = {Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.},
 bibtype = {inbook},
 author = {Becerra, Miguel A. and Londoño-Delgado, Edwin and Pelaez-Becerra, Sonia M. and Castro-Ospina, Andrés Eduardo and Mejia-Arboleda, Cristian and Durango, Julián and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-01132-1_35},
 chapter = {Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Electroencephalographic signal,Emotion,Odor pleasantness,Sensorial stimuli,Signal processing},
 pages = {128-138},
 websites = {http://link.springer.com/10.1007/978-3-319-98998-3_10},
 id = {c4f9d909-7b5f-338d-af73-b2609bee48ce},
 created = {2022-02-02T07:25:48.229Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:48.229Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2018a},
 private_publication = {false},
 abstract = {Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.},
 bibtype = {inbook},
 author = {Becerra, M. A. and Londoño-Delgado, E. and Pelaez-Becerra, S. M. and Serna-Guarín, L. and Castro-Ospina, A. E. and Marin-Castrillón, D. and Peluffo-Ordóñez, D. H.},
 doi = {10.1007/978-3-319-98998-3_10},
 chapter = {Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States},
 title = {Communications in Computer and Information Science}
}

@article{
 title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview},
 type = {article},
 year = {2022},
 volume = {22},
 websites = {https://www.mdpi.com/1424-8220/22/5/2028},
 id = {d7d54a6d-055f-32d0-b9cb-8fd937f6d931},
 created = {2022-03-05T01:20:50.477Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-03-05T01:20:50.477Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {s22052028},
 source_type = {article},
 private_publication = {false},
 abstract = {Recent engineering and neuroscience applications have led to the development of brain&ndash;computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},
 bibtype = {article},
 author = {Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},
 doi = {10.3390/s22052028},
 journal = {Sensors},
 number = {5}
}

@article{
 title = {Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks},
 type = {article},
 year = {2022},
 volume = {12},
 websites = {https://www.mdpi.com/2076-3417/12/12/6258},
 id = {99fceab7-8a81-396f-bf1b-b4c6f422df69},
 created = {2022-06-22T00:17:33.358Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-06-22T00:17:33.358Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {app12126258},
 source_type = {article},
 private_publication = {false},
 abstract = {Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging &ldquo;hands-on&rdquo; training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.},
 bibtype = {article},
 author = {Alpala, Luis Omar and Quiroga-Parra, Darío J and Torres, Juan Carlos and Peluffo-Ordóñez, Diego H},
 doi = {10.3390/app12126258},
 journal = {Applied Sciences},
 number = {12}
}

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Electroencephalogram (EEG),Epilepsy diagnosis,K-Nearest Neighbors (KNN),Linear Discriminant Analysis (LDA),Quadratic Discriminant Analysis (QDA),Support Vector Machine (SVM)},
 pages = {189-198},
 volume = {11976 LNAI},
 websites = {http://link.springer.com/10.1007/978-3-030-37078-7_19},
 id = {e6a1062d-c1eb-3698-b271-4811a1de0b2e},
 created = {2022-08-27T03:33:14.408Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-08-27T03:33:14.408Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.},
 bibtype = {inbook},
 author = {Vega-Gualán, Emil and Vargas, Andrés and Becerra, Miguel and Umaquinga, Ana and Riascos, Jaime A. and Peluffo, Diego},
 doi = {10.1007/978-3-030-37078-7_19},
 chapter = {Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@inproceedings{
 title = {Electrooculography Signals Classification for FPGA-based Human-Computer Interaction},
 type = {inproceedings},
 year = {2022},
 pages = {1-7},
 websites = {https://ieeexplore.ieee.org/document/9989664},
 id = {32da4701-f45e-3ed3-bd93-6a874cfca39f},
 created = {2022-12-28T23:00:00.348Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-12-28T23:00:00.348Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {9989664},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.},
 bibtype = {inproceedings},
 author = {Asanza, Víctor and Miranda, Jesús and Miranda, Jocelyn and Rivas, Leiber and Hernan Peluffo-Ordóñez, Diego and Pelaez, Enrique and Loayza, Francis and Alejandro, Otilia},
 doi = {10.1109/ANDESCON56260.2022.9989664},
 booktitle = {2022 IEEE ANDESCON}
}

@article{
 title = {IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia},
 type = {article},
 year = {2023},
 keywords = {Internet of Things,Scrum,agile methodologies,digital twins,virtual simulation},
 pages = {0-2},
 volume = {32},
 websites = {https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254},
 id = {3a7d4687-e775-3697-8faa-4c4e5841439b},
 created = {2023-02-23T09:38:19.689Z},
 file_attached = {true},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2023-02-23T09:38:37.406Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Digital Twins (DTs) have the potential to revolutionize the manufacturing, construction, maintenance, and control of industrial processes due to their ability to bridge the physical and digital realms. However, creating complex digital concepts requires carefully implementing appropriate methodologies and processes. This study presents a prototype digital twin of the art and technology laboratories at the Departmental Institute of Fine Arts in Cali, Colombia. We employ the Scrum agile development methodology to streamline the design and development process of the IoT-ATL prototype. A three-layer IoT architecture is established; it facilitates mapping the components of the digital twin and the physical elements to be simulated. The IoT-ATL prototype allows students and teachers to interact and learn about the physical state of the laboratory in a digital environment, thus increasing visibility of the availability and use of technological elements in the space. Keywords:},
 bibtype = {article},
 author = {Ordóñez-Bolaños, Oswaldo Andrés and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán},
 journal = {Revista de Facultad de Ingeniería UPTC},
 number = {March}
}

@article{
 title = {A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network},
 type = {article},
 year = {2023},
 volume = {8},
 websites = {https://www.mdpi.com/2313-7673/8/2/255},
 id = {3d3573b3-2a8f-3e53-aa22-47342ea079a5},
 created = {2023-06-17T22:55:15.947Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2023-06-17T22:55:15.947Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {biomimetics8020255},
 source_type = {article},
 private_publication = {false},
 abstract = {About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.},
 bibtype = {article},
 author = {Avilés-Mendoza, Karla and Gaibor-León, Neil George and Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},
 doi = {10.3390/biomimetics8020255},
 journal = {Biomimetics},
 number = {2}
}

@article{
 title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks},
 type = {article},
 year = {2023},
 keywords = {Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI},
 pages = {109540},
 volume = {50},
 websites = {https://www.sciencedirect.com/science/article/pii/S2352340923006406},
 id = {85847825-d177-389b-a562-2690e6cd35a1},
 created = {2023-09-22T18:07:54.476Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2023-09-22T18:07:54.476Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {ASANZA2023109540},
 source_type = {article},
 private_publication = {false},
 abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},
 bibtype = {article},
 author = {Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber},
 doi = {https://doi.org/10.1016/j.dib.2023.109540},
 journal = {Data in Brief}
}

@article{
 title = {Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals},
 type = {article},
 year = {2023},
 keywords = {artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion},
 pages = {641-649},
 volume = {56},
 websites = {https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413},
 id = {5d5be7d5-7828-38be-87a8-5f74e3afb5fd},
 created = {2023-10-02T16:29:47.648Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2023-10-02T16:29:47.648Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023},
 private_publication = {false},
 abstract = {Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.},
 bibtype = {article},
 author = {Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.},
 journal = {Journal Européen des Systèmes Automatisés},
 number = {4}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/55483c42-1c6e-3138-8b55-8d557318ba00?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/55483c42-1c6e-3138-8b55-8d557318ba00?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/55483c42-1c6e-3138-8b55-8d557318ba00?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/6cadbe30-0682-5d3b-7a2b-cb9d21fc88dc/2023_RevistaFI_UPTC_IoT_ATLPrototypeOfADigitalTwin.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023', 'https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/6cadbe30-0682-5d3b-7a2b-cb9d21fc88dc/2023_RevistaFI_UPTC_IoT_ATLPrototypeOfADigitalTwin.pdf.pdf', event);\">\n    IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ordóñez-Bolaños,  O., A.; Sierra-Martínez,  L., M.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Revista de Facultad de Ingeniería UPTC</i>, 32(March): 0-2.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/6cadbe30-0682-5d3b-7a2b-cb9d21fc88dc/2023_RevistaFI_UPTC_IoT_ATLPrototypeOfADigitalTwin.pdf.pdf\"\n     onclick=\"log_download('ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023', 'https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/6cadbe30-0682-5d3b-7a2b-cb9d21fc88dc/2023_RevistaFI_UPTC_IoT_ATLPrototypeOfADigitalTwin.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254\"\n     onclick=\"log_download('ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023', 'https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ordezbolaos-sierramartnez-peluffoordez-iotatlprototypeofadigitaltwintosimulateeducationalscenariosintheartandtechnologylaboratoriesatthedepartmentalinstituteoffineartsincalicolombia-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {IoT-ATL : Prototype of a Digital Twin to Simulate Educational Scenarios in the Art and Technology Laboratories at the Departmental Institute of Fine Arts in Cali , Colombia},\n type = {article},\n year = {2023},\n keywords = {Internet of Things,Scrum,agile methodologies,digital twins,virtual simulation},\n pages = {0-2},\n volume = {32},\n websites = {https://revistas.uptc.edu.co/index.php/ingenieria/article/view/15254},\n id = {3a7d4687-e775-3697-8faa-4c4e5841439b},\n created = {2023-02-23T09:38:19.689Z},\n file_attached = {true},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2023-02-23T09:38:37.406Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Digital Twins (DTs) have the potential to revolutionize the manufacturing, construction, maintenance, and control of industrial processes due to their ability to bridge the physical and digital realms. However, creating complex digital concepts requires carefully implementing appropriate methodologies and processes. This study presents a prototype digital twin of the art and technology laboratories at the Departmental Institute of Fine Arts in Cali, Colombia. We employ the Scrum agile development methodology to streamline the design and development process of the IoT-ATL prototype. A three-layer IoT architecture is established; it facilitates mapping the components of the digital twin and the physical elements to be simulated. The IoT-ATL prototype allows students and teachers to interact and learn about the physical state of the laboratory in a digital environment, thus increasing visibility of the availability and use of technological elements in the space. Keywords:},\n bibtype = {article},\n author = {Ordóñez-Bolaños, Oswaldo Andrés and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán},\n journal = {Revista de Facultad de Ingeniería UPTC},\n number = {March}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Digital Twins (DTs) have the potential to revolutionize the manufacturing, construction, maintenance, and control of industrial processes due to their ability to bridge the physical and digital realms. However, creating complex digital concepts requires carefully implementing appropriate methodologies and processes. This study presents a prototype digital twin of the art and technology laboratories at the Departmental Institute of Fine Arts in Cali, Colombia. We employ the Scrum agile development methodology to streamline the design and development process of the IoT-ATL prototype. A three-layer IoT architecture is established; it facilitates mapping the components of the digital twin and the physical elements to be simulated. The IoT-ATL prototype allows students and teachers to interact and learn about the physical state of the laboratory in a digital environment, thus increasing visibility of the availability and use of technological elements in the space. Keywords:\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/2313-7673/8/2/255\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023', 'https://www.mdpi.com/2313-7673/8/2/255', event);\">\n    A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Avilés-Mendoza,  K.; Gaibor-León,  N., G.; Asanza,  V.; Lorente-Leyva,  L., L.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Biomimetics</i>, 8(2).  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2313-7673/8/2/255\"\n     onclick=\"log_download('avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023', 'https://www.mdpi.com/2313-7673/8/2/255', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/biomimetics8020255\"\n     onclick=\"log_download('avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023', 'http://doi.org/10.3390/biomimetics8020255', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('avilsmendoza-gaiborlen-asanza-lorenteleyva-peluffoordez-a3dprintedbionichandpoweredbyemgsignalsandcontrolledbyanonlineneuralnetwork-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network},\n type = {article},\n year = {2023},\n volume = {8},\n websites = {https://www.mdpi.com/2313-7673/8/2/255},\n id = {3d3573b3-2a8f-3e53-aa22-47342ea079a5},\n created = {2023-06-17T22:55:15.947Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2023-06-17T22:55:15.947Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {biomimetics8020255},\n source_type = {article},\n private_publication = {false},\n abstract = {About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.},\n bibtype = {article},\n author = {Avilés-Mendoza, Karla and Gaibor-León, Neil George and Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/biomimetics8020255},\n journal = {Biomimetics},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2352340923006406\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023', 'https://www.sciencedirect.com/science/article/pii/S2352340923006406', event);\">\n    MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asanza,  V.; Lorente-Leyva,  L., L.; Peluffo-Ordóñez,  D., H.; Montoya,  D.;  and Gonzalez,  K.\n</span>\n\n  \n\n</span>\n\n  <i>Data in Brief</i>, 50: 109540.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2352340923006406\"\n     onclick=\"log_download('asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023', 'https://www.sciencedirect.com/science/article/pii/S2352340923006406', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1016/j.dib.2023.109540\"\n     onclick=\"log_download('asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023', 'http://doi.org/https://doi.org/10.1016/j.dib.2023.109540', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asanza-lorenteleyva-peluffoordez-montoya-gonzalez-milimbeegadatasetofeegsignalsrelatedtoupperandlowerlimbexecutionofmotorandmotorimagerytasks-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks},\n type = {article},\n year = {2023},\n keywords = {Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI},\n pages = {109540},\n volume = {50},\n websites = {https://www.sciencedirect.com/science/article/pii/S2352340923006406},\n id = {85847825-d177-389b-a562-2690e6cd35a1},\n created = {2023-09-22T18:07:54.476Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2023-09-22T18:07:54.476Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {ASANZA2023109540},\n source_type = {article},\n private_publication = {false},\n abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},\n bibtype = {article},\n author = {Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber},\n doi = {https://doi.org/10.1016/j.dib.2023.109540},\n journal = {Data in Brief}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023', 'https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413', event);\">\n    Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez,  D.\n</span>\n\n  \n\n</span>\n\n  <i>Journal Européen des Systèmes Automatisés</i>, 56(4): 641-649.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413\"\n     onclick=\"log_download('torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023', 'https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('torres-myoelectricprosthesisusingsensorfusionbetweenelectromyographyandpulseoximetrysignals-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals},\n type = {article},\n year = {2023},\n keywords = {artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion},\n pages = {641-649},\n volume = {56},\n websites = {https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413},\n id = {5d5be7d5-7828-38be-87a8-5f74e3afb5fd},\n created = {2023-10-02T16:29:47.648Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2023-10-02T16:29:47.648Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023},\n private_publication = {false},\n abstract = {Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there&#x27;s been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis&#x27;s creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis&#x27;s endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.},\n bibtype = {article},\n author = {Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.},\n journal = {Journal Européen des Systèmes Automatisés},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/1424-8220/22/5/2028\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022', 'https://www.mdpi.com/1424-8220/22/5/2028', event);\">\n    Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asanza,  V.; Peláez,  E.; Loayza,  F.; Lorente-Leyva,  L., L.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Sensors</i>, 22(5).  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/1424-8220/22/5/2028\"\n     onclick=\"log_download('asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022', 'https://www.mdpi.com/1424-8220/22/5/2028', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/s22052028\"\n     onclick=\"log_download('asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022', 'http://doi.org/10.3390/s22052028', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asanza-pelez-loayza-lorenteleyva-peluffoordez-identificationoflowerlimbmotortasksviabraincomputerinterfacesatopicaloverview-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview},\n type = {article},\n year = {2022},\n volume = {22},\n websites = {https://www.mdpi.com/1424-8220/22/5/2028},\n id = {d7d54a6d-055f-32d0-b9cb-8fd937f6d931},\n created = {2022-03-05T01:20:50.477Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-03-05T01:20:50.477Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {s22052028},\n source_type = {article},\n private_publication = {false},\n abstract = {Recent engineering and neuroscience applications have led to the development of brain&amp;ndash;computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},\n bibtype = {article},\n author = {Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/s22052028},\n journal = {Sensors},\n number = {5}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent engineering and neuroscience applications have led to the development of brain&ndash;computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/2076-3417/12/12/6258\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022', 'https://www.mdpi.com/2076-3417/12/12/6258', event);\">\n    Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Alpala,  L., O.; Quiroga-Parra,  D., J.; Torres,  J., C.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Sciences</i>, 12(12).  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2076-3417/12/12/6258\"\n     onclick=\"log_download('alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022', 'https://www.mdpi.com/2076-3417/12/12/6258', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/app12126258\"\n     onclick=\"log_download('alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022', 'http://doi.org/10.3390/app12126258', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('alpala-quirogaparra-torres-peluffoordez-smartfactoryusingvirtualrealityandonlinemultiusertowardsametaverseforexperimentalframeworks-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Smart Factory Using Virtual Reality and Online Multi-User: Towards a Metaverse for Experimental Frameworks},\n type = {article},\n year = {2022},\n volume = {12},\n websites = {https://www.mdpi.com/2076-3417/12/12/6258},\n id = {99fceab7-8a81-396f-bf1b-b4c6f422df69},\n created = {2022-06-22T00:17:33.358Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-06-22T00:17:33.358Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {app12126258},\n source_type = {article},\n private_publication = {false},\n abstract = {Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging &amp;ldquo;hands-on&amp;rdquo; training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.},\n bibtype = {article},\n author = {Alpala, Luis Omar and Quiroga-Parra, Darío J and Torres, Juan Carlos and Peluffo-Ordóñez, Diego H},\n doi = {10.3390/app12126258},\n journal = {Applied Sciences},\n number = {12}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Virtual reality (VR) has been brought closer to the general public over the past decade as it has become increasingly available for desktop and mobile platforms. As a result, consumer-grade VR may redefine how people learn by creating an engaging &ldquo;hands-on&rdquo; training experience. Today, VR applications leverage rich interactivity in a virtual environment without real-world consequences to optimize training programs in companies and educational institutions. Therefore, the main objective of this article was to improve the collaboration and communication practices in 3D virtual worlds with VR and metaverse focused on the educational and productive sector in smart factory. A key premise of our work is that the characteristics of the real environment can be replicated in a virtual world through digital twins, wherein new, configurable, innovative, and valuable ways of working and learning collaboratively can be created using avatar models. To do so, we present a proposal for the development of an experimental framework that constitutes a crucial first step in the process of formalizing collaboration in virtual environments through VR-powered metaverses. The VR system includes functional components, object-oriented configurations, advanced core, interfaces, and an online multi-user system. We present the study of the first application case of the framework with VR in a metaverse, focused on the smart factory, that shows the most relevant technologies of Industry 4.0. Functionality tests were carried out and evaluated with users through usability metrics that showed the satisfactory results of its potential educational and commercial use. Finally, the experimental results show that a commercial software framework for VR games can accelerate the development of experiments in the metaverse to connect users from different parts of the world in real time.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9989664\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022', 'https://ieeexplore.ieee.org/document/9989664', event);\">\n    Electrooculography Signals Classification for FPGA-based Human-Computer Interaction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asanza,  V.; Miranda,  J.; Miranda,  J.; Rivas,  L.; Hernan Peluffo-Ordóñez,  D.; Pelaez,  E.; Loayza,  F.;  and Alejandro,  O.\n</span>\n\n  \n\n</span>\n\n  In <i>2022 IEEE ANDESCON</i>, pages 1-7,  2022. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9989664\"\n     onclick=\"log_download('asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022', 'https://ieeexplore.ieee.org/document/9989664', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Electrooculography Signals Classification for FPGA-based Human-Computer Interaction [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ANDESCON56260.2022.9989664\"\n     onclick=\"log_download('asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022', 'http://doi.org/10.1109/ANDESCON56260.2022.9989664', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asanza-miranda-miranda-rivas-hernanpeluffoordez-pelaez-loayza-alejandro-electrooculographysignalsclassificationforfpgabasedhumancomputerinteraction-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Electrooculography Signals Classification for FPGA-based Human-Computer Interaction},\n type = {inproceedings},\n year = {2022},\n pages = {1-7},\n websites = {https://ieeexplore.ieee.org/document/9989664},\n id = {32da4701-f45e-3ed3-bd93-6a874cfca39f},\n created = {2022-12-28T23:00:00.348Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-12-28T23:00:00.348Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {9989664},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.},\n bibtype = {inproceedings},\n author = {Asanza, Víctor and Miranda, Jesús and Miranda, Jocelyn and Rivas, Leiber and Hernan Peluffo-Ordóñez, Diego and Pelaez, Enrique and Loayza, Francis and Alejandro, Otilia},\n doi = {10.1109/ANDESCON56260.2022.9989664},\n booktitle = {2022 IEEE ANDESCON}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021', 'https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5', event);\">\n    Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  López-Albán,  D.; López-Barrera,  A.; Mayorca-Torres,  D.;  and Peluffo-Ordóñez,  D.\n</span>\n\n  \n\n</span>\n\n  In Florez,  H.;  and Pollo-Cattaneo,  M., F., editor(s), <i>Applied Informatics</i>, pages 55-67,  2021. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5\"\n     onclick=\"log_download('lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021', 'https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},\n type = {inproceedings},\n year = {2021},\n pages = {55-67},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {e77821ff-b41a-304c-8ccb-cc74a3c124ad},\n created = {2022-02-02T07:25:42.987Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:42.987Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-030-89654-6_5},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\% rating yield using the proposed methodology with the Fast Tree algorithm.},\n bibtype = {inproceedings},\n author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},\n editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},\n booktitle = {Applied Informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\% rating yield using the proposed methodology with the Fast Tree algorithm.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2405896321016876\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021', 'https://www.sciencedirect.com/science/article/pii/S2405896321016876', event);\">\n    BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Constantine,  A.; Asanza,  V.; Loayza,  F., R.; Peláez,  E.;  and Peluffo-Ordóñez,  D.\n</span>\n\n  \n\n</span>\n\n  <i>IFAC-PapersOnLine</i>, 54(15): 364-369.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2405896321016876\"\n     onclick=\"log_download('constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021', 'https://www.sciencedirect.com/science/article/pii/S2405896321016876', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.283\"\n     onclick=\"log_download('constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021', 'http://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.283', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('constantine-asanza-loayza-pelez-peluffoordez-bcisystemusinganovelprocessingtechniquebasedonelectrodesselectionforhandprosthesiscontrol-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {BCI System using a Novel Processing Technique Based on Electrodes Selection for Hand Prosthesis Control},\n type = {article},\n year = {2021},\n keywords = {Bio-signals analysis,Brain Computer Interface,Embedded Systems,FPGA,Neural Networks},\n pages = {364-369},\n volume = {54},\n websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016876},\n id = {19c1b4bf-e5af-3508-bea8-6087ba4d3f97},\n created = {2022-02-02T07:25:43.282Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:43.282Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {CONSTANTINE2021364},\n source_type = {article},\n private_publication = {false},\n abstract = {This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.},\n bibtype = {article},\n author = {Constantine, Alisson and Asanza, Víctor and Loayza, Francis R and Peláez, Enrique and Peluffo-Ordóñez, Diego},\n doi = {https://doi.org/10.1016/j.ifacol.2021.10.283},\n journal = {IFAC-PapersOnLine},\n number = {15}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2405896321016906\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021', 'https://www.sciencedirect.com/science/article/pii/S2405896321016906', event);\">\n    Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Asanza,  V.; Sánchez-Pozo,  N., N.; Lorente-Leyva,  L., L.; Peluffo-Ordóñez,  D., H.; Loayza,  F., R.;  and Peláez,  E.\n</span>\n\n  \n\n</span>\n\n  <i>IFAC-PapersOnLine</i>, 54(15): 376-381.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S2405896321016906\"\n     onclick=\"log_download('asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021', 'https://www.sciencedirect.com/science/article/pii/S2405896321016906', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.285\"\n     onclick=\"log_download('asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021', 'http://doi.org/https://doi.org/10.1016/j.ifacol.2021.10.285', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('asanza-snchezpozo-lorenteleyva-peluffoordez-loayza-pelez-classificationofsubjectswithparkinsonsdiseaseusingfingertappingdataset-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset},\n type = {article},\n year = {2021},\n keywords = {Classification,Finger Tapping,Machine Learning,Parkinson’s disease},\n pages = {376-381},\n volume = {54},\n websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016906},\n id = {a0809ce4-85c2-3631-b319-abf677f9d692},\n created = {2022-02-02T07:25:43.665Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:43.665Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {ASANZA2021376},\n source_type = {article},\n private_publication = {false},\n abstract = {Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.},\n bibtype = {article},\n author = {Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique},\n doi = {https://doi.org/10.1016/j.ifacol.2021.10.285},\n journal = {IFAC-PapersOnLine},\n number = {15}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ijaseit.insightsociety.org/index.php?option=com_content&amp;view=article&amp;id=9&amp;Itemid=1&amp;article_id=10814\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020', 'http://ijaseit.insightsociety.org/index.php?option=com_content&amp;view=article&amp;id=9&amp;Itemid=1&amp;article_id=10814', event);\">\n    Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mayorca-Torres,  D.; Caicedo-Eraso,  J., C.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal on Advanced Science, Engineering and Information Technology</i>.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ijaseit.insightsociety.org/index.php?option=com_content&amp;view=article&amp;id=9&amp;Itemid=1&amp;article_id=10814\"\n     onclick=\"log_download('mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020', 'http://ijaseit.insightsociety.org/index.php?option=com_content&amp;view=article&amp;id=9&amp;Itemid=1&amp;article_id=10814', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.18517/ijaseit.10.2.10814\"\n     onclick=\"log_download('mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020', 'http://doi.org/10.18517/ijaseit.10.2.10814', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mayorcatorres-caicedoeraso-peluffoordez-kneejointanglemeasuringportableembeddedsystembasedoninertialmeasurementunitsforgaitanalysis-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Knee joint angle measuring portable embedded system based on inertial measurement units for gait analysis},\n type = {article},\n year = {2020},\n keywords = {Gait analysis,IMU,Kalman filter,Knee-joint angle,Motion analysis},\n websites = {http://ijaseit.insightsociety.org/index.php?option=com_content&amp;view=article&amp;id=9&amp;Itemid=1&amp;article_id=10814},\n id = {f9bdd20a-54e8-348c-a457-82751a93f902},\n created = {2022-02-02T07:25:43.940Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:43.940Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Mayorca-Torres2020},\n private_publication = {false},\n abstract = {Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body&#x27;s movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient&#x27;s knee-joint kinematics during a human gait.},\n bibtype = {article},\n author = {Mayorca-Torres, Dagoberto and Caicedo-Eraso, Julio C. and Peluffo-Ordóñez, Diego H.},\n doi = {10.18517/ijaseit.10.2.10814},\n journal = {International Journal on Advanced Science, Engineering and Information Technology}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee. This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the kneejoint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient's knee-joint kinematics during a human gait.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&amp;fromopenview=true\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020', 'https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&amp;fromopenview=true', event);\">\n    Data fusion and information quality for biometric identification from multimodal signals.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Becerra,  M., A.; Lasso-Arciniegas,  L.; Viveros,  A.; Serna-Guarín,  L.; Peluffo-Ordóñez,  D.;  and Tobón,  C.\n</span>\n\n  \n\n</span>\n\n  <i>RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao</i>.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&amp;fromopenview=true\"\n     onclick=\"log_download('becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020', 'https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&amp;fromopenview=true', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Data fusion and information quality for biometric identification from multimodal signals [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('becerra-lassoarciniegas-viveros-sernaguarn-peluffoordez-tobn-datafusionandinformationqualityforbiometricidentificationfrommultimodalsignals-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Data fusion and information quality for biometric identification from multimodal signals},\n type = {article},\n year = {2020},\n keywords = {Biometry,Data fusion,Information quality,Signal processing},\n websites = {https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&amp;fromopenview=true},\n id = {dc99d2ec-10d5-34ea-b53a-882ff1aae863},\n created = {2022-02-02T07:25:44.209Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:44.209Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2020a},\n private_publication = {false},\n abstract = {Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.},\n bibtype = {article},\n author = {Becerra, Miguel A. and Lasso-Arciniegas, Laura and Viveros, Andrés and Serna-Guarín, Leonardo and Peluffo-Ordóñez, Diego and Tobón, Catalina},\n journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020', 'https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7', event);\">\n    Clustering of Reading Ability Performance Variables in the English Language Based on TBL Methodology and Behavior in the Left Hemisphere of the Brain.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Patiño-Alarcón,  D., R.; Patiño-Alarcón,  F., A.; Lorente-Leyva,  L., L.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Communications in Computer and Information Science.  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7\"\n     onclick=\"log_download('patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020', 'https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Communications in Computer and Information Science [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-62833-8_7\"\n     onclick=\"log_download('patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020', 'http://doi.org/10.1007/978-3-030-62833-8_7', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('patioalarcn-patioalarcn-lorenteleyva-peluffoordez-communicationsincomputerandinformationscience-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2020},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-62833-8_7},\n id = {b6b9da88-86d3-3cd4-86ae-b66d5e3ba59e},\n created = {2022-02-02T07:25:44.514Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:44.514Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Patino-Alarcon2020},\n private_publication = {false},\n abstract = {This research presents an application of the clustering based on Thinking Based - Learning methodology (TBL), which offers guidelines to promote students’ reflective thinking. Within this methodology, the Intelligence Execution Theory (IET) tool will be used to encourage this kind of thinking in the classroom. Having in mind that, in any educational process, methodologies and pedagogical tools have a pivotal role as they are one of the bases for optimizing cognitive intelligence. In this case, it was given a priority to the potential development of a specific linguistic skill. This study presented a mixed methodology with an exploratory and descriptive scope. The main objective of this research was the clustering of the variables of functioning of the reading ability in the English language based on the TBL methodology and its behavior in the left hemisphere of the brain, specifically to analyze the improvement of the reading ability in the English language of the participants of this case study. With the expectation of generating sustainability of adequate levels of performance, instruction and learning of the English language of students at all levels.},\n bibtype = {inbook},\n author = {Patiño-Alarcón, Delio R. and Patiño-Alarcón, Fernando A. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-62833-8_7},\n chapter = {Clustering of Reading Ability Performance Variables in the English Language Based on TBL Methodology and Behavior in the Left Hemisphere of the Brain},\n title = {Communications in Computer and Information Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This research presents an application of the clustering based on Thinking Based - Learning methodology (TBL), which offers guidelines to promote students’ reflective thinking. Within this methodology, the Intelligence Execution Theory (IET) tool will be used to encourage this kind of thinking in the classroom. Having in mind that, in any educational process, methodologies and pedagogical tools have a pivotal role as they are one of the bases for optimizing cognitive intelligence. In this case, it was given a priority to the potential development of a specific linguistic skill. This study presented a mixed methodology with an exploratory and descriptive scope. The main objective of this research was the clustering of the variables of functioning of the reading ability in the English language based on the TBL methodology and its behavior in the left hemisphere of the brain, specifically to analyze the improvement of the reading ability in the English language of the participants of this case study. With the expectation of generating sustainability of adequate levels of performance, instruction and learning of the English language of students at all levels.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (6)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019', 'https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54', event);\">\n    Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Herrera-Granda,  E., P.; Caraguay-Procel,  J., A.; Granda-Gudiño,  P., D.; Herrera-Granda,  I., D.; Lorente-Leyva,  L., L.; Peluffo-Ordóñez,  D., H.;  and Revelo-Fuelagán,  J.\n</span>\n\n  \n\n</span>\n\n  In <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>,  2019. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54\"\n     onclick=\"log_download('herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019', 'https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-14799-0_54\"\n     onclick=\"log_download('herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019', 'http://doi.org/10.1007/978-3-030-14799-0_54', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('herreragranda-caraguayprocel-grandagudio-herreragranda-lorenteleyva-peluffoordez-revelofuelagn-drowsinessdetectionindriversthroughrealtimeimageprocessingofthehumaneye-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Drowsiness Detection in Drivers Through Real-Time Image Processing of the Human Eye},\n type = {inproceedings},\n year = {2019},\n keywords = {Alarm,Artificial intelligence,Drowsiness detection,Human eye,Image processing},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-14799-0_54},\n id = {8e496276-78ed-3f37-bf6f-bbc726f99b3e},\n created = {2022-02-02T07:25:44.943Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:44.943Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Herrera-Granda2019},\n private_publication = {false},\n abstract = {At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.},\n bibtype = {inproceedings},\n author = {Herrera-Granda, Erick P. and Caraguay-Procel, Jorge A. and Granda-Gudiño, Pedro D. and Herrera-Granda, Israel D. and Lorente-Leyva, Leandro L. and Peluffo-Ordóñez, Diego H. and Revelo-Fuelagán, Javier},\n doi = {10.1007/978-3-030-14799-0_54},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  At a global level, drowsiness is one of the main causes of road accidents causing frequent deaths and economic losses. To solve this problem an application developed in Matlab environment was made, which processes real time acquired images in order to determine if the driver is awake or drowsy. Using AdaBoost training Algorithm for Viola-Jones eyes detection, a cascade classifier finds the location and the area of the driver eyes in each frame of the video. Once the driver eyes are detected, they are analyzed whether are open or closed by color segmentation and thresholding based on the sclera binarized area. Finally, it was implemented as a drowsiness detection system which aims to prevent driver fall asleep while driving a vehicle by activating an audible alert, reaching speeds up to 14.5 fps.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8667585/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019', 'https://ieeexplore.ieee.org/document/8667585/', event);\">\n    Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ordonez-Bolanos,  O., A.; Gomez-Lara,  J., F.; Becerra,  M., A.; Peluffo-Ordonez,  D., H.; Duque-Mejia,  C., M.; Medrano-David,  D.;  and Mejia-Arboleda,  C.\n</span>\n\n  \n\n</span>\n\n  In <i>2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)</i>, pages 113-116, 2 2019. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8667585/\"\n     onclick=\"log_download('ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019', 'https://ieeexplore.ieee.org/document/8667585/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/LASCAS.2019.8667585\"\n     onclick=\"log_download('ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019', 'http://doi.org/10.1109/LASCAS.2019.8667585', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ordonezbolanos-gomezlara-becerra-peluffoordonez-duquemejia-medranodavid-mejiaarboleda-recognitionofemotionsusingiceemdbasedcharacterizationofmultimodalphysiologicalsignals-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals},\n type = {inproceedings},\n year = {2019},\n pages = {113-116},\n websites = {https://ieeexplore.ieee.org/document/8667585/},\n month = {2},\n publisher = {IEEE},\n id = {4400fe53-d983-3bea-a5f0-8d4c35f5eea9},\n created = {2022-02-02T07:25:45.442Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:45.442Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Ordonez-Bolanos2019},\n private_publication = {false},\n abstract = {Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients&#x27; metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.},\n bibtype = {inproceedings},\n author = {Ordonez-Bolanos, O. A. and Gomez-Lara, J. F. and Becerra, M. A. and Peluffo-Ordonez, D. H. and Duque-Mejia, C. M. and Medrano-David, D. and Mejia-Arboleda, C.},\n doi = {10.1109/LASCAS.2019.8667585},\n booktitle = {2019 IEEE 10th Latin American Symposium on Circuits &amp; Systems (LASCAS)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-14799-0_30\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-14799-0_30', event);\">\n    Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Gómez-Lara,  J., F.; Ordóñez-Bolaños,  O., A.; Becerra,  M., A.; Castro-Ospina,  A., E.; Mejía-Arboleda,  C.; Duque-Mejía,  C.; Rodriguez,  J.; Revelo-Fuelagán,  J.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 351-362.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-14799-0_30\"\n     onclick=\"log_download('gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-14799-0_30', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-14799-0_30\"\n     onclick=\"log_download('gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://doi.org/10.1007/978-3-030-14799-0_30', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('gmezlara-ordezbolaos-becerra-castroospina-mejaarboleda-duquemeja-rodriguez-revelofuelagn-etal-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n keywords = {Emotion recognition,Improved complementary ensemble empirical mode dec,Multimodal,Physiological signals,Signal processing},\n pages = {351-362},\n websites = {http://link.springer.com/10.1007/978-3-030-14799-0_30},\n id = {6cf5ffee-cfb5-370e-81d8-26953a492962},\n created = {2022-02-02T07:25:45.719Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:45.719Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Gomez-Lara2019},\n private_publication = {false},\n abstract = {The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.},\n bibtype = {inbook},\n author = {Gómez-Lara, J. F. and Ordóñez-Bolaños, O. A. and Becerra, M. A. and Castro-Ospina, A. E. and Mejía-Arboleda, C. and Duque-Mejía, C. and Rodriguez, J. and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-14799-0_30},\n chapter = {Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-17935-9_30\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-17935-9_30', event);\">\n    Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Becerra,  M., A.; Londoño-Delgado,  E.; Botero-Henao,  O., I.; Marín-Castrillón,  D.; Mejia-Arboleda,  C.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Volume 11466 LNBI . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 333-342. Springer Verlag,  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-17935-9_30\"\n     onclick=\"log_download('becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-17935-9_30', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-17935-9_30\"\n     onclick=\"log_download('becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://doi.org/10.1007/978-3-030-17935-9_30', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('becerra-londoodelgado-boterohenao-marncastrilln-mejiaarboleda-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n keywords = {Electroencephalographic signal,Machine learning,Semantic category,Semantic retrieval,Signal processing},\n pages = {333-342},\n volume = {11466 LNBI},\n websites = {http://link.springer.com/10.1007/978-3-030-17935-9_30},\n publisher = {Springer Verlag},\n id = {1041e065-3f93-3648-93a1-ece26d777002},\n created = {2022-02-02T07:25:46.027Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:46.027Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2019},\n private_publication = {false},\n abstract = {Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.},\n bibtype = {inbook},\n author = {Becerra, Miguel Alberto and Londoño-Delgado, Edwin and Botero-Henao, Oscar I. and Marín-Castrillón, Diana and Mejia-Arboleda, Cristian and Peluffo-Ordóñez, Diego Hernán},\n doi = {10.1007/978-3-030-17935-9_30},\n chapter = {Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019', 'https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33', event);\">\n    Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Londoño-Delgado,  E.; Becerra,  M., A.; Duque-Mejía,  C., M.; Zapata,  J., C.; Mejía-Arboleda,  C.; Castro-Ospina,  A., E.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  In <i>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</i>, volume 11466 LNBI, pages 363-373,  2019. Springer Verlag\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33\"\n     onclick=\"log_download('londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019', 'https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-17935-9_33\"\n     onclick=\"log_download('londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019', 'http://doi.org/10.1007/978-3-030-17935-9_33', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('londoodelgado-becerra-duquemeja-zapata-mejaarboleda-castroospina-peluffoordez-nongeneralizedanalysisofthemultimodalsignalsforemotionrecognitionpreliminaryresults-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results},\n type = {inproceedings},\n year = {2019},\n keywords = {Emotion recognition,Physiological signals,Signal processing},\n pages = {363-373},\n volume = {11466 LNBI},\n websites = {https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33},\n publisher = {Springer Verlag},\n id = {2fd81d28-93a5-3eed-a1ab-49d718fe8de7},\n created = {2022-02-02T07:25:46.321Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:46.321Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.},\n bibtype = {inproceedings},\n author = {Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},\n doi = {10.1007/978-3-030-17935-9_33},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-37078-7_19\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-37078-7_19', event);\">\n    Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vega-Gualán,  E.; Vargas,  A.; Becerra,  M.; Umaquinga,  A.; Riascos,  J., A.;  and Peluffo,  D.\n</span>\n\n  \n\n</span>\n\n  Volume 11976 LNAI . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 189-198.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-37078-7_19\"\n     onclick=\"log_download('vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-37078-7_19', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-37078-7_19\"\n     onclick=\"log_download('vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://doi.org/10.1007/978-3-030-37078-7_19', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vegagualn-vargas-becerra-umaquinga-riascos-peluffo-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n keywords = {Electroencephalogram (EEG),Epilepsy diagnosis,K-Nearest Neighbors (KNN),Linear Discriminant Analysis (LDA),Quadratic Discriminant Analysis (QDA),Support Vector Machine (SVM)},\n pages = {189-198},\n volume = {11976 LNAI},\n websites = {http://link.springer.com/10.1007/978-3-030-37078-7_19},\n id = {e6a1062d-c1eb-3698-b271-4811a1de0b2e},\n created = {2022-08-27T03:33:14.408Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-08-27T03:33:14.408Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.},\n bibtype = {inbook},\n author = {Vega-Gualán, Emil and Vargas, Andrés and Becerra, Miguel and Umaquinga, Ana and Riascos, Jaime A. and Peluffo, Diego},\n doi = {10.1007/978-3-030-37078-7_19},\n chapter = {Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  © 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2018\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2018')\"\n      onkeypress=\"toggleGroup('group_2018')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2018</span>\n      <span class=\"bibbase_group_count\">\n        \n        (6)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/8580268/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018', 'https://ieeexplore.ieee.org/document/8580268/', event);\">\n    Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Rosero-Montalvo,  P., D.; Godoy-Trujillo,  P.; Flores-Bosmediano,  E.; Carrascal-Garcia,  J.; Otero-Potosi,  S.; Benitez-Pereira,  H.;  and Peluffo-Ordonez,  D., H.\n</span>\n\n  \n\n</span>\n\n  In <i>2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)</i>, pages 1-5, 10 2018. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/8580268/\"\n     onclick=\"log_download('roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018', 'https://ieeexplore.ieee.org/document/8580268/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ETCM.2018.8580268\"\n     onclick=\"log_download('roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018', 'http://doi.org/10.1109/ETCM.2018.8580268', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques},\n type = {inproceedings},\n year = {2018},\n keywords = {intelligent glove,knn,prototype selection,sign language},\n pages = {1-5},\n websites = {https://ieeexplore.ieee.org/document/8580268/},\n month = {10},\n publisher = {IEEE},\n id = {88cf6012-fe39-33e4-a203-667c31dad596},\n created = {2022-02-02T07:25:46.595Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:46.595Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Rosero-Montalvo2018a},\n private_publication = {false},\n abstract = {We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.},\n bibtype = {inproceedings},\n author = {Rosero-Montalvo, Paul D. and Godoy-Trujillo, Pamela and Flores-Bosmediano, Edison and Carrascal-Garcia, Jorge and Otero-Potosi, Santiago and Benitez-Pereira, Henry and Peluffo-Ordonez, Diego H.},\n doi = {10.1109/ETCM.2018.8580268},\n booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-92639-1_38\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-319-92639-1_38', event);\">\n    Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Castro-Ospina,  A., E.; Correa-Mira,  A., M.; Herrera-Granda,  I., D.; Peluffo-Ordóñez,  D., H.;  and Fandiño-Toro,  H., A.\n</span>\n\n  \n\n</span>\n\n  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 455-463.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-92639-1_38\"\n     onclick=\"log_download('castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-319-92639-1_38', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-319-92639-1_38\"\n     onclick=\"log_download('castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://doi.org/10.1007/978-3-319-92639-1_38', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('castroospina-correamira-herreragranda-peluffoordez-fandiotoro-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Fingertip segmentation,NPR measurement,Thermal hand images,Thermorregulation},\n pages = {455-463},\n websites = {http://link.springer.com/10.1007/978-3-319-92639-1_38},\n id = {b1c9a55d-a816-3a99-84f3-5482a3049548},\n created = {2022-02-02T07:25:46.867Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:46.867Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Castro-Ospina2018},\n private_publication = {false},\n abstract = {Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.},\n bibtype = {inbook},\n author = {Castro-Ospina, A. E. and Correa-Mira, A. M. and Herrera-Granda, I. D. and Peluffo-Ordóñez, D. H. and Fandiño-Toro, H. A.},\n doi = {10.1007/978-3-319-92639-1_38},\n chapter = {Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Thermoregulation refers to the physiological processes that maintain stable the body temperatures. Infrared thermography is a non-invasive technique useful for visualizing these temperatures. Previous works suggest it is important to analyze thermoregulation in peripheral regions, such as the fingertips, because some disabling pathologies affect particularly the thermoregulation of these regions. This work proposes an algorithm for fingertip segmentation in thermal images of the hand. By using a supervised index, the results are compared against segmentations provided by humans. The results are outstanding even when the analyzed images are highly resized.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01132-1_42\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-030-01132-1_42', event);\">\n    Movement Identification in EMG Signals Using Machine Learning: A Comparative Study.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lasso-Arciniegas,  L.; Viveros-Melo,  A.; Salazar-Castro,  J., A.; Becerra,  M., A.; Castro-Ospina,  A., E.; Revelo-Fuelagán,  E., J.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 368-375.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01132-1_42\"\n     onclick=\"log_download('lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-030-01132-1_42', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-01132-1_42\"\n     onclick=\"log_download('lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://doi.org/10.1007/978-3-030-01132-1_42', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lassoarciniegas-viverosmelo-salazarcastro-becerra-castroospina-revelofuelagn-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2018},\n keywords = {ANN,EMG signals,Feature extraction,KNN,Parzen},\n pages = {368-375},\n websites = {http://link.springer.com/10.1007/978-3-030-01132-1_42},\n id = {c11c79f3-1069-34d7-a120-f55b8bd1e76e},\n created = {2022-02-02T07:25:47.120Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:47.120Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lasso-Arciniegas2018},\n private_publication = {false},\n abstract = {The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.},\n bibtype = {inbook},\n author = {Lasso-Arciniegas, Laura and Viveros-Melo, Andres and Salazar-Castro, José A. and Becerra, Miguel A. and Castro-Ospina, Andrés Eduardo and Revelo-Fuelagán, E. Javier and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-01132-1_42},\n chapter = {Movement Identification in EMG Signals Using Machine Learning: A Comparative Study},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-98998-3_11\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018', 'http://link.springer.com/10.1007/978-3-319-98998-3_11', event);\">\n    Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Viveros-Melo,  A.; Lasso-Arciniegas,  L.; Salazar-Castro,  J., A.; Peluffo-Ordóñez,  D., H.; Becerra,  M., A.; Castro-Ospina,  A., E.;  and Revelo-Fuelagán,  E., J.\n</span>\n\n  \n\n</span>\n\n  Communications in Computer and Information Science, pages 139-149.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-98998-3_11\"\n     onclick=\"log_download('viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018', 'http://link.springer.com/10.1007/978-3-319-98998-3_11', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Communications in Computer and Information Science [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-319-98998-3_11\"\n     onclick=\"log_download('viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018', 'http://doi.org/10.1007/978-3-319-98998-3_11', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('viverosmelo-lassoarciniegas-salazarcastro-peluffoordez-becerra-castroospina-revelofuelagn-communicationsincomputerandinformationscience-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Classification,EMG signals,Movements selection,Wavelet},\n pages = {139-149},\n websites = {http://link.springer.com/10.1007/978-3-319-98998-3_11},\n id = {18798fe2-4e14-3b67-9d3a-4302a79a7d02},\n created = {2022-02-02T07:25:47.482Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:47.482Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Viveros-Melo2018},\n private_publication = {false},\n abstract = {Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).},\n bibtype = {inbook},\n author = {Viveros-Melo, A. and Lasso-Arciniegas, L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E. and Revelo-Fuelagán, E. J.},\n doi = {10.1007/978-3-319-98998-3_11},\n chapter = {Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results},\n title = {Communications in Computer and Information Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01132-1_35\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-030-01132-1_35', event);\">\n    Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Becerra,  M., A.; Londoño-Delgado,  E.; Pelaez-Becerra,  S., M.; Castro-Ospina,  A., E.; Mejia-Arboleda,  C.; Durango,  J.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 309-316.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-01132-1_35\"\n     onclick=\"log_download('becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://link.springer.com/10.1007/978-3-030-01132-1_35', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-01132-1_35\"\n     onclick=\"log_download('becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018', 'http://doi.org/10.1007/978-3-030-01132-1_35', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('becerra-londoodelgado-pelaezbecerra-castroospina-mejiaarboleda-durango-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Electroencephalographic signal,Sensorial stimulus,Signal processing,Tactile pleasantness},\n pages = {309-316},\n websites = {http://link.springer.com/10.1007/978-3-030-01132-1_35},\n id = {742d67ee-3e64-37d1-b66e-8c3feb2d7b40},\n created = {2022-02-02T07:25:47.802Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:47.802Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2018b},\n private_publication = {false},\n abstract = {Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.},\n bibtype = {inbook},\n author = {Becerra, Miguel A. and Londoño-Delgado, Edwin and Pelaez-Becerra, Sonia M. and Castro-Ospina, Andrés Eduardo and Mejia-Arboleda, Cristian and Durango, Julián and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-01132-1_35},\n chapter = {Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-98998-3_10\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018', 'http://link.springer.com/10.1007/978-3-319-98998-3_10', event);\">\n    Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Becerra,  M., A.; Londoño-Delgado,  E.; Pelaez-Becerra,  S., M.; Serna-Guarín,  L.; Castro-Ospina,  A., E.; Marin-Castrillón,  D.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Communications in Computer and Information Science, pages 128-138.  2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-319-98998-3_10\"\n     onclick=\"log_download('becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018', 'http://link.springer.com/10.1007/978-3-319-98998-3_10', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Communications in Computer and Information Science [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-319-98998-3_10\"\n     onclick=\"log_download('becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018', 'http://doi.org/10.1007/978-3-319-98998-3_10', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('becerra-londoodelgado-pelaezbecerra-sernaguarn-castroospina-marincastrilln-peluffoordez-communicationsincomputerandinformationscience-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2018},\n keywords = {Electroencephalographic signal,Emotion,Odor pleasantness,Sensorial stimuli,Signal processing},\n pages = {128-138},\n websites = {http://link.springer.com/10.1007/978-3-319-98998-3_10},\n id = {c4f9d909-7b5f-338d-af73-b2609bee48ce},\n created = {2022-02-02T07:25:48.229Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},\n last_modified = {2022-02-02T07:25:48.229Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Becerra2018a},\n private_publication = {false},\n abstract = {Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.},\n bibtype = {inbook},\n author = {Becerra, M. A. and Londoño-Delgado, E. and Pelaez-Becerra, S. M. and Serna-Guarín, L. and Castro-Ospina, A. E. and Marin-Castrillón, D. and Peluffo-Ordóñez, D. H.},\n doi = {10.1007/978-3-319-98998-3_10},\n chapter = {Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States},\n title = {Communications in Computer and Information Science}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);