Intelligent Sitting Posture Classifier for Wheelchair Users
In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quanti...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10016689/ |
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| author | Patrick Vermander Aitziber Mancisidor Itziar Cabanes Nerea Perez Jon Torres-Unda |
| author_facet | Patrick Vermander Aitziber Mancisidor Itziar Cabanes Nerea Perez Jon Torres-Unda |
| author_sort | Patrick Vermander |
| collection | DOAJ |
| description | In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions. |
| format | Article |
| id | doaj-art-2eb6898743484ec48f3bd70e976b7da7 |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-2eb6898743484ec48f3bd70e976b7da72025-08-20T01:52:03ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-013194495310.1109/TNSRE.2023.323669210016689Intelligent Sitting Posture Classifier for Wheelchair UsersPatrick Vermander0https://orcid.org/0000-0003-3842-2957Aitziber Mancisidor1https://orcid.org/0000-0002-2178-345XItziar Cabanes2https://orcid.org/0000-0002-1949-953XNerea Perez3https://orcid.org/0000-0001-9515-3242Jon Torres-Unda4https://orcid.org/0000-0003-2379-0546Department of Automatic Control and System Engineering, Faculty of Engineering of Bilbao, University of the Basque Country UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Faculty of Engineering of Bilbao, University of the Basque Country UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Faculty of Engineering of Bilbao, University of the Basque Country UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Faculty of Engineering of Bilbao, University of the Basque Country UPV/EHU, Bilbao, SpainDepartment of Physiology, Faculty of Medicine and Nursing, University of Basque Country UPV/EHU, Bilbao, SpainIn recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions.https://ieeexplore.ieee.org/document/10016689/Artificial neural networksitting posture classificationwheelchairforce sensors |
| spellingShingle | Patrick Vermander Aitziber Mancisidor Itziar Cabanes Nerea Perez Jon Torres-Unda Intelligent Sitting Posture Classifier for Wheelchair Users IEEE Transactions on Neural Systems and Rehabilitation Engineering Artificial neural network sitting posture classification wheelchair force sensors |
| title | Intelligent Sitting Posture Classifier for Wheelchair Users |
| title_full | Intelligent Sitting Posture Classifier for Wheelchair Users |
| title_fullStr | Intelligent Sitting Posture Classifier for Wheelchair Users |
| title_full_unstemmed | Intelligent Sitting Posture Classifier for Wheelchair Users |
| title_short | Intelligent Sitting Posture Classifier for Wheelchair Users |
| title_sort | intelligent sitting posture classifier for wheelchair users |
| topic | Artificial neural network sitting posture classification wheelchair force sensors |
| url | https://ieeexplore.ieee.org/document/10016689/ |
| work_keys_str_mv | AT patrickvermander intelligentsittingpostureclassifierforwheelchairusers AT aitzibermancisidor intelligentsittingpostureclassifierforwheelchairusers AT itziarcabanes intelligentsittingpostureclassifierforwheelchairusers AT nereaperez intelligentsittingpostureclassifierforwheelchairusers AT jontorresunda intelligentsittingpostureclassifierforwheelchairusers |