Enhancing Postural Monitoring in Wheelchair Users Through Context Classification
Globally, the number of wheelchair users is steadily increasing. These people often adopt sitting patterns that reflect their functional status. Monitoring the user’s postural status can help users and healthcare professionals to treat them. However, this posture is sometimes influenced b...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11115105/ |
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| author | Nerea Perez Aitziber Mancisidor Itziar Cabanes Patrick Vermander |
| author_facet | Nerea Perez Aitziber Mancisidor Itziar Cabanes Patrick Vermander |
| author_sort | Nerea Perez |
| collection | DOAJ |
| description | Globally, the number of wheelchair users is steadily increasing. These people often adopt sitting patterns that reflect their functional status. Monitoring the user’s postural status can help users and healthcare professionals to treat them. However, this posture is sometimes influenced by the environment in which the chairs move, and not necessarily by changes in their functional status. To address this problem, this study presents a model designed to classify wheelchair movement contexts, enabling the identification of what is happening in the user’s environment. To do this, data has been collected using a robust and non-intrusive combined monitoring system, which records both the wheelchair’s movement and the user’s posture. These data have been used to train classifier models capable of distinguishing between seven categories of environments that are common in the daily lives of wheelchair users: flat surface, ramp up, ramp down, right turn, left turn, obstacles, and abrupt braking. These models have been developed using machine learning techniques, such as K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results show an accuracy of 90% in free-running tests and more than 99% in controlled runs. These results remained consistent despite variations in training subjects, validated by leave 2 out cross-validation. This innovative approach has the potential to improve the quality of life of wheelchair users by providing an accurate and effective tool to understand and address complex interactions between the environment and the users’ posture. |
| format | Article |
| id | doaj-art-8754d898d4cd4c3bada0d1812dd5065a |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-8754d898d4cd4c3bada0d1812dd5065a2025-08-20T03:43:55ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01333129313610.1109/TNSRE.2025.359647211115105Enhancing Postural Monitoring in Wheelchair Users Through Context ClassificationNerea Perez0https://orcid.org/0000-0001-9515-3242Aitziber Mancisidor1https://orcid.org/0000-0002-2178-345XItziar Cabanes2https://orcid.org/0000-0002-1949-953XPatrick Vermander3https://orcid.org/0000-0003-3842-2957Department of Automatic Control and System Engineering, Bilbao School of Engineering, University of the Basque Country-UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Bilbao School of Engineering, University of the Basque Country-UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Bilbao School of Engineering, University of the Basque Country-UPV/EHU, Bilbao, SpainDepartment of Automatic Control and System Engineering, Bilbao School of Engineering, University of the Basque Country-UPV/EHU, Bilbao, SpainGlobally, the number of wheelchair users is steadily increasing. These people often adopt sitting patterns that reflect their functional status. Monitoring the user’s postural status can help users and healthcare professionals to treat them. However, this posture is sometimes influenced by the environment in which the chairs move, and not necessarily by changes in their functional status. To address this problem, this study presents a model designed to classify wheelchair movement contexts, enabling the identification of what is happening in the user’s environment. To do this, data has been collected using a robust and non-intrusive combined monitoring system, which records both the wheelchair’s movement and the user’s posture. These data have been used to train classifier models capable of distinguishing between seven categories of environments that are common in the daily lives of wheelchair users: flat surface, ramp up, ramp down, right turn, left turn, obstacles, and abrupt braking. These models have been developed using machine learning techniques, such as K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results show an accuracy of 90% in free-running tests and more than 99% in controlled runs. These results remained consistent despite variations in training subjects, validated by leave 2 out cross-validation. This innovative approach has the potential to improve the quality of life of wheelchair users by providing an accurate and effective tool to understand and address complex interactions between the environment and the users’ posture.https://ieeexplore.ieee.org/document/11115105/Biomedical monitoringwheelchairssmart healthcarecontext modelingintelligent sensors |
| spellingShingle | Nerea Perez Aitziber Mancisidor Itziar Cabanes Patrick Vermander Enhancing Postural Monitoring in Wheelchair Users Through Context Classification IEEE Transactions on Neural Systems and Rehabilitation Engineering Biomedical monitoring wheelchairs smart healthcare context modeling intelligent sensors |
| title | Enhancing Postural Monitoring in Wheelchair Users Through Context Classification |
| title_full | Enhancing Postural Monitoring in Wheelchair Users Through Context Classification |
| title_fullStr | Enhancing Postural Monitoring in Wheelchair Users Through Context Classification |
| title_full_unstemmed | Enhancing Postural Monitoring in Wheelchair Users Through Context Classification |
| title_short | Enhancing Postural Monitoring in Wheelchair Users Through Context Classification |
| title_sort | enhancing postural monitoring in wheelchair users through context classification |
| topic | Biomedical monitoring wheelchairs smart healthcare context modeling intelligent sensors |
| url | https://ieeexplore.ieee.org/document/11115105/ |
| work_keys_str_mv | AT nereaperez enhancingposturalmonitoringinwheelchairusersthroughcontextclassification AT aitzibermancisidor enhancingposturalmonitoringinwheelchairusersthroughcontextclassification AT itziarcabanes enhancingposturalmonitoringinwheelchairusersthroughcontextclassification AT patrickvermander enhancingposturalmonitoringinwheelchairusersthroughcontextclassification |