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 by the envir...

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Autores: Pérez Odriozola, Nerea, Mancisidor Barinagarrementeria, Aitziber, Cabanes Axpe, Itziar, Vermander García, Patrick
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/75084
Acceso en línea:http://hdl.handle.net/10810/75084
Access Level:acceso abierto
Palabra clave:biomedical monitoring
wheelchairs
smart healthcare
context modeling
intelligent sensors
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spelling Enhancing Postural Monitoring in Wheelchair Users Through Context ClassificationPérez Odriozola, NereaMancisidor Barinagarrementeria, AitziberCabanes Axpe, ItziarVermander García, Patrickbiomedical monitoringwheelchairssmart healthcarecontext modelingintelligent sensorsGlobally, 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.This work was supported in part by the Fondo Europeo de Desarrollo Regional (FEDER)/Ministry of Science and Innovation- State Research Agency funded by MCIN/AEI/10.13039/501100011033 under Project PID2020-112667RB-I00 and in part by the Basque Government under Grant IT1726-22.IEEE202520252025info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/75084reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/PID2020-112667RB-I00/https://ieeexplore.ieee.org/document/11115105info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.oai:addi.ehu.eus:10810/750842026-06-18T09:23:17Z
dc.title.none.fl_str_mv Enhancing Postural Monitoring in Wheelchair Users Through Context Classification
title Enhancing Postural Monitoring in Wheelchair Users Through Context Classification
spellingShingle Enhancing Postural Monitoring in Wheelchair Users Through Context Classification
Pérez Odriozola, Nerea
biomedical monitoring
wheelchairs
smart healthcare
context modeling
intelligent sensors
title_short 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_sort Enhancing Postural Monitoring in Wheelchair Users Through Context Classification
dc.creator.none.fl_str_mv Pérez Odriozola, Nerea
Mancisidor Barinagarrementeria, Aitziber
Cabanes Axpe, Itziar
Vermander García, Patrick
author Pérez Odriozola, Nerea
author_facet Pérez Odriozola, Nerea
Mancisidor Barinagarrementeria, Aitziber
Cabanes Axpe, Itziar
Vermander García, Patrick
author_role author
author2 Mancisidor Barinagarrementeria, Aitziber
Cabanes Axpe, Itziar
Vermander García, Patrick
author2_role author
author
author
dc.subject.none.fl_str_mv biomedical monitoring
wheelchairs
smart healthcare
context modeling
intelligent sensors
topic biomedical monitoring
wheelchairs
smart healthcare
context modeling
intelligent sensors
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.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/75084
url http://hdl.handle.net/10810/75084
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/PID2020-112667RB-I00/
https://ieeexplore.ieee.org/document/11115105
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
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