Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques

Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monito...

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Detalles Bibliográficos
Autores: Vermander García, Patrick, Mancisidor Barinagarrementeria, Aitziber, Gravina, Raffaele, Cabanes Axpe, Itziar, Fortino, Giancarlo
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:10810/75081
Acceso en línea:http://hdl.handle.net/10810/75081
Access Level:acceso abierto
Palabra clave:sitting posture monitoring
anomaly detection
assistive technology
pressure sensors
unsupervised techniques
individualization
wheelchair
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spelling Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniquesVermander García, PatrickMancisidor Barinagarrementeria, AitziberGravina, RaffaeleCabanes Axpe, ItziarFortino, Giancarlositting posture monitoringanomaly detectionassistive technologypressure sensorsunsupervised techniquesindividualizationwheelchairDetecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.This work has been funded by: FEDER/Ministry of Science and Innovation - State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033, the Basque Government, IT1726-22, as well as by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Government. The work has also been partially supported by the Italian MIUR, PRIN 2020 Project “COMMON-WEARS”, N.2020HCWWLP, CUP: H23C22000230005; we also acknowledge co-funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, through the Italian MUR, PRIN 2022 Project ”COCOWEARS” (A framework for COntinuum COmputing WEARable Systems), N. 2022T2XNJE, CUP: H53D23003640006.Elsevier202520252025info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/75081reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/PID2020-112667RB-I00/https://www.sciencedirect.com/science/article/pii/S235286482400066Xinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/© 2024 Chongqing University of Posts and Telecommunications. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND licenseoai:addi.ehu.eus:10810/750812026-06-18T09:23:17Z
dc.title.none.fl_str_mv Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
title Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
spellingShingle Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
Vermander García, Patrick
sitting posture monitoring
anomaly detection
assistive technology
pressure sensors
unsupervised techniques
individualization
wheelchair
title_short Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
title_full Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
title_fullStr Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
title_full_unstemmed Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
title_sort Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques
dc.creator.none.fl_str_mv Vermander García, Patrick
Mancisidor Barinagarrementeria, Aitziber
Gravina, Raffaele
Cabanes Axpe, Itziar
Fortino, Giancarlo
author Vermander García, Patrick
author_facet Vermander García, Patrick
Mancisidor Barinagarrementeria, Aitziber
Gravina, Raffaele
Cabanes Axpe, Itziar
Fortino, Giancarlo
author_role author
author2 Mancisidor Barinagarrementeria, Aitziber
Gravina, Raffaele
Cabanes Axpe, Itziar
Fortino, Giancarlo
author2_role author
author
author
author
dc.subject.none.fl_str_mv sitting posture monitoring
anomaly detection
assistive technology
pressure sensors
unsupervised techniques
individualization
wheelchair
topic sitting posture monitoring
anomaly detection
assistive technology
pressure sensors
unsupervised techniques
individualization
wheelchair
description Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
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/10810/75081
url http://hdl.handle.net/10810/75081
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://www.sciencedirect.com/science/article/pii/S235286482400066X
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 Elsevier
publisher.none.fl_str_mv Elsevier
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
repository.mail.fl_str_mv
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