dc.contributor.author | Vermander García, Patrick ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.contributor.author | Mancisidor Barinagarrementeria, Aitziber | |
dc.contributor.author | Cabanes Axpe, Itziar | |
dc.contributor.author | Pérez Odriozola, Nerea | |
dc.contributor.author | Torres Unda, Juan José ![ORCID](/themes/Mirage2//images/orcid_16x16.png) | |
dc.date.accessioned | 2023-04-26T17:09:46Z | |
dc.date.available | 2023-04-26T17:09:46Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 : 944-953 (2023) | es_ES |
dc.identifier.issn | 1534-4320 | |
dc.identifier.issn | 1558-0210 | |
dc.identifier.uri | http://hdl.handle.net/10810/60950 | |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | This work was supported in part by the Ministry of Science and Innovation-StateResearch Agency/Project funded by MCIN/State Research Agency(AEI)/10.13039/501100011033 under Grant PID2020-112667RB-I00,in part by the Basque Government under Grant IT1726-22, and in part by the Predoctoral Contracts of the Basque Government under Grant PRE-2021-1-0001 and Grant PRE-2021-1-0214 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2020-112667RB-I00 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | artificial neural network | es_ES |
dc.subject | sitting posture classification | es_ES |
dc.subject | wheelchair | es_ES |
dc.subject | force sensors | es_ES |
dc.title | Intelligent Sitting Posture Classifier for Wheelchair Users | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0 | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/10016689 | es_ES |
dc.identifier.doi | 10.1109/TNSRE.2023.3236692 | |
dc.departamentoes | Fisiología | es_ES |
dc.departamentoes | Ingeniería de sistemas y automática | es_ES |
dc.departamentoeu | Fisiologia | es_ES |
dc.departamentoeu | Sistemen ingeniaritza eta automatika | es_ES |