AI-based smart sensing and AR for gait rehabilitation assessment
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/28936 |
Resumo: | Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans. |
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AI-based smart sensing and AR for gait rehabilitation assessmentAIAALIMUIoT embedded systemsMachine learningNon-intrusivePhysical rehabilitationSensFloorSmart sensingWearable devicesHealth monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.MDPI2023-07-05T14:57:36Z2023-01-01T00:00:00Z20232023-07-05T15:56:32Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/28936eng2078-248910.3390/info14070355Monge, J.Raimundo, A.Ribeiro, G.Postolache, O.Santos, J.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:28:35Zoai:repositorio.iscte-iul.pt:10071/28936Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:12:49.488020Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
AI-based smart sensing and AR for gait rehabilitation assessment |
title |
AI-based smart sensing and AR for gait rehabilitation assessment |
spellingShingle |
AI-based smart sensing and AR for gait rehabilitation assessment Monge, J. AI AAL IMU IoT embedded systems Machine learning Non-intrusive Physical rehabilitation SensFloor Smart sensing Wearable devices |
title_short |
AI-based smart sensing and AR for gait rehabilitation assessment |
title_full |
AI-based smart sensing and AR for gait rehabilitation assessment |
title_fullStr |
AI-based smart sensing and AR for gait rehabilitation assessment |
title_full_unstemmed |
AI-based smart sensing and AR for gait rehabilitation assessment |
title_sort |
AI-based smart sensing and AR for gait rehabilitation assessment |
author |
Monge, J. |
author_facet |
Monge, J. Raimundo, A. Ribeiro, G. Postolache, O. Santos, J. |
author_role |
author |
author2 |
Raimundo, A. Ribeiro, G. Postolache, O. Santos, J. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Monge, J. Raimundo, A. Ribeiro, G. Postolache, O. Santos, J. |
dc.subject.por.fl_str_mv |
AI AAL IMU IoT embedded systems Machine learning Non-intrusive Physical rehabilitation SensFloor Smart sensing Wearable devices |
topic |
AI AAL IMU IoT embedded systems Machine learning Non-intrusive Physical rehabilitation SensFloor Smart sensing Wearable devices |
description |
Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-05T14:57:36Z 2023-01-01T00:00:00Z 2023 2023-07-05T15:56:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/28936 |
url |
http://hdl.handle.net/10071/28936 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2078-2489 10.3390/info14070355 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134684155740160 |