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Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation

Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform wit...

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Autores principales: Barzegar Khanghah, Ali, Fernie, Geoff, Roshan Fekr, Atena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920527/
https://www.ncbi.nlm.nih.gov/pubmed/36772246
http://dx.doi.org/10.3390/s23031206
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author Barzegar Khanghah, Ali
Fernie, Geoff
Roshan Fekr, Atena
author_facet Barzegar Khanghah, Ali
Fernie, Geoff
Roshan Fekr, Atena
author_sort Barzegar Khanghah, Ali
collection PubMed
description Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform with the capability of automating exercise guidance is likely to have a significant impact on rehabilitation outcomes. In this paper, a new vision-based biofeedback system is designed and validated to identify the quality of performed exercises. This new system will help patients to refine their movements to get the most out of their plan of care. An open dataset was used, which consisted of data from 30 participants performing nine different exercises. Each exercise was labeled as “Correctly” or “Incorrectly” executed by five clinicians. We used a pre-trained 3D Convolution Neural Network (3D-CNN) to design our biofeedback system. The proposed system achieved average accuracy values of 90.57% ± 9.17% and 83.78% ± 7.63% using 10-Fold and Leave-One-Subject-Out (LOSO) cross validation, respectively. In addition, we obtained average F1-scores of 71.78% ± 5.68% using 10-Fold and 60.64% ± 21.3% using LOSO validation. The proposed 3D-CNN was able to classify the rehabilitation videos and feedback on the quality of exercises to help users modify their movement patterns.
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spelling pubmed-99205272023-02-12 Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation Barzegar Khanghah, Ali Fernie, Geoff Roshan Fekr, Atena Sensors (Basel) Article Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform with the capability of automating exercise guidance is likely to have a significant impact on rehabilitation outcomes. In this paper, a new vision-based biofeedback system is designed and validated to identify the quality of performed exercises. This new system will help patients to refine their movements to get the most out of their plan of care. An open dataset was used, which consisted of data from 30 participants performing nine different exercises. Each exercise was labeled as “Correctly” or “Incorrectly” executed by five clinicians. We used a pre-trained 3D Convolution Neural Network (3D-CNN) to design our biofeedback system. The proposed system achieved average accuracy values of 90.57% ± 9.17% and 83.78% ± 7.63% using 10-Fold and Leave-One-Subject-Out (LOSO) cross validation, respectively. In addition, we obtained average F1-scores of 71.78% ± 5.68% using 10-Fold and 60.64% ± 21.3% using LOSO validation. The proposed 3D-CNN was able to classify the rehabilitation videos and feedback on the quality of exercises to help users modify their movement patterns. MDPI 2023-01-20 /pmc/articles/PMC9920527/ /pubmed/36772246 http://dx.doi.org/10.3390/s23031206 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Barzegar Khanghah, Ali
Fernie, Geoff
Roshan Fekr, Atena
Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title_full Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title_fullStr Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title_full_unstemmed Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title_short Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation
title_sort design and validation of vision-based exercise biofeedback for tele-rehabilitation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920527/
https://www.ncbi.nlm.nih.gov/pubmed/36772246
http://dx.doi.org/10.3390/s23031206
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