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Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning

Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing auto...

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Autores principales: Arrowsmith, Colin, Burns, David, Mak, Thomas, Hardisty, Michael, Whyne, Cari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824820/
https://www.ncbi.nlm.nih.gov/pubmed/36616961
http://dx.doi.org/10.3390/s23010363
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author Arrowsmith, Colin
Burns, David
Mak, Thomas
Hardisty, Michael
Whyne, Cari
author_facet Arrowsmith, Colin
Burns, David
Mak, Thomas
Hardisty, Michael
Whyne, Cari
author_sort Arrowsmith, Colin
collection PubMed
description Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low-back and shoulder physiotherapy exercises using a mobile phone camera. Joint locations were extracted from the videos of healthy subjects performing low-back and shoulder physiotherapy exercises using an open source pose detection framework. A convolutional neural network was trained to classify physiotherapy exercises based on the segments of keypoint time series data. The model’s performance as a function of input keypoint combinations was studied in addition to its robustness to variation in the camera angle. The CNN model achieved optimal performance using a total of 12 pose estimation landmarks from the upper and lower body (low-back exercise classification: 0.995 ± 0.009; shoulder exercise classification: 0.963 ± 0.020). Training the CNN on a variety of angles was found to be effective in making the model robust to variations in video filming angle. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively classify at-home physiotherapy participation and could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings.
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spelling pubmed-98248202023-01-08 Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning Arrowsmith, Colin Burns, David Mak, Thomas Hardisty, Michael Whyne, Cari Sensors (Basel) Article Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low-back and shoulder physiotherapy exercises using a mobile phone camera. Joint locations were extracted from the videos of healthy subjects performing low-back and shoulder physiotherapy exercises using an open source pose detection framework. A convolutional neural network was trained to classify physiotherapy exercises based on the segments of keypoint time series data. The model’s performance as a function of input keypoint combinations was studied in addition to its robustness to variation in the camera angle. The CNN model achieved optimal performance using a total of 12 pose estimation landmarks from the upper and lower body (low-back exercise classification: 0.995 ± 0.009; shoulder exercise classification: 0.963 ± 0.020). Training the CNN on a variety of angles was found to be effective in making the model robust to variations in video filming angle. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively classify at-home physiotherapy participation and could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings. MDPI 2022-12-29 /pmc/articles/PMC9824820/ /pubmed/36616961 http://dx.doi.org/10.3390/s23010363 Text en © 2022 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
Arrowsmith, Colin
Burns, David
Mak, Thomas
Hardisty, Michael
Whyne, Cari
Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title_full Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title_fullStr Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title_full_unstemmed Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title_short Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning
title_sort physiotherapy exercise classification with single-camera pose detection and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824820/
https://www.ncbi.nlm.nih.gov/pubmed/36616961
http://dx.doi.org/10.3390/s23010363
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