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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9824820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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