Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning Article Swipe
Colin Arrowsmith
,
David Burns
,
Thomas C. W. Mak
,
Michael Hardisty
,
Cari Whyne
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s23010363
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s23010363
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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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23010363
- https://www.mdpi.com/1424-8220/23/1/363/pdf?version=1672308063
- OA Status
- gold
- Cited By
- 33
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313294819
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313294819Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23010363Digital Object Identifier
- Title
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Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
-
Colin Arrowsmith, David Burns, Thomas C. W. Mak, Michael Hardisty, Cari WhyneList of authors in order
- Landing page
-
https://doi.org/10.3390/s23010363Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/1/363/pdf?version=1672308063Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/1/363/pdf?version=1672308063Direct OA link when available
- Concepts
-
Convolutional neural network, Computer science, Artificial intelligence, Robustness (evolution), Machine learning, Physical medicine and rehabilitation, Medicine, Biochemistry, Chemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
33Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 15, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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