An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914194301911040 |
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| author | Kandylakis, Stylianos Orfanopoulos, Christos Siolas, Georgios Tsanakas, Panayiotis |
| author_facet | Kandylakis, Stylianos Orfanopoulos, Christos Siolas, Georgios Tsanakas, Panayiotis |
| contents | This work presents an efficient algorithmic framework for real-time identification, classification, and evaluation of human physiotherapy exercises using mobile devices. The proposed method interprets a kinetic movement as a sequence of static poses, which are estimated from camera input using a pose-estimation neural network. Extracted body keypoints are transformed into trigonometric angle-based features and classified with lightweight supervised models to generate frame-level pose predictions and accuracy scores. To recognize full exercise movements and detect deviations from prescribed patterns, we employ a dynamic-programming scheme based on a modified Levenshtein distance algorithm, enabling robust sequence matching and localization of inaccuracies. The system operates entirely on the client side, ensuring scalability and real-time performance. Experimental evaluation demonstrates the effectiveness of the methodology and highlights its applicability to remote physiotherapy supervision and m-health applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_10437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time Kandylakis, Stylianos Orfanopoulos, Christos Siolas, Georgios Tsanakas, Panayiotis Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68T10 I.2.10; I.5.4 This work presents an efficient algorithmic framework for real-time identification, classification, and evaluation of human physiotherapy exercises using mobile devices. The proposed method interprets a kinetic movement as a sequence of static poses, which are estimated from camera input using a pose-estimation neural network. Extracted body keypoints are transformed into trigonometric angle-based features and classified with lightweight supervised models to generate frame-level pose predictions and accuracy scores. To recognize full exercise movements and detect deviations from prescribed patterns, we employ a dynamic-programming scheme based on a modified Levenshtein distance algorithm, enabling robust sequence matching and localization of inaccuracies. The system operates entirely on the client side, ensuring scalability and real-time performance. Experimental evaluation demonstrates the effectiveness of the methodology and highlights its applicability to remote physiotherapy supervision and m-health applications. |
| title | An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 68T10 I.2.10; I.5.4 |
| url | https://arxiv.org/abs/2512.10437 |