An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time

• URL: http://arxiv.org/abs/2512.10437v1
• Date: Thu, 11 Dec 2025 08:56:03 GMT
• Title: An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time
• Authors: Stylianos Kandylakis, Christos Orfanopoulos, Georgios Siolas, Panayiotis Tsanakas,
• Abstract summary: This work presents an efficient algorithmic framework for real-time identification, classification, and evaluation of human physiotherapy exercises using mobile devices.<n>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.<n>To recognize full exercise movements and detect deviations from prescribed patterns, we employ a dynamic-programming scheme based on a modified Levenshtein distance algorithm.
• Score: 0.20878272814614096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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