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

arXiv: https://arxiv.org/abs/2512.10437 · HTML: https://mlsanity.com/paper/2512.10437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 1 of 1)

- [Identifying document similarity using a fast estimation of the Levenshtein Distance based on compression and signatures](https://mlsanity.com/paper/2307.11496/) (2023)

## Abstract (arXiv)

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.