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

TL;DR

This paper introduces a real-time, mobile-based algorithmic system for identifying, classifying, and evaluating physiotherapy exercises using pose estimation and sequence matching, supporting remote healthcare.

Contribution

It presents a novel client-side framework combining pose estimation, feature extraction, and sequence matching for real-time physiotherapy assessment on mobile devices.

Findings

High accuracy in exercise classification

Robust detection of exercise deviations

Operates efficiently on mobile hardware

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.