Pose Estimation of Physiotherapy Exercises using ML Techniques

Abstract

This study introduces an innovative methodology for accurately classifying physiotherapy exercises, integrating Pose Estimation and diverse Machine Learning (ML) techniques within the alwaysAI framework. The workflow includes data preprocessing, feature normalization, feature extraction, exploration of ML techniques, model training, and evaluation using the accuracy metric, applied to eight diverse exercise datasets. Unlike traditional approaches relying solely on Support Vector Machines (SVM), this study explores a range of ML techniques adaptable to high-dimensional data, showcasing the effectiveness of the proposed methodology. The results demonstrate precise exercise classification based on pose information, affirming the robustness of the approach and highlighting its potential integration into physiotherapy practices. This research contributes to advancing technology-driven solutions in healthcare by emphasizing the versatility of combining pose estimation with ML techniques for precise physiotherapy exercise classification.