Year: 2025
Type: article
Abstract: . The paper focuses on developing a method for evaluating the similarity between skeletal models of an instructor and a patient during physical therapy exercises. Unlike general-purpose measures, our approach considers the specific characteristics of therapeutic exercises, including initial positioning, predominant movements, and exercise pace. By incorporating the concept of informativeness for skeletal points and refining normalization techniques, we improve the accuracy of pairwise dissimilarity measures. Experimental results demonstrate improved separability of records based on the accuracy of exercise repetition, highlighting the potential of this approach for enhancing automated physical rehabilitation systems.
Authors: Parama Bagchi, Oleg S. Seredin, Andrey V. Kopylov, Egor E. Surkov, Nikita S. Mityugov, Alexei R. Tokarev, Debotosh Bhattacharjee
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413977440