Machine Learning-Based Computer Vision for Depth Camera-Based Physiotherapy Movement Assessment: A Systematic Review

Background: Machine learning-based computer vision techniques using depth cameras have shown potential in physiotherapy movement assessment. However, a comprehensive understanding of their implementation, effectiveness, and limitations remains needed. Methods: We conducted a systematic review following PRISMA guidelines, searching Web of Science, Scopus, PubMed, and Astrophysics Data System databases (2020-2024). From 371 initially identified publications, 18 met the inclusion criteria for detailed analysis. Results: The analysis revealed three primary implementation scenarios: local (50\%), clinical (33.4\%), and remote (22.3\%). Depth cameras, particularly the Kinect series (65.4\%), dominated data collection methods. Data processing approaches primarily utilized RGB-D (55.6\%) and skeletal data (27.8\%), with algorithms split between traditional machine learning (44.4\%) and deep learning (41.7\%). Key challenges included limited real-world validation, insufficient dataset diversity, and algorithm generalization issues. Conclusions: While machine learning-based computer vision systems demonstrated effectiveness in movement assessment tasks, further research is needed to address validation in clinical settings and improve algorithm generalization. This review provides a foundation for enhancing computer vision-based assessment tools in physiotherapy practice.