Performance Assessment of Functional Upper Extremity Exercises with Deep Learning

Rehabilitation exercises are essential for recovery following surgery and for managing musculoskeletal conditions. However, regular in-person physiotherapy sessions can be costly and difficult to access, particularly in home-based or remote care settings. This study presents a deep learning-based approach for automatically evaluating rehabilitation exercise performance using RGB videos captured with standard, low-cost cameras. Unlike conventional systems requiring costly depth sensors or wearables, the proposed method extracts 3D joint positions from standard RGB videos to assess movement quality. The model is trained on expert physiotherapist scores to ensure clinically meaningful evaluations. Experimental results show that the model’s predictions closely match the scores given by physiotherapists, demonstrating the reliability and accuracy of the system. This framework offers a practical and scalable solution for remote monitoring of rehabilitation exercises, reducing dependence on clinical supervision while maintaining assessment quality. The findings highlight the potential of deep learning to support more accessible, flexible, and cost-effective rehabilitation, particularly for individuals with limited access to traditional care services.