AI-Powered Real-Time Tele-Rehabilitation System Using Computer Vision for Orthopedic Patients

Abstract

This methodology presents the design and implementation of an AI-powered computer vision–based physiotherapy rehabilitation system intended to support home and remote exercise monitoring. The proposed system utilizes real-time pose estimation through a standard webcam to detect body key points, compute joint angles, and evaluate exercise performance without the need for wearable sensors or invasive devices. By integrating machine learning–based movement classification with automated visual and audio feedback, the system provides objective posture correction and repetition tracking. Session data is recorded and analyzed to generate performance metrics and progress reports, enabling quantitative assessment of rehabilitation outcomes. The results demonstrate that camera-based motion analysis can offer a cost-effective, non-invasive, and scalable solution for enhancing accessibility and consistency in physiotherapy rehabilitation.In addition to improving accessibility, the system significantly reduces the dependency on continuous clinical supervision by enabling patients to perform exercises independently at home. The use of real-time feedback ensures that users are immediately guided to correct improper posture, thereby minimizing the risk of injury and improving exercise effectiveness. The system is designed with a user-friendly interface that allows individuals with minimal technical knowledge to operate it Furthermore, the integration of automated data logging ensures accurate tracking of patient performance over time, which can be useful for long-term rehabilitation planning. The ability to generate structured reports supports better communication between patients and physiotherapists. The system also promotes consistency in exercise routines by providing continuous monitoring and reminders.