A System for Real-Time Shoulder Angle Measurement Using Computer Vision

Recently, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. This article presents a real-time shoulder angle measurement system utilizing computer vision and machine learning techniques, specifically designed for the rehabilitation of frozen shoulder and related conditions. The system leverages MediaPipe, an open-source framework, to capture and analyze shoulder movements accurately. With its modular architecture, the system processes video feeds in real time, detecting key body points and calculating shoulder angles, thereby providing immediate feedback to patients and therapists. The study evaluates the system's effectiveness in clinical and home-based environments, highlighting its potential to enhance physiotherapy by enabling precise, data-driven treatment plans. This research study also identifies current research gaps, proposes solutions, and explores future enhancements, including environmental adaptation, mobile integration, and the extension to other joints. This study has attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI), Computer vision, and a machine learning mode.