Low-Cost Vision-Based 3-D Elbow Tracking for Post-Stroke Rehabilitation: Development and Pilot Evaluation of a Serious Game

Abstract

Stroke is a leading contributor to long-term disability worldwide, and rehabilitation often relies on costly devices, limited infrastructure, or labor-intensive protocols. While virtual reality-based exergames have gained popularity for promoting patient engagement, most rely on proprietary sensors or wearable electronics, limiting accessibility and clinical adaptability. This study presents the design, implementation, and pilot evaluation of a custom exergame that estimates the 3D elbow angle using a single RGB camera and two colored spheres as markers, eliminating the need for specialized hardware. The proposed system performs camera calibration, color segmentation, geometric 3D reconstruction, and real-time elbow angle estimation using low-cost equipment. Extensive technical tests revealed robust performance, with angular errors below 5° for joint amplitudes under 110°, and consistent accuracy across different lighting conditions, marker sizes, and distances. Additional tests showed that excessive sphere velocity (>20 cm/s) or proximity to image corners increased error due to motion blur and lens distortion, respectively. The system outperformed the AI-based MediaPipe framework in occluded-arm scenarios. Regression analysis showed strong correlation (r =0.70) between movement velocity and angular error. Usability testing with eight post-stroke participants yielded a mean SUS score of 92.5/100. The proposed solution is a promising alternative for home-based, sensor-free rehabilitation, supporting personalized exercise routines and remote progress monitoring.