Wireless Single-Camera Markerless Motion Capture System for Healthcare Applications

Abstract

Single-camera markerless systems have emerged as a robust methodology for human motion capture and rehabilitation applications. Traditional methodologies typically necessitate multiple strategically positioned cameras or special equipment, including sensors to capture patient ambulatory motion, requiring preliminary calibration and synchronization procedures, which may incur significant costs. This paper presents a wireless single-camera markerless framework for rehabilitation applications that leverages advanced deep learning (DL) architectures to estimate and extract three-dimensional skeletal coordinates from monocular camera views of ambulatory patients. The extracted skeletal representation is subsequently transmitted across wireless communication channels. Then, the rendering technique has been applied for displaying virtual movement of the patient for privacy enhancement. Simulations demonstrate the effectiveness of the framework while maintaining motion assessment capabilities, presenting opportunities for deployment in remote healthcare monitoring scenarios.