Wearable Body Sensors Integrated into a Virtual Reality Environment - A Modality for Automating the Rehabilitation of...

Area of Science:

• Rehabilitation Engineering
• Biomedical Instrumentation
• Virtual Reality Applications

Background:

• Rising incidence of stroke and spinal cord injuries necessitates advanced rehabilitation solutions.
• Existing upper limb assessment tools may lack comprehensive motion capture capabilities.
• Need for portable and adaptable systems for diverse patient needs.

Purpose of the Study:

• To introduce a novel Virtual Reality (VR) system integrated with wearable sensor-based motion capture for upper limb impairment rehabilitation and assessment.
• To develop a modular and portable motion capture system using Inertial Measurement Units (IMUs).
• To establish a foundation for an AI-driven autonomous rehabilitation system.

Main Methods:

• Utilized 15 modular IMU sensors for full-body motion capture and 11 miniature sensors in hand gloves for hand motion.
• Integrated the motion capture system with a VR environment for rehabilitation and assessment.
• Validated sensor accuracy against a commercial robotic arm, achieving Root Mean Square Error below 1.78 degrees.

Main Results:

• Demonstrated high accuracy in capturing human body and hand motion with the wearable sensor system.
• The VR system offers enhanced assessment tools: range of motion, reachable workspace, and motor learning.
• Developed rehabilitative intervention games within the VR environment.

Conclusions:

• The developed VR system with wearable motion capture provides accurate, quantitative data for upper limb motor control assessment.
• The system supports personalized rehabilitation through AI-driven features and adaptive difficulty.
• Offers a versatile solution for both clinical and home-based rehabilitation settings.