A VR-based self-rehabilitation system

Abstract

This paper proposed a VR-based self-rehabilitation system which utilizes the virtual training model rendered by OpenGL and collects electromyography (EMG) signals from the subjects to perform hand motion recognition. EMG signals are biomedical signals generated in muscles and can be applied in many fields such as clinical diagnosis and biomedical applications. The subjects were asked to manipulate a haptic device (Phantom Premium) to operate a virtual hand to catch a ball in the virtual environment which displayed on the computer's screen. A dry electrode was attached on the subject's skin to collect sEMG signals and recognize the action of grasping. Once caught by subjects, the virtual ball will appear in another location at random on the computer's screen. Therefore, the subject needs to manipulate the Phantom to the new destination and catch the ball once again. Combining sEMG with VR Technology, the proposed self-rehabilitation system could provide enhanced visual feedback about movement trajectory, which is beneficial to improve motor function task learning and execution compared with traditional therapy. By this method, stroke patients can realize self-rehabilitation exercise of upper limb at home. The effectiveness of the proposed rehabilitation system has been verified by experiments.