Research on upper limb rehabilitation system based on visual feedback

Abstract

Patients with the loss of limb motor function often suffer from neurological damage of brain in the presence of normal limb neural pathways, resulting in reduced or lost limb movement. By observing the mirror image of the healthy limb movement, the relevant motor perception areas of the patient's brain can be stimulated and the motor and brain functions of the affected limb could also be remodeled. Based on the above theoretical support, this study designed a visual feedback upper limb rehabilitation system based on an attentional neural network, which analyzed and modelled the electromyography (EMG) of the upper limb through a new attentional neural network named MCAT-net. The proposed MCAT-net could determine the movement intention of the healthy upper limb with an accuracy about 91.04%. Further, based on MCAT-net, a visual feedback upper limb rehabilitation system is designed, which could receive and generate the augmented reality models of the affected upper limb for the patients who need the purpose of visual rehabilitating stimulation.