SEMG Based ANN for Shoulder Angle Prediction

Abstract

Many kinds of upper limb rehabilitation systems have been developing for physically weak and/or injured patients to assist their daily life activities and promote their quality of life. Among those systems, EMG controlled rehabilitation systems provide the most effective and fastest ways to restore the lost functions due to such weakness and injuries. This paper presents the prediction of shoulder angle based on acquisition of surface electromyogram (sEMG) signals. Backpropagation neural network (BPNN) controller is developed to predict the angle of shoulder flexion/extension and abduction/adduction movements. Virtual Reality (VR) human model is developed to simulate the predicted shoulder angle which results from BPNN controller. Four sEMG signals are collected from user arm muscles and processed to extract their feature with root mean square (RMS) method. Then, the signals features directed to the neural network as input and the network predicts the angle of the shoulder joint as an output. The angles from BPNN drive the shoulder joint of the VR human model in virtual environment. Experiments were carried out to evaluate the effectiveness of the developed system and it was found that the constructed BPNN model and VR model can well represent the relationship between sEMG and shoulder joint angles and rotation. These positive results elaborate a move to design and develop the EMG controlled upper limb rehabilitation robot system to rehabilitate the physically weak person and paralysed patients.