Continuous Estimation of Human Knee-Joint Angles from SEMG Using Wavelet Neural Network

Abstract

Surface electromyography (sEMG) signals contain a wealth of information associated with human's movement. In this paper, a wavelet neural network (WNN) model is proposed and implemented to estimate human knee-joint angles from the sEMG signals. With the processed signals as input, the WNN model is trained to estimate the knee-joint angles in continuous motion. To validate the effectiveness of the WNN model, one able-bodied person sit in a chair and accomplish leg stretching in the experiment, and simultaneously record the sEMG signals from the vastus rectus (VR) and the angles of the knee joint. Then, the estimation results of the WNN model are compared with the RBF neural network and the BP neural network. The experimental results show that the WNN model has the best performance in the knee-joint angles estimation than the other two neural network models. The root mean square (RMS) error of the knee-joint angles is 6.5054° and the time is 5.3271 seconds. The proposed method can be applied to rehabilitation robots or assisted exoskeleton.