Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time.