Estimate the Kinematics with EMG Signal Using Fuzzy Wavelet Neural Network for Biomechanical Leg Application

Several linear and nonlinear models were proposed to predict the forward relationship between EMG signals and kinematics for biomechanical limbs, which is meaningful for EMG-based control. Although using nonlinear model to predict the kinematics is able to represent rational complex relationship between EMG signals and desired outputs, there exists high risk for overfitting models to training data and calculating burden because of the multi-channel variation EMG signals. Inspired by the hypothesis that CNS modulates muscle synergies to simplify the motor control and learning of coordinating variation of redundant joints, this paper proposed to extract the synergies to reduce the dimension of EMG-based control. Furthermore, the fuzzy wavelet neural network was developed to generate velocity–adapted gait by the reference gaits only with the limited set of experimental trials. The experimental results show the efficiency and robust of this approach.