Forehead sEMG signal based HMI for hands-free control

A hands-free method is proposed to control an electric powered wheelchair (EPW) based on surface electromyography (sEMG) signals. A CyberLink device is deployed to obtain and analyze forehead sEMG signals generated by the facial movements. The autoregressive (AR) model is used to extract sEMG features. Then, the back-propagation artificial neural network (BPANN) is proposed to recognize different facial movement patterns and improved by Bayesian regularization and Levenberg-Marquardt (LM) algorithm. A sEMG based human-machine interface (HMI) is designed to map facial movement patterns into corresponding control commands. The experimental results show that the method is simple, real-time and have a high recognition rate.