Design of a surface EMG based human-machine interface for an intelligent wheelchair

This paper presents a novel human-machine interface to control an intelligent wheelchair based on surface electromyography (sEMG) signals. Forehead sEMG signals generated by the facial movements are obtained and analysed by using a CyberLink sensing device. The autoregressive (AR) model is used to extract sEMG features. Then, the BP artificial neural network (BPANN) improved by Levenberg-Marquardt algorithm is proposed to recognize different facial movement patterns. A human-machine interface (HMI) is designed to map facial movement patterns into corresponding control commands. The experiment results show that the method is simple, real-time and at a high recognition rate. It lays the foundation for us to use forehead sEMG signals based control of wheelchairs in real world applications.