Development of sEMG based human machine interface control system for robotic watch

Abstract

In this paper, we introduced a novel and simple methods of extracting the general features of the EMG signal hand gestures segmentation for Human Machine Interface (HMI) for Spinal Cord Injury (SCI), recorded Electrode signals from the Abductor pollicies longus above the elbow are noise filtered and features sets were extracted. These feature set's are used to control wheelchair motion and control of Automated Instrument. Finally the results for HMI using SEMG signals of SCI proves that a subject can move the wheel chair in the desired direction and control of cursor; thus, a more intuitive human interface is implemented.