Hand gesture recognition by using sEMG signals for human machine interaction applications

Abstract

Cyber physical systems are gaining more place in daily life so interaction with the machines are increasing. Hand gestures are one of the tools for interaction with the machines and human-machines interfaces. Image processing, sensor based and sEMG based methods are the most popular for hand gesture recognition. sEMG based hand gesture recognition is chosen especially for graphical controller, hand rehabilitation software development and manipulation of robotic devices etc. In this study, classification of 5 hand motion, which are hand open, hand close, cylindrical grasp, Lateral pinch(key grasp) and index finger opening, have been realized. As a classifier, Artificial Neural Network(ANN) is used. The Data used for training and validation recorded from five subjects by using MYO® armband. Mean absolute value, slope sign change, waveform length, Willison amplitude and mean frequency features are used for classification. Classification performances were evaluated for all five subject together and each subject separately. In the study, we achieved 88.4% mean classification rate by using five subject's recordings. © 2018 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).