A human motion angle prediction method using a neural network and feature-extraction-processed sEMG obtained in real time to utilize mechanical delay of sEMG

Abstract

Surface electromyogram (sEMG) has been used to estimate human motions. Feature extraction (FE) has been proposed as a process to decrease noise and extract information about muscle activities from measured raw sEMG signals. Electromechanical delay (EMD) is a time delay between a generation of sEMG and subsequent occurrence of human motions, and sEMG can be obtained in advance of motions occurrence by using this time delay. The purpose of this study is to propose a real time human motion prediction method using a neural network model and FE-processed sEMG signals utilizing EMD. Experimental results show that the proposed method has better prediction results among other methods.