Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition.

Xiaoyun Liu,Hujiao Wang,Wei Zhang,Xian Hua,Xugang Xi,Peng Li

doi:10.1155/2020/8824194

Abstract

The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control. To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE). First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment. Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set. Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements. Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set. The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.

Highlights

Surface electromyography is an electrophysiological signal produced by a motor unit during the muscle activities of the human body
TM Trigno Wireless EMG (Delsys Inc, Natick, MA, USA) and proposed the feature extraction of Surface electromyography (sEMG) signals using a wavelet weighted permutation entropy (WWPE) method for recognizing seven hand movements based on four channels
Wavelet Decomposition. e four-channel raw sEMG signals of one set of hand movements acquired from one subject are shown in Figure 5. e horizontal coordinate represents the number of sample points, and the longitudinal coordinate represents the amplitude of sEMG signals

Summary

Introduction

Surface electromyography (sEMG) is an electrophysiological signal produced by a motor unit during the muscle activities of the human body. E research of collecting sEMG signals from specific muscle groups to recognize hand movements has gradually emerged, and many studies have been made in the field of intelligent prostheses [5]. E hand-action pattern recognition technology based on sEMG is deeply studied to obtain high-efficiency and accurate hand-action recognition ability, and the recognition results are converted into multidegree-of-freedom control instruction to drive the output [6], which has very important research significance and application value for the research of intelligent artificial hand. Due to the complexity of sEMG and the diversity of hand movements, there are still many problems and challenges to achieve efficient and accurate action analysis [10], such as the selection and extraction of sEMG features and the limited type and quantity of recognition actions. Few features can fully reflect the detailed characteristics of sEMG signals [17]. e major problem is extracting useful information from signals [18, 19]

Methods

Results

Discussion

Conclusion