Pattern recognition of surface electromyography signal based on multi-scale fuzzy entropy.
- Author:
Xiaoyang ZOU
1
;
Min LEI
Author Information
1. Institute of Vibration, Shock and Noise, Shanghai Jiao Tong University, 200240 Shanghai, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electromyography;
methods;
Entropy;
Fuzzy Logic;
Humans;
Muscle, Skeletal;
physiology;
Pattern Recognition, Automated;
methods;
Signal Processing, Computer-Assisted;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2012;29(6):1184-1188
- CountryChina
- Language:Chinese
-
Abstract:
Action surface electromyography (SEMG) signals can be acquired from human skin surface. Its pattern recognition plays a very important role in practical applications such as human prosthesis and human-computer interface systems. For the purpose of increasing the recognition accuracy, we proposed a new recognition method combining fuzzy entropy (FuzzyEn) with multi-scale analysis. Considering the nonlinear and non-stationary characteristics of the SEMG, a multi-scale fuzzy entropy (MSFuzzyEn) feature was introduced and applied to the pattern recognition of six type action SEMG signals of the forearm. Firstly, multi-scale decomposition was applied to original signal using wavelet decomposition. Then MSFuzzyEn of the decomposed signals were calculated and inputted to support vector machine (SVM) for classification as feature vectors. The mean recognition accuracy reached 97%, which was 3% greater than that when FuzzyEn of original signal is applied to the classification of SEMG signals. The results have proved that the MSFuzzyEn is effective and precise in the classification of action SEMG signals.
