Surface electromyography signal classification using gray system theory.
- Author:
Hongbo XIE
1
;
Congbin MA
;
Zhizhong WANG
;
Hai HUANG
Author Information
1. Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai 200030, China. xiehb2008@hotmail.com
- Publication Type:Journal Article
- MeSH:
Electromyography;
Humans;
Models, Biological;
Muscle, Skeletal;
physiology;
Neural Networks (Computer);
Signal Processing, Computer-Assisted;
Systems Theory
- From:
Journal of Biomedical Engineering
2004;21(6):901-904
- CountryChina
- Language:Chinese
-
Abstract:
A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.
