Multi-pattern recognition of the forearm movement based on SEMG

Author

Luo, Zhizeng ; Ren, Xiaoliang ; Jia, Yutao

Author_Institution

Robot Res. Inst., Hangzhou Inst. of Electron. Eng., China

fYear

2004

fDate

21-25 June 2004

Firstpage

369

Lastpage

371

Abstract

In the article, a new feature extraction method of surface electromyography (SEMG) is introduced. It is called power spectrum coefficient method. This method defines the ratio of maximum energy-band spectrum and power spectrum as an eigenvalue, mostly depressing the influence of special person. By using Bayes statistics algorithm in the power spectrum coefficient method, multipattern recognition of the forearm movement is fulfilled. The experiment verified that it is effective for recognition, and in the state of nonspecific-person, the correctness of recognition reaches eighty-four percents.

Keywords

Bayes methods; artificial limbs; band structure; decision making; eigenvalues and eigenfunctions; electromyography; feature extraction; medical image processing; Bayes statistics decision-making algorithm; eigenvalue; feature extraction method; forearm movement; maximum energy-band spectrum; multipattern recognition; power spectrum coefficient method; surface electromyography; Artificial limbs; Data mining; Decision making; Eigenvalues and eigenfunctions; Electromyography; Feature extraction; Frequency; Muscles; Statistics; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2004. Proceedings. International Conference on

Print_ISBN

0-7803-8629-9

Type

conf

DOI

10.1109/ICIA.2004.1373391

Filename

1373391