Title :
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
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;
Conference_Titel :
Information Acquisition, 2004. Proceedings. International Conference on
Print_ISBN :
0-7803-8629-9
DOI :
10.1109/ICIA.2004.1373391
