An Efficient Approach for Feature Selection of SEMG Signal

Author

Qi, Liang ; Ming, Ye ; Wenjie, Ma

Author_Institution

Inst. of Intell. Control & Robert Res., Hanzghou Dianzi Univ., Hangzhou

Volume

2

fYear

2008

fDate

17-18 Oct. 2008

Firstpage

134

Lastpage

137

Abstract

This paper introduces an approach to obtain the feature vectors of surface electromyography (sEMG) signal based on Hilbert Huang transform (HHT). An adaptive segmentation method that could effectively select appropriate intrinsic mode function (IMF) is proposed. With the features gathered by using the energy of one channel signal, we also provide an optimized strategy based on experiments and experiences to increase the recognition rate of hand-motion patterns. The results from SVM neural networks classifier are presented to support this approach.

Keywords

Hilbert transforms; biomechanics; electromyography; feature extraction; medical signal processing; neural nets; signal classification; support vector machines; Hilbert Huang transform; SEMG signal; SVM neural network classifier; adaptive segmentation method; feature selection; feature vector; hand-motion pattern recognition; intrinsic mode function; surface electromyography; Computational intelligence; Electromyography; Frequency conversion; Intelligent control; Neural networks; Pattern recognition; Signal design; Support vector machine classification; Support vector machines; Wrist; HHT; SEMG; SVM; feature; optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design, 2008. ISCID '08. International Symposium on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3311-7

Type

conf

DOI

10.1109/ISCID.2008.171

Filename

4725475