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BioMed Research International - Volume 2014 2014, Article ID 781769, 8 pages -

Research ArticleThe Key Laboratory of Biomedical Information Engineering, Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710049, China

Received 25 February 2014; Revised 15 August 2014; Accepted 4 September 2014; Published 5 November 2014

Academic Editor: Ivo Meinhold-Heerlein

Copyright © 2014 Yanyan Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic SEMG signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy RFBE method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis PCA and the Davies-Bouldin DB index were used to perform the feature selection. Lastly, the support vector machine SVM was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.

Autor: Yanyan Zhang, Gang Wang, Chaolin Teng, Zhongjiang Sun, and Jue Wang



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