NDLI: The Application of Support Vector Machine in Pattern Recognition

Content Provider	IEEE Xplore Digital Library
Author	Jianguo Cui Zhonghai Li Jian Gao Rui Lv Xinhe Xu
Copyright Year	2007
Description	Author affiliation: Northeastern Univ., Shenyang (Jianguo Cui)
Abstract	To nonstationary characteristics of surface electromyography (sEMG) signals, a novel sEMG pattern recognition method, which is based on wavelet packet transformation and support vector machine (SVM), is proposed. Raw four channels sEMG signals from four corresponding muscles are first analyzed with wavelet packet transformation. And then the energy of different frequency bands in the wavelet packet decomposition coefficients is extracted as the signal character to construct eigenvector. A new multi-class SVM classifier is designed with "one versus one" classification strategy and binary tree. Experiment results show that eight upper-limb movement patterns can be well identified after training by the SVM and average identification ratio is 99.375%, and that the SVM can sort out sEMG eight movement patterns more accurately than traditional BP neural network, Elman neural network and RBF neural network. And the SVM recognition result is robust. It offers a new method for sEMG pattern recognition, which can be directly applied to the other nonstationary bioelectric signals pattern recognition study.
Starting Page	3135
Ending Page	3138
File Size	461303
Page Count	4
File Format	PDF
ISBN	9781424408177
DOI	10.1109/ICCA.2007.4376939
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Pattern recognition Support vector machine classification Wavelet packets Neural networks Classification tree analysis Surface waves Electromyography Muscles Signal analysis pattern recognition surface electromyography (sEMG) wavelet packet transformation support vector machine (SVM)
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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