DocumentCode
3347144
Title
Basing on RBF Neural Network to Classify Surface Electromyography
Author
Shicai, Liu ; Qingju, Zhang ; Bo, Sun
Author_Institution
Sch. of Inf., Linyi Univ., Linyi, China
fYear
2011
fDate
21-23 Oct. 2011
Firstpage
262
Lastpage
265
Abstract
In this paper, a method is presented, which bases on Power Spectrum and RBF neural network. First, we calculate Power Spectrum eigenvector that is pretreated. Second, using the Power Spectrum coefficient to train the RBF neural network and classify the muscle movement of forearm. The experiment indicates this measure can reduce workload and get the relatively good results.
Keywords
eigenvalues and eigenfunctions; electromyography; learning (artificial intelligence); medical signal processing; radial basis function networks; RBF neural network training; forearm; muscle movement classification; power spectrum coefficient; power spectrum eigenvector; surface electromyography classification; Electrodes; Electromyography; Muscles; Pattern recognition; Radial basis function networks; Training; Wrist; Power Spectrum; RBF neural network; Signal; Surface Electromyography; pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation, Measurement, Computer, Communication and Control, 2011 First International Conference on
Conference_Location
Beijing
Print_ISBN
978-0-7695-4519-6
Type
conf
DOI
10.1109/IMCCC.2011.72
Filename
6154050
Link To Document