Modeling and classifying of sEMG based on FFT blind identification

被引：0
|
作者：
Li, Yang [1 ]
Tian, Yan-Tao [1 ,2 ]
Chen, Wan-Zhong [1 ]
机构：
[1] State Key Laboratory of Automobile Dynamic Simulation, Jilin University
[2] China Key Laboratory of Bionic Engineering, Ministry of Education
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2012年 / 38卷 / 01期
关键词：
Blind identification; Electromyographic signal (sEMG); Fast Fourier transform (FFT); Singular value decomposition;
D O I：
10.3724/SP.J.1004.2012.00128
中图分类号：
学科分类号：
摘要：
In this paper, the FFT-based blind identification method is used to establish surface electromyographic signal (sEMG) in order to overcome the disadvantage of sEMG, which is susceptible to muscle fatigue and external factors. With no assumption on the precise knowledge of channel order, the FFT (fast Fourier transform)-based method is able to estimate the channel parameters as well as determine channel order. It extends the cross-relation principle to the frequency domain via the discrete Fourier transform, and performs better in small sample signal modeling, which is suitable for sEMG. The parameters of sEMG model are used as the input of the improved BP neural network to classify different movement patterns and a better recognition result is achieved compared with other blind identification methods. Copyright © 2012 Acta Automatica Sinica. All rights reserved.
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页码：128 / 134
页数：6
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