%0 Journal Article
%T Modeling and Classifying of sEMG Based on FFT Blind Identification<br>基于FFT盲辨识的肌电信号建模及模式识别
%A LI Yang
%A TIAN Yan-Tao
%A CHEN Wan-Zhong
%A <br>李阳
%A 田彦涛
%A 陈万忠
%J 自动化学报
%D 2012
%I 
%X 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.
%K Electromyographic signal (sEMG)
%K blind identification
%K fast Fourier transform(FFT)
%K singular value decomposition<br>肌电信号
%K 盲辨识
%K 快速傅里叶变换
%K 奇异值分解
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=7541B02586A94E581B61A2EE0708F857&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=CA4FD0336C81A37A&sid=1F199509C0B6C4D6&eid=03A030BB0C519C60&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=13