DocumentCode :
554975
Title :
SEMG feature extraction methods for pattern recognition of upper limbs
Author :
Feng Zhang ; Pengfeng Li ; Zeng-Guang Hou ; Yixiong Chen ; Fei Xu ; Jin Hu ; Qingling Li ; Min Tan
Author_Institution :
Lab. of Complex Syst. & Intell. Sci., Chinese Acad. of Sci., Beijing, China
fYear :
2011
fDate :
11-13 Aug. 2011
Firstpage :
222
Lastpage :
227
Abstract :
In this paper, a new feature of surface electromyo-graphy (sEMG) by using discrete wavelet transform (DWT) is proposed for motion recognition of upper limbs, and this method can be eventually used for rehabilitation robot control. Seven traditional features of sEMG are also extracted for comparative study, they are integral of absolute value (IAV), difference absolute mean value (DAMV), zero crossing (ZC), variance (VAR), mean power spectral density (MPSD), mean frequency (MF) and median frequency (MDF) respectively. For comparing the recognition rate of the different motions of the upper limb, each feature or their combination are used to construct the feature vectors, and the BP neural network with variable learning rate back propagation with momentum (GDX) algorithm is used to classify these motion modes. The experimental results summarize that the new feature extracted by using DWT presents a higher recognition rate (98.9%) than all of the traditional features, and the traditional features combination can also greatly improve the recognition rate (99%).
Keywords :
backpropagation; discrete wavelet transforms; electromyography; feature extraction; medical computing; medical control systems; mobile robots; neural nets; patient rehabilitation; BP neural network; SEMG feature extraction method; difference absolute mean value; discrete wavelet transform; feature vectors; integral of absolute value; mean frequency; mean power spectral density; median frequency; motion recognition; pattern recognition; rehabilitation robot control; surface electromyography; upper limb; variable learning rate back propagation with momentum algorithm; variance; zero crossing; Discrete wavelet transforms; Feature extraction; Muscles; Neurons; Reactive power; Signal processing algorithms; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Mechatronic Systems (ICAMechS), 2011 International Conference on
Conference_Location :
Zhengzhou
Print_ISBN :
978-1-4577-1698-0
Type :
conf
Filename :
6025019
Link To Document :
بازگشت