Wavelet based neuro-fuzzy classification for EMG control

Author

Zhang, Xiaowen ; Yang, Yupu ; Xu, Xiaoming ; Zhang, Ming

Author_Institution

Dept. of Autom., Shanghai Jiaotong Univ., China

Volume

2

fYear

2002

fDate

29 June-1 July 2002

Firstpage

1087

Abstract

High accuracy of multiple degrees of freedom in prosthetic control is hard to obtain because uncertainty exists between different movements. Neuro-fuzzy technology is suitable to deal with such problems. We adopt a wavelet based neuro-fuzzy approach to classify EMG signals for movement recognition in order to decrease classification error. EMG signals are analyzed by wavelet transform, and feature vectors are constructed by SVD transform from wavelet coefficients for further movement recognition. A neuro-fuzzy network is designed as classifier, and its initialization and training are also involved. Comparison results for this method and traditional ones are provided to show its efficiency. High recognition and reliability are achieved in preliminary experiments.

Keywords

biocontrol; electromyography; fuzzy neural nets; learning (artificial intelligence); medical signal processing; pattern classification; prosthetics; signal classification; singular value decomposition; wavelet transforms; EMG control; SVD transform; feature vectors; initialization; movement recognition; multiple degrees of freedom; neuro-fuzzy network; prosthetic control; signal classification; training; wavelet coefficients; Automatic control; Control systems; Data mining; Electromyography; Feature extraction; Prosthetics; Signal analysis; Uncertainty; Wavelet analysis; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on

Print_ISBN

0-7803-7547-5

Type

conf

DOI

10.1109/ICCCAS.2002.1178974

Filename

1178974