Title :
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
fDate :
29 June-1 July 2002
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;
Conference_Titel :
Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on
Print_ISBN :
0-7803-7547-5
DOI :
10.1109/ICCCAS.2002.1178974
