Wavelet based neuro-fuzzy classification for EMG control

Xiaowen Zhang, Yupu Yang, Xiaoming Xu, Ming Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2002 International Conference on Communications, Circuits and Systems and West Sino Exposition, ICCCAS 2002 - Proceedings
PublisherIEEE
Pages1087-1089
Number of pages3
Volume2
ISBN (Electronic)0780375475, 9780780375475
DOIs
Publication statusPublished - 1 Jan 2002
Event1st International Conference on Communications, Circuits and Systems, ICCCAS 2002 - Tibet Hotel, Chengdu, China
Duration: 29 Jun 20021 Jul 2002

Conference

Conference1st International Conference on Communications, Circuits and Systems, ICCCAS 2002
CountryChina
CityChengdu
Period29/06/021/07/02

Keywords

  • EMG Classification
  • Neuro-Fuzzy Network
  • Wavelet Transform

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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