NDLI: A real-time EMG pattern recognition based on linear-nonlinear feature projection for multifunction myoelectric hand

Content Provider	IEEE Xplore Digital Library
Author	Jun-Uk Chu Inhyuk Moon Mu-Seong Mun
Copyright Year	2005
Description	Author affiliation: Korea Orthopedics & Rehabilitation Eng. Center, Incheon, South Korea (Jun-Uk Chu; Inhyuk Moon; Mu-Seong Mun)
Abstract	This paper proposes a novel real-time EMG pattern recognition for the control of multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.
Starting Page	295
Ending Page	298
File Size	408548
Page Count	4
File Format	PDF
ISBN	0780390032
DOI	10.1109/ICORR.2005.1501105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Real time systems Neural networks Feature extraction Control systems Electromyography Wavelet packets Pattern recognition Principal component analysis Multi-layer neural network
Content Type	Text
Resource Type	Article

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