A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.

Full Abstract

This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (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 into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.

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Author information

Author/s: Chu, Jun-Uk (JU); Moon, Inhyuk (I); Mun, Mu-Seong (MS);

Affiliation: Korea Orthopedics and Rehabilitation Engineering Center, Incheon 403-712, Korea.

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng), published in United States. (Language: eng)

Reference: 2006-Nov; vol 53 (issue 11) : pp 2232-9

Dates: Created 2006/10/31; Completed 2006/12/05;

PMID: 17073328, status: MEDLINE (last retrieval date: 12/26/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Linear Models
Models, Biological
Muscle, Skeletal - physiopathology
Nonlinear Dynamics
Pattern Recognition, Automated - methods
Principal Component Analysis
Prosthesis Design
Robotics - methods
User-Computer Interface

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