Control of multifunction myoelectric hand using a real-time EMG pattern recognition

• Published in: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 3511 - 3516
• Main Authors: Jun-Uk Chu, Inhyuk Moon, Shin-Ki Kim, Mu-Seong Mun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Control systems; Electromyography; EMG; Feature extraction; linear-nonlinear feature projection; Multi-layer neural network; Myoelectric hand control; Neural networks; Pattern recognition; PCA; Principal component analysis; Real time systems; SOFM; wavelet packet transform; Wavelet packets; Wavelet transforms
• ISBN: 0780389123; 9780780389120
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2005.1545586

This paper proposes a novel real-time EMG pattern recognition for the control of a 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 myoelectric hand. From experimental results, we show that all processes, including myoelectric hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.