Brief paper: Hand movement recognition based on biosignal analysis

Authors:
Pawel Wojtczak;Tito G. Amaral;Octavio P. Dias;Andrzej Wolczowski;Marek Kurzynski
Affiliations:
Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland;Escola Superior de Tecnologia de Setubal, CESET, Instituto Politecnico de Setubal, Campus do IPS, Setubal, Portugal and Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, ...;Escola Superior de Tecnologia de Setubal, CESET, Instituto Politecnico de Setubal, Campus do IPS, Setubal, Portugal and INESC - Lisboa, Portugal;Institute of Robotics and Informatics, Faculty of Electronics, Technical University of Wroclaw, Wroclaw, Poland;Institute of Robotics and Informatics, Faculty of Electronics, Technical University of Wroclaw, Wroclaw, Poland
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

Citing 0
Cited 2

Identification of EMG signals using discriminant analysis and SVM classifier

Expert Systems with Applications: An International Journal
Sequential recognition of EMG signals applied to the control of bioprosthetic hand: experimental comparative analysis of methods

AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications

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Abstract

This paper proposes a methodology that analyses and classifies the electromyographic (EMG) signals using neural networks to control multifunction prostheses. The control of these prostheses can be made using myoelectric signals taken from surface electrodes. Finger motions discrimination is the key problem in this study. Thus the emphasis, in the proposed work, is put on myoelectric signal processing approaches. The EMG signals classification system was established using the linear neural network. The experimental results show a promising performance in classification of motions based on biosignal patterns.