Hand movement recognition based on biosignal analysis |
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| Authors: | Pawel Wojtczak Tito G Amaral Octavio P Dias Andrzej Wolczowski Marek Kurzynski |
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| Affiliation: | 1. Preventive Cardiology and Rehabilitation, Inst. Codivilla-Putti, Cortina d''Ampezzo (BL), Italy;2. Dpt Cardiac, Thoracic and Vascular Sciences, University of Padua, Padova, Italy;3. Dpt Cardiology, Krapinske Toplice Hospital for Medical Rehabilitation, Krapinske Toplice, Croatia;4. Faculty of Medicine, J. J. Strossmayer University of Osijek, Osijek, Croatia;5. Dpt Medicine, School of Emergency Medicine, University of Padua, Padova, Italy |
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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. |
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