Control of active hand prostheses is an open challenge. In fact, the advances in mechatronics made available prosthetic hands with multiple active degrees of freedom; however the predominant control strategies are still not natural for the user, enabling only few gestures, thus not exploiting the prosthesis potential. Pattern recognition and machine learning techniques can be of great help when applied to surface electromyography signals to offer a natural control based on the contraction of muscles corresponding to the real movements. The implementation of such approach for an active prosthetic system offers many challenges related to the reliability of data collected to train the classification algorithm. This paper focuses on these problems and propose an implementation suitable for an embedded system. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.
Analysis of robust implementation of an EMG pattern recognition based control / Benatti, S.; Farella, E.; Gruppioni, E.; Benini, L.. - (2014), pp. 45-54. (Intervento presentato al convegno 7th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014 tenutosi a Angers, Loire Valley, fra nel 2014) [10.5220/0004800300450054].
Analysis of robust implementation of an EMG pattern recognition based control
Benatti S.;
2014
Abstract
Control of active hand prostheses is an open challenge. In fact, the advances in mechatronics made available prosthetic hands with multiple active degrees of freedom; however the predominant control strategies are still not natural for the user, enabling only few gestures, thus not exploiting the prosthesis potential. Pattern recognition and machine learning techniques can be of great help when applied to surface electromyography signals to offer a natural control based on the contraction of muscles corresponding to the real movements. The implementation of such approach for an active prosthetic system offers many challenges related to the reliability of data collected to train the classification algorithm. This paper focuses on these problems and propose an implementation suitable for an embedded system. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.
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