Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications

One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated...

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Main Authors: Giuseppina Gini, Matteo Arvetti, Ian Somlai, Michele Folgheraiter
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Applied Bionics and Biomechanics
Online Access:http://dx.doi.org/10.3233/ABB-2011-0024
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author Giuseppina Gini
Matteo Arvetti
Ian Somlai
Michele Folgheraiter
author_facet Giuseppina Gini
Matteo Arvetti
Ian Somlai
Michele Folgheraiter
author_sort Giuseppina Gini
collection DOAJ
description One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.
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institution OA Journals
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publishDate 2012-01-01
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spelling doaj-art-e9833ccb807b465db74bee72ba060cd32025-08-20T02:22:15ZengWileyApplied Bionics and Biomechanics1176-23221754-21032012-01-019214515510.3233/ABB-2011-0024Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic ApplicationsGiuseppina Gini0Matteo Arvetti1Ian Somlai2Michele Folgheraiter3Department of Electronics and Information of Politecnico di Milano, Milan, ItalyDepartment of Electronics and Information of Politecnico di Milano, Milan, ItalyDepartment of Micro-technology and Medical Device Technology, Technical University of Munich, Munich, GermanyDFKI, Bremen, GermanyOne of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.http://dx.doi.org/10.3233/ABB-2011-0024
spellingShingle Giuseppina Gini
Matteo Arvetti
Ian Somlai
Michele Folgheraiter
Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
Applied Bionics and Biomechanics
title Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
title_full Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
title_fullStr Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
title_full_unstemmed Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
title_short Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications
title_sort acquisition and analysis of emg signals to recognize multiple hand movements for prosthetic applications
url http://dx.doi.org/10.3233/ABB-2011-0024
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