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Authors: Paulo Viana 1 ; Victoria Fujii 1 ; Larissa Lima 1 ; Gabriel Ouriques 1 ; Gustavo Oliveira 2 ; Renato Varoto 3 and Alberto Cliquet Jr. 4

Affiliations: 1 Department of Electrical and Computer Engineering, Trabalhador São-Carlense Avenue, 400, São Carlos and Brazil ; 2 University of São Paulo Interunits Graduate Program in Bioengineering, University of São Paulo, Trabalhador São-Carlense Avenue, 400, São Carlos and Brazil ; 3 Department of Orthopedics and Traumatology, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas and Brazil ; 4 Department of Electrical and Computer Engineering, Trabalhador São-Carlense Avenue, 400, São Carlos, Brazil, University of São Paulo Interunits Graduate Program in Bioengineering, University of São Paulo, Trabalhador São-Carlense Avenue, 400, São Carlos, Brazil, Department of Orthopedics and Traumatology, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas and Brazil

ISBN: 978-989-758-353-7

Keyword(s): Neural Networks, Hand Movement, Electromyography, Rehabilitation, Machine Learning.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing

Abstract: In this paper we present the development of an artificial neural network that uses surface EMG data from two forearm muscles to classify hand movements and gestures. We trained our network to classify three different sets of movements, using EMG data from six healthy subjects. We were able to achieve hit rates of above 99% in the training sets and hit rates of above 85% in all three test sets, with a maximum of 88.8% for the second movement set. Advantages of the proposed method include small number of electrodes, reduced complexity, computational cost and response time.

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Paper citation in several formats:
Viana, P.; Fujii, V.; Lima, L.; Ouriques, G.; Oliveira, G.; Varoto, R. and Cliquet Jr., A. (2019). An Artificial Neural Network for Hand Movement Classification using Surface Electromyography.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS, ISBN 978-989-758-353-7, pages 185-192. DOI: 10.5220/0007404201850192

@conference{biosignals19,
author={Paulo L. Viana. and Victoria S. Fujii. and Larissa M. Lima. and Gabriel L. Ouriques. and Gustavo C. Oliveira. and Renato Varoto. and Alberto Cliquet Jr..},
title={An Artificial Neural Network for Hand Movement Classification using Surface Electromyography},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS,},
year={2019},
pages={185-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404201850192},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4 BIOSIGNALS: BIOSIGNALS,
TI - An Artificial Neural Network for Hand Movement Classification using Surface Electromyography
SN - 978-989-758-353-7
AU - Viana, P.
AU - Fujii, V.
AU - Lima, L.
AU - Ouriques, G.
AU - Oliveira, G.
AU - Varoto, R.
AU - Cliquet Jr., A.
PY - 2019
SP - 185
EP - 192
DO - 10.5220/0007404201850192

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