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Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing

This paper aims to present the development of a computational intelligence method based on Regularized Logistic Regression able to classify 17 distinguish upper-limb movements through the sEMG signal processing. The choose of the tuning parameters of the regularization and the generation of the diff...

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Principais autores: Cene, Vinicius Horn, Balbinot, Alexandre
Formato: Artigo
Idioma: Inglês
Publicado em: Universidade Tecnológica Federal do Paraná (UTFPR) 2016
Acesso em linha: http://periodicos.utfpr.edu.br/bjic/article/view/4878
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spelling peri-article-48782017-09-20T14:52:16Z Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing Cene, Vinicius Horn Balbinot, Alexandre Processamento de Sinais Biológicos sEMG; upper-limb; logistic regression; feature selection; channel variation; accuracy rate This paper aims to present the development of a computational intelligence method based on Regularized Logistic Regression able to classify 17 distinguish upper-limb movements through the sEMG signal processing. The choose of the tuning parameters of the regularization and the generation of the different classification methods are presented. For the different models were used variations involving 12 sEMG channels and the RMS, Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameter to perform the movements classification. The tests involved 50 subjects, including 10 amputees, using the NinaPro database and also a database currently on development by the authors. The global mean accuracy rate considering all the subjects and the channel and features variations was 70,2% prior the definition of the best case scenario. Once we defined the most proper channel and features combination, we were able to improve the accuracy rate to 87,1%, raising the rates of all movements performed for all databases. Universidade Tecnológica Federal do Paraná (UTFPR) CAPES 2016-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf http://periodicos.utfpr.edu.br/bjic/article/view/4878 10.3895/bjic.v4n1.4878 Journal of Applied Instrumentation and Control; v. 4, n. 1 (2016); 14-20 Journal of Applied Instrumentation and Control; v. 4, n. 1 (2016); 14-20 2594-3553 10.3895/bjic.v4n1 eng http://periodicos.utfpr.edu.br/bjic/article/view/4878/3218 Direitos autorais 2016 CC-BY http://creativecommons.org/licenses/by/4.0
institution Universidade Tecnológica Federal do Paraná
collection PERI
language Inglês
format Artigo
author Cene, Vinicius Horn
Balbinot, Alexandre
spellingShingle Cene, Vinicius Horn
Balbinot, Alexandre
Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
author_sort Cene, Vinicius Horn
title Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
title_short Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
title_full Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
title_fullStr Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
title_full_unstemmed Optimization of Features to Classify Upper-Limb Movements Through sEMG Signal Processing
title_sort optimization of features to classify upper-limb movements through semg signal processing
description This paper aims to present the development of a computational intelligence method based on Regularized Logistic Regression able to classify 17 distinguish upper-limb movements through the sEMG signal processing. The choose of the tuning parameters of the regularization and the generation of the different classification methods are presented. For the different models were used variations involving 12 sEMG channels and the RMS, Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameter to perform the movements classification. The tests involved 50 subjects, including 10 amputees, using the NinaPro database and also a database currently on development by the authors. The global mean accuracy rate considering all the subjects and the channel and features variations was 70,2% prior the definition of the best case scenario. Once we defined the most proper channel and features combination, we were able to improve the accuracy rate to 87,1%, raising the rates of all movements performed for all databases.
publisher Universidade Tecnológica Federal do Paraná (UTFPR)
publishDate 2016
url http://periodicos.utfpr.edu.br/bjic/article/view/4878
_version_ 1805292611449126912
score 10,814766

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