Optimization of features to classify upper-limb movements through sEMG signal processing
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/166207 |
Resumo: | This paper presents the development of a computational intelligence method based on Regularized Logistic Regression to classify 17 distinct upper-limb movements through surface electromyography (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 Root Mean Square (RMS), Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameters 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 defined the most proper features combination, the accuracy rate reached 87,1%, raising the rates of all movements accuracies performed for all databases. |
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Cene, Vinicius HornBalbinot, Alexandre2017-09-07T02:36:04Z20162318-4531http://hdl.handle.net/10183/166207001046968This paper presents the development of a computational intelligence method based on Regularized Logistic Regression to classify 17 distinct upper-limb movements through surface electromyography (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 Root Mean Square (RMS), Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameters 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 defined the most proper features combination, the accuracy rate reached 87,1%, raising the rates of all movements accuracies performed for all databases.application/pdfengBrazilian journal of instrumentation and control [recurso eletrônico] = Revista brasileira de instrumentação e controle. Curitiba. Vol. 4, n. 1 (2016), p. 14-20Regressão logísticaExtremidade superiorProcessamento de sinaisEletromiografiasEMGUpper-limbLogistic regressionFeature selectionChannel variationAccuracy rateOptimization of features to classify upper-limb movements through sEMG signal processinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL001046968.pdf001046968.pdfTexto completo (inglês)application/pdf929807http://www.lume.ufrgs.br/bitstream/10183/166207/1/001046968.pdf3d2db43c3c53649c3e6a9a801c8cfeaaMD51TEXT001046968.pdf.txt001046968.pdf.txtExtracted Texttext/plain32310http://www.lume.ufrgs.br/bitstream/10183/166207/2/001046968.pdf.txt1f8e11f1eabdf51799de773acf3b5ecfMD52THUMBNAIL001046968.pdf.jpg001046968.pdf.jpgGenerated Thumbnailimage/jpeg2223http://www.lume.ufrgs.br/bitstream/10183/166207/3/001046968.pdf.jpgf7f7a9cad4d9e17ee12099fd6982f8c3MD5310183/1662072018-10-23 09:22:16.178oai:www.lume.ufrgs.br:10183/166207Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2018-10-23T12:22:16Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Optimization of features to classify upper-limb movements through sEMG signal processing |
title |
Optimization of features to classify upper-limb movements through sEMG signal processing |
spellingShingle |
Optimization of features to classify upper-limb movements through sEMG signal processing Cene, Vinicius Horn Regressão logística Extremidade superior Processamento de sinais Eletromiografia sEMG Upper-limb Logistic regression Feature selection Channel variation Accuracy rate |
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 |
author |
Cene, Vinicius Horn |
author_facet |
Cene, Vinicius Horn Balbinot, Alexandre |
author_role |
author |
author2 |
Balbinot, Alexandre |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Cene, Vinicius Horn Balbinot, Alexandre |
dc.subject.por.fl_str_mv |
Regressão logística Extremidade superior Processamento de sinais Eletromiografia |
topic |
Regressão logística Extremidade superior Processamento de sinais Eletromiografia sEMG Upper-limb Logistic regression Feature selection Channel variation Accuracy rate |
dc.subject.eng.fl_str_mv |
sEMG Upper-limb Logistic regression Feature selection Channel variation Accuracy rate |
description |
This paper presents the development of a computational intelligence method based on Regularized Logistic Regression to classify 17 distinct upper-limb movements through surface electromyography (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 Root Mean Square (RMS), Variance and Medium Frequency features with which we proposed to achieve a most proper combination of parameters 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 defined the most proper features combination, the accuracy rate reached 87,1%, raising the rates of all movements accuracies performed for all databases. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2017-09-07T02:36:04Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/166207 |
dc.identifier.issn.pt_BR.fl_str_mv |
2318-4531 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001046968 |
identifier_str_mv |
2318-4531 001046968 |
url |
http://hdl.handle.net/10183/166207 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Brazilian journal of instrumentation and control [recurso eletrônico] = Revista brasileira de instrumentação e controle. Curitiba. Vol. 4, n. 1 (2016), p. 14-20 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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