A neuro-fuzzy system for characterization of arm movements
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/267683 |
Resumo: | The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours). |
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Balbinot, AlexandreFavieiro, Gabriela Winkler2023-11-25T03:27:55Z20131424-8220http://hdl.handle.net/10183/267683000876159The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours).application/pdfengSensors [recurso eletrônico]. Basel. Vol. 13, n. 2 (Feb. 2013), p. 2613-2630EletromiografiaTecnologia assistivaProcessamento de sinaisBiomedical instrumentationSurface electromyographyArm movementsNeuro-fuzzy systemA neuro-fuzzy system for characterization of arm movementsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000876159.pdf.txt000876159.pdf.txtExtracted Texttext/plain47780http://www.lume.ufrgs.br/bitstream/10183/267683/2/000876159.pdf.txta04bd4ff9ece6f9c35b59aa4e071366cMD52ORIGINAL000876159.pdfTexto completo (inglês)application/pdf722372http://www.lume.ufrgs.br/bitstream/10183/267683/1/000876159.pdf7a14a013c7192de422e7295bef7f0edfMD5110183/2676832023-12-06 04:25:33.221204oai:www.lume.ufrgs.br:10183/267683Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-12-06T06:25:33Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
A neuro-fuzzy system for characterization of arm movements |
title |
A neuro-fuzzy system for characterization of arm movements |
spellingShingle |
A neuro-fuzzy system for characterization of arm movements Balbinot, Alexandre Eletromiografia Tecnologia assistiva Processamento de sinais Biomedical instrumentation Surface electromyography Arm movements Neuro-fuzzy system |
title_short |
A neuro-fuzzy system for characterization of arm movements |
title_full |
A neuro-fuzzy system for characterization of arm movements |
title_fullStr |
A neuro-fuzzy system for characterization of arm movements |
title_full_unstemmed |
A neuro-fuzzy system for characterization of arm movements |
title_sort |
A neuro-fuzzy system for characterization of arm movements |
author |
Balbinot, Alexandre |
author_facet |
Balbinot, Alexandre Favieiro, Gabriela Winkler |
author_role |
author |
author2 |
Favieiro, Gabriela Winkler |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Balbinot, Alexandre Favieiro, Gabriela Winkler |
dc.subject.por.fl_str_mv |
Eletromiografia Tecnologia assistiva Processamento de sinais |
topic |
Eletromiografia Tecnologia assistiva Processamento de sinais Biomedical instrumentation Surface electromyography Arm movements Neuro-fuzzy system |
dc.subject.eng.fl_str_mv |
Biomedical instrumentation Surface electromyography Arm movements Neuro-fuzzy system |
description |
The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours). |
publishDate |
2013 |
dc.date.issued.fl_str_mv |
2013 |
dc.date.accessioned.fl_str_mv |
2023-11-25T03:27:55Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
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/10183/267683 |
dc.identifier.issn.pt_BR.fl_str_mv |
1424-8220 |
dc.identifier.nrb.pt_BR.fl_str_mv |
000876159 |
identifier_str_mv |
1424-8220 000876159 |
url |
http://hdl.handle.net/10183/267683 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Sensors [recurso eletrônico]. Basel. Vol. 13, n. 2 (Feb. 2013), p. 2613-2630 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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