Recognition of Movements Through Dynamic Electromyographic Signals

Autores
Farfan, Fernando Daniel; Soletta, Jorge Humberto; Ruiz, Gabriel Alfredo; Felice, Carmelo Jose
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The recognition of movements through electromyographic (EMG) signals is critical for myoelectric control systems. Performance of these systems depend on processing methods and protocols used to extract the EMG signals. The aim of this study is to evaluate the performance of classification of a kinematic recognition system based on dynamic EMG signals. For this, a correlation analysis between dynamic EMG signals and kinematic features of movements is realized, and then, a kinematic recognition system based on dynamic EMG signals is implemented. Dynamic EMG signals from forearm muscles during finger flexion movements were recorded and analyzed by using an amplitude estimator. Linear and no-linear correlations between EMG amplitudes and kinematic features were found. Then, a step of classification based on discriminant analysis was implemented to categorize the finger movements in multiple kinematic states. The accuracy of classifications were 95%, 88%, 81% and 76% for two, three, four and five states respectively, and by using a simple-channel recording and an EMG amplitude estimator. The results of this study demonstrate that it is possible to improve aspects of “intuitiveness” through dynamic EMG evoked by natural and more intuitive movements.
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Soletta, Jorge Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Ruiz, Gabriel Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Materia
EMG
Root mean square
Kinematic features
Discriminant analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/51800
Acceder
id CONICETDig_9c91ed29f06f3c634839fdeff2496422
oai_identifier_str oai:ri.conicet.gov.ar:11336/51800
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Recognition of Movements Through Dynamic Electromyographic SignalsFarfan, Fernando DanielSoletta, Jorge HumbertoRuiz, Gabriel AlfredoFelice, Carmelo JoseEMGRoot mean squareKinematic featuresDiscriminant analysishttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The recognition of movements through electromyographic (EMG) signals is critical for myoelectric control systems. Performance of these systems depend on processing methods and protocols used to extract the EMG signals. The aim of this study is to evaluate the performance of classification of a kinematic recognition system based on dynamic EMG signals. For this, a correlation analysis between dynamic EMG signals and kinematic features of movements is realized, and then, a kinematic recognition system based on dynamic EMG signals is implemented. Dynamic EMG signals from forearm muscles during finger flexion movements were recorded and analyzed by using an amplitude estimator. Linear and no-linear correlations between EMG amplitudes and kinematic features were found. Then, a step of classification based on discriminant analysis was implemented to categorize the finger movements in multiple kinematic states. The accuracy of classifications were 95%, 88%, 81% and 76% for two, three, four and five states respectively, and by using a simple-channel recording and an EMG amplitude estimator. The results of this study demonstrate that it is possible to improve aspects of “intuitiveness” through dynamic EMG evoked by natural and more intuitive movements.Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Soletta, Jorge Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Ruiz, Gabriel Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaFil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; ArgentinaESRSA Publications2016-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/51800Farfan, Fernando Daniel; Soletta, Jorge Humberto; Ruiz, Gabriel Alfredo; Felice, Carmelo Jose; Recognition of Movements Through Dynamic Electromyographic Signals; ESRSA Publications; International Journal of Engineering Research and Technology; 5; 2; 2-2016; 450-4572278-0181CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.ijert.org/browse/volume-5-2016/february-2016-edition?start=80info:eu-repo/semantics/altIdentifier/doi/10.17577/IJERTV5IS020404info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:03:29Zoai:ri.conicet.gov.ar:11336/51800instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:03:29.754CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Recognition of Movements Through Dynamic Electromyographic Signals
title Recognition of Movements Through Dynamic Electromyographic Signals
spellingShingle Recognition of Movements Through Dynamic Electromyographic Signals
Farfan, Fernando Daniel
EMG
Root mean square
Kinematic features
Discriminant analysis
title_short Recognition of Movements Through Dynamic Electromyographic Signals
title_full Recognition of Movements Through Dynamic Electromyographic Signals
title_fullStr Recognition of Movements Through Dynamic Electromyographic Signals
title_full_unstemmed Recognition of Movements Through Dynamic Electromyographic Signals
title_sort Recognition of Movements Through Dynamic Electromyographic Signals
dc.creator.none.fl_str_mv Farfan, Fernando Daniel
Soletta, Jorge Humberto
Ruiz, Gabriel Alfredo
Felice, Carmelo Jose
author Farfan, Fernando Daniel
author_facet Farfan, Fernando Daniel
Soletta, Jorge Humberto
Ruiz, Gabriel Alfredo
Felice, Carmelo Jose
author_role author
author2 Soletta, Jorge Humberto
Ruiz, Gabriel Alfredo
Felice, Carmelo Jose
author2_role author
author
author
dc.subject.none.fl_str_mv EMG
Root mean square
Kinematic features
Discriminant analysis
topic EMG
Root mean square
Kinematic features
Discriminant analysis
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The recognition of movements through electromyographic (EMG) signals is critical for myoelectric control systems. Performance of these systems depend on processing methods and protocols used to extract the EMG signals. The aim of this study is to evaluate the performance of classification of a kinematic recognition system based on dynamic EMG signals. For this, a correlation analysis between dynamic EMG signals and kinematic features of movements is realized, and then, a kinematic recognition system based on dynamic EMG signals is implemented. Dynamic EMG signals from forearm muscles during finger flexion movements were recorded and analyzed by using an amplitude estimator. Linear and no-linear correlations between EMG amplitudes and kinematic features were found. Then, a step of classification based on discriminant analysis was implemented to categorize the finger movements in multiple kinematic states. The accuracy of classifications were 95%, 88%, 81% and 76% for two, three, four and five states respectively, and by using a simple-channel recording and an EMG amplitude estimator. The results of this study demonstrate that it is possible to improve aspects of “intuitiveness” through dynamic EMG evoked by natural and more intuitive movements.
Fil: Farfan, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Soletta, Jorge Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Ruiz, Gabriel Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
Fil: Felice, Carmelo Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto Superior de Investigaciones Biológicas. Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Departamento de Bioingeniería. Laboratorio de Medios e Interfases; Argentina
description The recognition of movements through electromyographic (EMG) signals is critical for myoelectric control systems. Performance of these systems depend on processing methods and protocols used to extract the EMG signals. The aim of this study is to evaluate the performance of classification of a kinematic recognition system based on dynamic EMG signals. For this, a correlation analysis between dynamic EMG signals and kinematic features of movements is realized, and then, a kinematic recognition system based on dynamic EMG signals is implemented. Dynamic EMG signals from forearm muscles during finger flexion movements were recorded and analyzed by using an amplitude estimator. Linear and no-linear correlations between EMG amplitudes and kinematic features were found. Then, a step of classification based on discriminant analysis was implemented to categorize the finger movements in multiple kinematic states. The accuracy of classifications were 95%, 88%, 81% and 76% for two, three, four and five states respectively, and by using a simple-channel recording and an EMG amplitude estimator. The results of this study demonstrate that it is possible to improve aspects of “intuitiveness” through dynamic EMG evoked by natural and more intuitive movements.
publishDate 2016
dc.date.none.fl_str_mv 2016-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/51800
Farfan, Fernando Daniel; Soletta, Jorge Humberto; Ruiz, Gabriel Alfredo; Felice, Carmelo Jose; Recognition of Movements Through Dynamic Electromyographic Signals; ESRSA Publications; International Journal of Engineering Research and Technology; 5; 2; 2-2016; 450-457
2278-0181
CONICET Digital
CONICET
url http://hdl.handle.net/11336/51800
identifier_str_mv Farfan, Fernando Daniel; Soletta, Jorge Humberto; Ruiz, Gabriel Alfredo; Felice, Carmelo Jose; Recognition of Movements Through Dynamic Electromyographic Signals; ESRSA Publications; International Journal of Engineering Research and Technology; 5; 2; 2-2016; 450-457
2278-0181
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.ijert.org/browse/volume-5-2016/february-2016-edition?start=80
info:eu-repo/semantics/altIdentifier/doi/10.17577/IJERTV5IS020404
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv ESRSA Publications
publisher.none.fl_str_mv ESRSA Publications
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846083177737617408
score 13.22299

Publicaciones similares