A novel spatial feature for the identification of motor tasks using high-density electromyography

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/o...

Descripción completa

Detalles Bibliográficos
Autores: Jordanic, Mislav|||0000-0001-6831-3327, Rojas Martínez, Mónica, Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083, Alonso López, Joan Francesc|||0000-0002-2980-6716, Marateb, Hamid Reza|||0000-0003-4408-2397
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/111932
Acceso en línea:https://hdl.handle.net/2117/111932
https://dx.doi.org/10.3390/s17071597
Access Level:acceso abierto
Palabra clave:Electromyography
Biomechanics
high-density electromyography
pattern recognition
myoelectric control
mean shift
prosthetics
Electromiografia
Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
id ES_df9cb502d6447bfc11ad17d005dfa5af
oai_identifier_str oai:upcommons.upc.edu:2117/111932
network_acronym_str ES
network_name_str España
repository_id_str
spelling A novel spatial feature for the identification of motor tasks using high-density electromyographyJordanic, Mislav|||0000-0001-6831-3327Rojas Martínez, MónicaMañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083Alonso López, Joan Francesc|||0000-0002-2980-6716Marateb, Hamid Reza|||0000-0003-4408-2397ElectromyographyBiomechanicshigh-density electromyographypattern recognitionmyoelectric controlmean shiftprostheticsElectromiografiaBiomecànicaÀrees temàtiques de la UPC::Enginyeria biomèdica::BiomecànicaÀrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::BiosensorsEstimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.Peer ReviewedMDPI AG20172017-07-0820172017-12-13journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/111932https://dx.doi.org/10.3390/s1707159728698474reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-59049-R DISEÑO DE METODOS PARA LA EVALUACION DE PROCESOS DE DETERIORO NEUROLOGICO Y NEUROMUSCULAR ASOCIADOS AL ENVEJECIMIENTOEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 600388 ACC10 programme to foster mobility of researchers with a focus in applied research and technology transferopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1119322026-05-27T15:37:01Z
dc.title.none.fl_str_mv A novel spatial feature for the identification of motor tasks using high-density electromyography
title A novel spatial feature for the identification of motor tasks using high-density electromyography
spellingShingle A novel spatial feature for the identification of motor tasks using high-density electromyography
Jordanic, Mislav|||0000-0001-6831-3327
Electromyography
Biomechanics
high-density electromyography
pattern recognition
myoelectric control
mean shift
prosthetics
Electromiografia
Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
title_short A novel spatial feature for the identification of motor tasks using high-density electromyography
title_full A novel spatial feature for the identification of motor tasks using high-density electromyography
title_fullStr A novel spatial feature for the identification of motor tasks using high-density electromyography
title_full_unstemmed A novel spatial feature for the identification of motor tasks using high-density electromyography
title_sort A novel spatial feature for the identification of motor tasks using high-density electromyography
dc.creator.none.fl_str_mv Jordanic, Mislav|||0000-0001-6831-3327
Rojas Martínez, Mónica
Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Alonso López, Joan Francesc|||0000-0002-2980-6716
Marateb, Hamid Reza|||0000-0003-4408-2397
author Jordanic, Mislav|||0000-0001-6831-3327
author_facet Jordanic, Mislav|||0000-0001-6831-3327
Rojas Martínez, Mónica
Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Alonso López, Joan Francesc|||0000-0002-2980-6716
Marateb, Hamid Reza|||0000-0003-4408-2397
author_role author
author2 Rojas Martínez, Mónica
Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Alonso López, Joan Francesc|||0000-0002-2980-6716
Marateb, Hamid Reza|||0000-0003-4408-2397
author2_role author
author
author
author
dc.subject.none.fl_str_mv Electromyography
Biomechanics
high-density electromyography
pattern recognition
myoelectric control
mean shift
prosthetics
Electromiografia
Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
topic Electromyography
Biomechanics
high-density electromyography
pattern recognition
myoelectric control
mean shift
prosthetics
Electromiografia
Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
description Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-07-08
2017
2017-12-13
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/111932
https://dx.doi.org/10.3390/s17071597
28698474
url https://hdl.handle.net/2117/111932
https://dx.doi.org/10.3390/s17071597
identifier_str_mv 28698474
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 DPI2014-59049-R DISEÑO DE METODOS PARA LA EVALUACION DE PROCESOS DE DETERIORO NEUROLOGICO Y NEUROMUSCULAR ASOCIADOS AL ENVEJECIMIENTO
European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 600388 ACC10 programme to foster mobility of researchers with a focus in applied research and technology transfer
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422079624871936
score 15,300719

Similares en LA Referencia