On the use of high-order cumulant and bispectrum formuscular-activity detection

Autores
Orosco, Eugenio Conrado; Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Soria, Carlos Miguel; Di Sciascio, Fernando Agustin
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.
Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentina
Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentina
Fil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Materia
Emg
Higher-Order Statistics
Cumulants
Bispectrum
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/4900
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/4900
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling On the use of high-order cumulant and bispectrum formuscular-activity detectionOrosco, Eugenio ConradoDiez, Pablo FedericoLaciar Leber, EricMut, Vicente AntonioSoria, Carlos MiguelDi Sciascio, Fernando AgustinEmgHigher-Order StatisticsCumulantsBispectrumhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaFil: Di Sciascio, Fernando Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; ArgentinaElsevier2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4900Orosco, Eugenio Conrado; Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Soria, Carlos Miguel; et al.; On the use of high-order cumulant and bispectrum formuscular-activity detection; Elsevier; Biomedical Signal Processing And Control; 18; 4-2015; 325-3331746-8094enginfo:eu-repo/semantics/altIdentifier/url/10.1016/j.bspc.2015.02.011info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1746809415000233info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bspc.2015.02.011info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:27:05Zoai:ri.conicet.gov.ar:11336/4900instacron: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 15:27:05.606CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On the use of high-order cumulant and bispectrum formuscular-activity detection
title On the use of high-order cumulant and bispectrum formuscular-activity detection
spellingShingle On the use of high-order cumulant and bispectrum formuscular-activity detection
Orosco, Eugenio Conrado
Emg
Higher-Order Statistics
Cumulants
Bispectrum
title_short On the use of high-order cumulant and bispectrum formuscular-activity detection
title_full On the use of high-order cumulant and bispectrum formuscular-activity detection
title_fullStr On the use of high-order cumulant and bispectrum formuscular-activity detection
title_full_unstemmed On the use of high-order cumulant and bispectrum formuscular-activity detection
title_sort On the use of high-order cumulant and bispectrum formuscular-activity detection
dc.creator.none.fl_str_mv Orosco, Eugenio Conrado
Diez, Pablo Federico
Laciar Leber, Eric
Mut, Vicente Antonio
Soria, Carlos Miguel
Di Sciascio, Fernando Agustin
author Orosco, Eugenio Conrado
author_facet Orosco, Eugenio Conrado
Diez, Pablo Federico
Laciar Leber, Eric
Mut, Vicente Antonio
Soria, Carlos Miguel
Di Sciascio, Fernando Agustin
author_role author
author2 Diez, Pablo Federico
Laciar Leber, Eric
Mut, Vicente Antonio
Soria, Carlos Miguel
Di Sciascio, Fernando Agustin
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Emg
Higher-Order Statistics
Cumulants
Bispectrum
topic Emg
Higher-Order Statistics
Cumulants
Bispectrum
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 electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.
Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentina
Fil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentina
Fil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
Fil: Di Sciascio, Fernando Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina
description The electromyographic (EMG) signals are extensively used on feature extraction methods for movement classification purposes. High-Order Statistics (HOS) is being employed increasingly in myoelectric research. HOS techniques could be represented in the frequency domain (high-order spectra, e.g., bispectrum, trispectrum) or in the time domain (higher-order cumulants). More calculus is required for computing the HOS in the frequency domain. On the one hand, classical bispectrum based features were applied to EMG signals. On the other hand, we propose novel third-order cumulant-based features for EMG signals. Two different classifiers are implemented for muscular activity detection. Different analysis and evaluations were applied to both HOS-based features in order to qualify and quantify similarities. Based on these results, it is possible to conclude that cumulant based features and bispectrum-based features had comparable behavior and allowed similar classification rates. Hence, extra calculus in order to convert time- to frequency-domain should be avoided.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
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/11336/4900
Orosco, Eugenio Conrado; Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Soria, Carlos Miguel; et al.; On the use of high-order cumulant and bispectrum formuscular-activity detection; Elsevier; Biomedical Signal Processing And Control; 18; 4-2015; 325-333
1746-8094
url http://hdl.handle.net/11336/4900
identifier_str_mv Orosco, Eugenio Conrado; Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Soria, Carlos Miguel; et al.; On the use of high-order cumulant and bispectrum formuscular-activity detection; Elsevier; Biomedical Signal Processing And Control; 18; 4-2015; 325-333
1746-8094
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/10.1016/j.bspc.2015.02.011
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1746809415000233
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bspc.2015.02.011
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1846083413178580992
score 13.22299

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