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dc.contributor.author
Orosco, Eugenio Conrado
dc.contributor.author
López Celani, Natalia Martina
dc.contributor.author
Di Sciascio, Fernando Agustín
dc.date.available
2017-09-11T21:14:41Z
dc.date.issued
2013-03
dc.identifier.citation
Orosco, Eugenio Conrado; López Celani, Natalia Martina; Di Sciascio, Fernando Agustín; Bispectrum-based features classification for myoelectric control; Elsevier; Biomedical Signal Processing and Control; 8; 3; 3-2013; 153-168
dc.identifier.issn
1746-8094
dc.identifier.uri
http://hdl.handle.net/11336/23954
dc.description.abstract
Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Emg
dc.subject
Robust Bispectrum
dc.subject
Continuous Classification
dc.subject
Myoelectric Control
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Bispectrum-based features classification for myoelectric control
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-09-08T20:21:55Z
dc.journal.volume
8
dc.journal.number
3
dc.journal.pagination
153-168
dc.journal.pais
Países Bajos
dc.journal.ciudad
Ámsterdam
dc.description.fil
Fil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Biomedical Signal Processing and Control
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1746809412000900
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.bspc.2012.08.008
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