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dc.contributor.author
Orosco, Eugenio Conrado
dc.contributor.author
Di Sciascio, Fernando Agustín
dc.date.available
2018-11-02T19:54:36Z
dc.date.issued
2017-10
dc.identifier.citation
Orosco, Eugenio Conrado; Di Sciascio, Fernando Agustín; Muscular synergy classification and myoelectric control using high-order cross-cumulants; Springer; Neural Computing And Applications; 28; 10; 10-2017; 2979-2993
dc.identifier.issn
0941-0643
dc.identifier.uri
http://hdl.handle.net/11336/63543
dc.description.abstract
High-order statistics (HOS) are well suited for describing non-Gaussian random processes. These techniques are increasingly being employed in myoelectric research, on both time and frequency domain techniques. This work presents HOS-based techniques using only HOS time domain features to classify myoelectric signals. The auto-, cross- and full- (joint) third-order cumulants are evaluated as EMG-signal feature vectors to be compared between them. Four surface EMG signals were processed for classify motions from the upper limbs. Synergy among channels is characterized by the features in both auto and cross modes, and their incidences for classifying five or six movements are analyzed. In contrast to the third-order auto-cumulants, it had been verified that the third-order cross-cumulants have the same classification rate by working with five or six movements. A myoelectric control scheme and its experimental application were executed with normal and disabled subjects, reaching a classification rates of 90%, in average. Accuracy in online experiments was similar to the off-line classification rate.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Cross-Cumulants
dc.subject
Hos
dc.subject
Muscular Synergy
dc.subject
Myoelectric Control
dc.subject
Semg
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
Muscular synergy classification and myoelectric control using high-order cross-cumulants
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-10-22T17:25:03Z
dc.identifier.eissn
1433-3058
dc.journal.volume
28
dc.journal.number
10
dc.journal.pagination
2979-2993
dc.journal.pais
Alemania
dc.journal.ciudad
Berlin
dc.description.fil
Fil: Orosco, Eugenio Conrado. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
dc.description.fil
Fil: Di Sciascio, Fernando Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
dc.journal.title
Neural Computing And Applications
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s00521-017-2927-6
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs00521-017-2927-6
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