sEMG feature evaluation for identification of elbow angle resolution in graded arm movement

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dc.contributor.authorCASTRO, M. C. F.
dc.contributor.authorCOLOMBINI, E. L.
dc.contributor.authorAQUINO JUNIOR, Plinio Thomaz
dc.contributor.authorARJUNAN, S. P.
dc.contributor.authorKUMAR, D. K.
dc.date.accessioned2019-08-17T20:00:29Z
dc.date.available2019-08-17T20:00:29Z
dc.date.issued2014
dc.description.abstractalternativeAutomatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system. This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.en
dc.description.firstpage155
dc.description.issuenumber1
dc.description.volume13
dc.identifier.citationCASTRO, M. C.; COLOMBINI, E. L.; Aquino Junior, Plinio Thomaz; ARJUNAN, S. P.; KUMAR, D. K. sEMG feature evaluation for identification of elbow angle resolution in graded arm movement. Biomedical Engineering Online (Online), v. 13, n. 1, p. 155, 2014.
dc.identifier.doi10.1186/1475-925x-13-155
dc.identifier.issn1475-925X
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/991
dc.identifier.urlhttps://doi.org/10.1186/1475-925X-13-155
dc.relation.ispartofBioMedical Engineering OnLine
dc.rightsAcesso Aberto
dc.rights.license"Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY 4.0 e CC0 1.0 Universal Public Domain Dedication). Fonte: <https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-13-155>. Acesso em: 28 out. 2019.
dc.subject.otherlanguageEMG signalen
dc.subject.otherlanguagePattern recognitionen
dc.subject.otherlanguageFeature extractionen
dc.subject.otherlanguageAngular positionen
dc.subject.otherlanguageArm flexionen
dc.subject.otherlanguageArm extensionen
dc.titlesEMG feature evaluation for identification of elbow angle resolution in graded arm movementpt_BR
dc.typeArtigopt_BR
fei.scopus.citations7
fei.scopus.eid2-s2.0-84924418234
fei.scopus.updated2024-12-01
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