Estimating and minimizing movement artifacts in surface electromyogram

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dc.authoridkaracan, ilhan/0000-0002-7462-1358
dc.authoridTopkara Arslan, Betilay/0000-0002-3509-9296
dc.contributor.authorKaracan, Ilhan
dc.contributor.authorArslan, Betilay Topkara
dc.contributor.authorKaraoglu, Ayse
dc.contributor.authorAydin, Tugba
dc.contributor.authorGray, Simon
dc.contributor.authorUngan, Pekcan
dc.contributor.authorTurker, Kemal S.
dc.date.accessioned2024-09-11T19:51:05Z
dc.date.available2024-09-11T19:51:05Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractWhile recording surface electromyography [sEMG], it is possible to record the electrical activities coming from the muscles and transients in the half-cell potential at the electrode-electrolyte interface due to micromovements of the electrode-skin interface. Separating the two sources of electrical activity usually fails due to the over-lapping frequency characteristics of the signals. This paper aims to develop a method that detects movement artifacts and suggests a minimization technique. Towards that aim, we first estimated the frequency charac-teristics of movement artifacts under various static and dynamic experimental conditions. We found that the extent of the movement artifact depended on the nature of the movement and varied from person to person. Our study's highest movement artifact frequency for the stand position was 10 Hz, tiptoe 22, walk 32, run 23, jump from box 41, and jump up and down 40 Hz. Secondly, using a 40 Hz highpass filter, we cut out most of the frequencies belonging to the movement artifacts. Finally, we checked whether the latencies and amplitudes of reflex and direct muscle responses were still observed in the highpass-filtered sEMG. We showed that the 40 Hz highpass filter did not significantly alter reflex and direct muscle variables. Therefore, we recommend that re-searchers who use sEMG under similar conditions employ the recommended level of highpass filtering to reduce movement artifacts from their records. However, suppose different movement conditions are used. In that case, it is best to estimate the frequency characteristics of the movement artifact before applying any highpass filtering to minimize movement artifacts and their harmonics from sEMG.en_US
dc.identifier.doi10.1016/j.jelekin.2023.102778
dc.identifier.issn1050-6411
dc.identifier.issn1873-5711
dc.identifier.pmid37141730en_US
dc.identifier.scopus2-s2.0-85153928361en_US
dc.identifier.urihttps://doi.org/10.1016/j.jelekin.2023.102778
dc.identifier.urihttps://hdl.handle.net/11363/7733
dc.identifier.volume70en_US
dc.identifier.wosWOS:000999815500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofJournal of Electromyography And Kinesiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectMovement Artifacten_US
dc.subjectSurface Electromyogramen_US
dc.subjectDynamic Conditionsen_US
dc.titleEstimating and minimizing movement artifacts in surface electromyogramen_US
dc.typeArticleen_US

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