Show simple item record

dc.contributor.authorBoeker, Matthias
dc.contributor.authorJakobsen, Petter
dc.contributor.authorRiegler, Michael
dc.contributor.authorStabell, Lena Antonsen
dc.contributor.authorFasmer, Ole Bernt
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorHammer, Hugo Lewi
dc.date.accessioned2023-10-26T05:23:26Z
dc.date.available2023-10-26T05:23:26Z
dc.date.created2023-08-23T14:38:08Z
dc.date.issued2023
dc.identifier.citationIEEE Access. 2023, 11 61914-61928.en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11250/3098821
dc.description.abstractThis study investigated the potential of recognising arousal in motor activity collected by wrist- worn accelerometers. We hypothesise that emotional arousal emerges from the generalised central nervous system which embeds affective states within motor activity. We formulate arousal detection as a statistical problem of separating two sets - motor activity under emotional arousal and motor activity without arousal. We propose a novel test regime based on machine learning assuming that the two sets can be distinguished if a machine learning classifier can separate the sets better than random guessing. To increase the statistical power of the testing regime, the performance of the classifiers is evaluated in a cross-validation framework, and to test if the classifiers perform better than random guessing, a repeated cross-validation corrected t-test is used. The classifiers were evaluated on the basis of accuracy and Matthew’s correlation coefficient. The suggested procedures were further compared against a traditional multivariate paired Hotelling’s T-squared test. The classifiers achieved an accuracy of about 60%, and according to the proposed t-test were significantly better than random guessing. The suggested test regime demonstrated higher statistical power than Hotelling’s T-squared test, and we conclude that we can distinguish between motor activity under emotional arousal and without it.en_US
dc.language.isoengen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleAffect Recognition in Muscular Response Signalsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1109/ACCESS.2023.3279720
dc.identifier.cristin2169082
dc.source.journalIEEE Accessen_US
dc.source.volume11en_US
dc.source.pagenumber61914-61928en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal