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AuthorHrabal, Daviddc.contributor.author
Date of accession2016-03-15T09:08:29Zdc.date.accessioned
Available in OPARU since2016-03-15T09:08:29Zdc.date.available
Year of creation2013dc.date.created
AbstractThis work addresses the still unsolved problem of stimulus- and subject-independent emotion identification from physiological data and presents, as a solution, a novel method for identification of affective changes from physiological data in the two dimensions of pleasure and arousal of the PAD (Pleasure-Arousal-Dominance) model. In our experiment, 110 participants passively viewed blocked affective pictures and then actively engaged in a mental training Wizard-of-Oz scenario while their physiological activity was recorded. In order to allow stimulus- and subject-independent testing, the group of subjects was split in two halfs. The first group’s IAPS picture viewing data was segmented into high pleasure, low pleasure, high arousal and low arousal physiological data and six ‘meaningful’ features were extracted from four physiological channels (EMG corrugator supercilii, EMG zygomaticus major, skin conductance, and blood volume pulse). These were used for creation of Emotion Identification Modules which were then applied on the second group’s affective picture viewing data for subject-independent emotion identification as well as on the first group’s Wizard-of-Oz scenario data for stimulus-independent emotion identification. With correct recognition rates of around 75 % (chance-level: 50 %) this method is a step towards a more accurate and robust stimulus- and subject-independent emotion identification.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordEmotion classificationdc.subject
Dewey Decimal GroupDDC 610 / Medicine & healthdc.subject.ddc
LCSHHuman-computer interactiondc.subject.lcsh
TitleEmotion classification in human-computer-interaction on the basis of physiological datadc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-3001dc.identifier.doi
PPN782352936dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-87747dc.identifier.urn
GNDAffective Computingdc.subject.gnd
GNDGefühlstheoriedc.subject.gnd
GNDMensch-Maschine-Kommunikationdc.subject.gnd
FacultyMedizinische Fakultätuulm.affiliationGeneral
Date of activation2014-04-01T15:31:30Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionW: W-H 13.602uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS-ID8774uulm.vtsID
CategoryPublikationenuulm.category
University Bibliographyjauulm.unibibliographie


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