Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/27589
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dc.contributor.authorBrás, Susanapt_PT
dc.contributor.authorCarvalho, João M.pt_PT
dc.contributor.authorBarros, Filipapt_PT
dc.contributor.authorFigueiredo, Cláudiapt_PT
dc.contributor.authorSoares, Sandra C.pt_PT
dc.contributor.authorPinho, Armando J.pt_PT
dc.date.accessioned2020-02-18T18:36:17Z-
dc.date.available2020-02-18T18:36:17Z-
dc.date.issued2019-09-25-
dc.identifier.isbn978-3-030-31634-1pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/27589-
dc.description.abstractIdentifying the emotion that someone is feeling will allow to improve the experience of the person interaction with environments, devices, and contents. Our body responds to events around us, by emotional responses, reflected in cognitive, behavioral and physiological dimensions. In the present work, we target the electrocardiogram (ECG) response as a mean to express emotions. Its processing is performed using information-theoretical measures, allowing true exploratory data mining. Participants recruited for the experiment watched three video sets in three different days, with a different emotion being induced in each day: fear, happiness, and neutral condition. The method is divided in: (1) conversion of the real-valued ECG record into a symbolic time-series; (2) relative compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as a reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. An accuracy of 90% was obtained. A posteriori analysis of the false negative results indicated that there was a relation between the relative dissimilarity measure and the self-reported emotions.pt_PT
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147437/PTpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/5665-PICT/135791/PTpt_PT
dc.relationSFRH/BD/136815/2018pt_PT
dc.rightsrestrictedAccesspt_PT
dc.subjectEmotionpt_PT
dc.subjectAffective computingpt_PT
dc.subjectClassificationpt_PT
dc.subjectData compressionpt_PT
dc.subjectKolmogorov complexitypt_PT
dc.titleAn information-theoretical method for emotion classificationpt_PT
dc.typebookPartpt_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage253pt_PT
degois.publication.lastPage261pt_PT
degois.publication.locationChampt_PT
degois.publication.titleXV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019pt_PT
degois.publication.volume76-
dc.identifier.doi10.1007/978-3-030-31635-8_30pt_PT
dc.identifier.esbn978-3-030-31635-8pt_PT
Appears in Collections:IEETA - Capítulo de livro
GOVCOPP - Capitulo de livro
CINTESIS - Capítulo de livro
WJCR - Capítulo de livro

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