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dc.contributor.authorNolazco Flores, Juan Arturo
dc.contributor.authorFaundez-Zanuy, Marcos
dc.contributor.authorVelázquez-Flores, O. A.
dc.contributor.authorCordasco, Gennaro
dc.contributor.authorEsposito, Anna
dc.contributor.otherTecnoCampus. Escola Superior Politècnica (ESUPT)ca
dc.date.accessioned2023-02-23T13:22:57Z
dc.date.available2023-02-23T13:22:57Z
dc.date.issued2021-02-21
dc.identifier.citationNolazco Flores JA, Faundez-Zanuy M, Velázquez-Flores OA, Cordasco G, Esposito A. Emotional state recognition performance improvement on a handwriting and drawing task. IEEE Access. 2021 Feb 23;(9):28496-28504. DOI: 10.1109/ACCESS.2021.3058443
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/20.500.12367/2195
dc.description.abstractIn this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstral-domain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank [...].ca
dc.format.extent9 p.ca
dc.language.isoengca
dc.publisherIEEEca
dc.relation.ispartofIEEE Access. 2021 Feb 23;(9):28496-28504
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherData augmentationca
dc.subject.otherEmotional state recognition
dc.subject.otherEmotional states
dc.subject.otherFeature extraction
dc.subject.otherSVM
dc.titleEmotional state recognition performance improvement on a handwriting and drawing taskca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.identifier.doi10.1109/ACCESS.2021.3058443ca


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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/
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