Mostra el registre parcial de l'element

dc.contributor.authorNolazco Flores, Juan Arturo
dc.contributor.authorFaundez-Zanuy, Marcos
dc.contributor.authorVelázquez-Flores, O. A.
dc.contributor.authorDel-Valle-Soto, Carolina
dc.contributor.authorCordasco, Gennaro
dc.contributor.authorEsposito, Anna
dc.contributor.otherTecnoCampus. Escola Superior Politècnica (ESUPT)
dc.date.accessioned2023-06-22T11:35:37Z
dc.date.available2023-06-22T11:35:37Z
dc.date.issued2022 Feb 21
dc.identifier.citationNolazco Flores JA, Faundez-Zanuy M, Velázquez-Flores OA, Del-Valle-Soto C, Cordasco G, Esposito A. Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning. Sensors. 2022 Feb 21;22(4):1686. DOI: 10.3390/ s22041686ca
dc.identifier.issn1424-8220ca
dc.identifier.urihttp://hdl.handle.net/20.500.12367/2198
dc.description.abstractIn this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen's position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation-based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size [...].ca
dc.format.extent22 p.ca
dc.language.isoengca
dc.publisherMDPIca
dc.relation.ispartofSensors. 2022 Feb 21;22(4):1686ca
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ca
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherAutoMLca
dc.subject.otherData augmentationca
dc.subject.otherNegative mood states recognitionca
dc.subject.otherFeature extractionca
dc.subject.otherSVMca
dc.titleMood state detection in handwritten tasks using PCA–mFCBF and automated machine learningca
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.doi10.3390/s22041686ca


Fitxers en aquest element

 

Aquest element apareix en la col·lecció o col·leccions següent(s)

Mostra el registre parcial de l'element

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Excepte que s'indiqui una altra cosa, la llicència de l'ítem es descriu com http://creativecommons.org/licenses/by/4.0/
Comparteix a TwitterComparteix a LinkedinComparteix a FacebookComparteix a TelegramComparteix a WhatsappImprimeix