Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning

In 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 hori...

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Detalles Bibliográficos
Autores: Nolazco Flores, Juan Arturo, Faundez-Zanuy, Marcos, Velázquez-Flores, O. A., Del-Valle-Soto, Carolina, Cordasco, Gennaro, Esposito, Anna
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:20.500.12367/2198
Acceso en línea:https://hdl.handle.net/20.500.12367/2198
Access Level:acceso abierto
Palabra clave:AutoML
Data augmentation
Negative mood states recognition
Feature extraction
SVM
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repository_id_str
spelling Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learningNolazco Flores, Juan ArturoFaundez-Zanuy, MarcosVelázquez-Flores, O. A.Del-Valle-Soto, CarolinaCordasco, GennaroEsposito, AnnaAutoMLData augmentationNegative mood states recognitionFeature extractionSVMIn 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 [...].info:eu-repo/semantics/publishedVersionMDPITecnoCampus. Escola Superior Politècnica (ESUPT)2022info:eu-repo/semantics/article22 p.https://hdl.handle.net/20.500.12367/2198reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésSensors. 2022 Feb 21;22(4):1686© 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/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:20.500.12367/21982026-05-29T05:05:01Z
dc.title.none.fl_str_mv Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
title Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
spellingShingle Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
Nolazco Flores, Juan Arturo
AutoML
Data augmentation
Negative mood states recognition
Feature extraction
SVM
title_short Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
title_full Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
title_fullStr Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
title_full_unstemmed Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
title_sort Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
dc.creator.none.fl_str_mv Nolazco Flores, Juan Arturo
Faundez-Zanuy, Marcos
Velázquez-Flores, O. A.
Del-Valle-Soto, Carolina
Cordasco, Gennaro
Esposito, Anna
author Nolazco Flores, Juan Arturo
author_facet Nolazco Flores, Juan Arturo
Faundez-Zanuy, Marcos
Velázquez-Flores, O. A.
Del-Valle-Soto, Carolina
Cordasco, Gennaro
Esposito, Anna
author_role author
author2 Faundez-Zanuy, Marcos
Velázquez-Flores, O. A.
Del-Valle-Soto, Carolina
Cordasco, Gennaro
Esposito, Anna
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv TecnoCampus. Escola Superior Politècnica (ESUPT)
dc.subject.none.fl_str_mv AutoML
Data augmentation
Negative mood states recognition
Feature extraction
SVM
topic AutoML
Data augmentation
Negative mood states recognition
Feature extraction
SVM
description In 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 [...].
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12367/2198
url https://hdl.handle.net/20.500.12367/2198
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors. 2022 Feb 21;22(4):1686
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 22 p.
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424799401377793
score 15,811543

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