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dc.contributor.authorSerrano Mamolar, Ana 
dc.contributor.authorArevalillo-Herráez, Miguel
dc.contributor.authorChicote Huete, Guillermo
dc.contributor.authorBoticario, Jesús G.
dc.date.accessioned2023-02-06T07:39:50Z
dc.date.available2023-02-06T07:39:50Z
dc.date.issued2021-03
dc.identifier.urihttp://hdl.handle.net/10259/7376
dc.description.abstractPrevious research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.en
dc.description.sponsorshipThis research was partly supported by Spanish Ministry of Science, Innovation and Universities through projects PGC2018-096463-B-I00 and PGC2018-102279-B-I00 (MCIU/AEI/FEDER, UE).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofSensors. 2021, V. 21, n. 5, 1777es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAffective computingen
dc.subjectPhysiological sensorsen
dc.subjectNonintrusiveen
dc.subjectLearner modellingen
dc.subjectUser-centred systemsen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleAn Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situationsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/s21051777es
dc.identifier.doi10.3390/s21051777
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-096463-B-I00/ES/USO DE SISTEMAS TUTORIALES INTELIGENTES PARA ESTUDIAR ASPECTOS COGNITIVOS Y AFECTIVOS EN LA ENSEÑANZA Y EL APRENDIZAJE DE LA RESOLUCION DE PROBLEMAS VERBALES/es
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-102279-B-I00/ES/ENFOQUE DE DESARROLLO INTELIGENTE E INTRASUJETO PARA MEJORAR ACCIONES EN SISTEMAS ADAPTATIVOS EDUCATIVOS QUE CONSIDERAN EL ESTADO AFFECTIVO (INT2AFF)/es
dc.identifier.essn1424-8220
dc.journal.titleSensorsen
dc.volume.number21es
dc.issue.number5es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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