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An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

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dc.contributor.authorSerrano Mamolar, Ana
dc.contributor.authorArevalillo Herráez, Miguel
dc.contributor.authorChicote Huete, Guillermo
dc.contributor.authorGonzález Boticario, Jesús
dc.contributor.orcidhttps://orcid.org/0000-0002-0027-7128
dc.contributor.orcidhttps://orcid.org/0000-0002-0350-2079
dc.contributor.orcidhttps://orcid.org/0000-0002-7736-5572
dc.date.accessioned2025-01-13T10:02:05Z
dc.date.available2025-01-13T10:02:05Z
dc.date.issued2021-03-04
dc.descriptionThe registered version of this article, first published in “Sensors, 21, 2021", is available online at the publisher's website: MDPI, https://doi.org/10.3390/s21051777 La versión registrada de este artículo, publicado por primera vez en “Sensors, 21, 2021", está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/s21051777
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.versionversión publicada
dc.identifier.citationSerrano-Mamolar, A., Arevalillo-Herráez, M., Chicote-Huete, G., & Boticario, J. G. (2021). An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations. Sensors, 21(5), 1777. https://doi.org/10.3390/s21051777
dc.identifier.doihttps://doi.org/10.3390/s21051777
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14468/25214
dc.journal.issue5
dc.journal.titleSensors
dc.journal.volume21
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.keywordsaffective computingen
dc.subject.keywordsphysiological sensorsen
dc.subject.keywordsnonintrusiveen
dc.subject.keywordslearner modellingen
dc.subject.keywordsuser-centred systemsen
dc.titleAn Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situationses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicatione067a1f1-6036-4974-a582-85b556587d18
relation.isAuthorOfPublication.latestForDiscoverye067a1f1-6036-4974-a582-85b556587d18
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