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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7376

    Título
    An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations
    Autor
    Serrano Mamolar, AnaAutoridad UBU Orcid
    Arevalillo-Herráez, Miguel
    Chicote Huete, Guillermo
    Boticario, Jesús G.
    Publicado en
    Sensors. 2021, V. 21, n. 5, 1777
    Editorial
    MDPI
    Fecha de publicación
    2021-03
    DOI
    10.3390/s21051777
    Resumen
    Previous 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.
    Palabras clave
    Affective computing
    Physiological sensors
    Nonintrusive
    Learner modelling
    User-centred systems
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/7376
    Versión del editor
    https://doi.org/10.3390/s21051777
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    • Artículos ADMIRABLE
    Atribución 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
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    Serrano-sensors_2021.pdf
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