2026-05-25T21:47:55Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73762023-03-22T11:58:21Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Serrano Mamolar, Ana 820 500 0000-0002-0027-7128 Arevalillo-Herráez, Miguel f4abd9c8-94e7-41dc-9efa-100e89e63db2 600 Chicote Huete, Guillermo a7e8ac85-d96b-46c4-9a85-0a2996b3a0c5 600 Boticario, Jesús G. 32584877-4227-4809-b2ce-d3f64ad74982 600 2023-02-06T07:39:50Z 2023-02-06T07:39:50Z 2021-03 http://hdl.handle.net/10259/7376 10.3390/s21051777 1424-8220 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. This 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). application/pdf eng MDPI Sensors. 2021, V. 21, n. 5, 1777 https://doi.org/10.3390/s21051777 info: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/ info: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)/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Affective computing Physiological sensors Nonintrusive Learner modelling User-centred systems Informática Computer science An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sensors 21 5 THUMBNAIL Serrano-sensors_2021.pdf.jpg Serrano-sensors_2021.pdf.jpg IM Thumbnail image/jpeg 3548 https://riubu.ubu.es/bitstream/10259/7376/4/Serrano-sensors_2021.pdf.jpg 347f0a6e49cd0d5fbc39d0721bc4319a MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/7376/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/7376/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Serrano-sensors_2021.pdf Serrano-sensors_2021.pdf application/pdf 2066352 https://riubu.ubu.es/bitstream/10259/7376/1/Serrano-sensors_2021.pdf 58c81c692f8ca1f8ccef431d0951da51 MD5 1 10259/7376 oai:riubu.ubu.es:10259/7376 2023-03-22 12:58:21.425 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIA0KQVVUT1JJWkEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSBkaWZ1bmRpciwgZGUgbWFuZXJhIGdyYXR1aXRhLCBlbCBjb250ZW5pZG8gZGUgbG9zIGFyY2hpdm9zIGRpZ2l0YWxlcyBxdWUgY29ycmVzcG9uZGVuIGFsIGRvY3VtZW50byBkZXNjcml0byBhbnRlcmlvcm1lbnRlLCBjb24gY2Fyw6FjdGVyIG5vIGV4Y2x1c2l2byB5IGRlIG1hbmVyYSBww7pibGljYSBlbiBhY2Nlc28gYWJpZXJ0byBhIHRyYXbDqXMgZGUgSW50ZXJuZXQsIHBhcmEgbG8gcXVlIGxhIEJpYmxpb3RlY2EgcHJvY2VkZXLDoSBhIGFyY2hpdmFybG9zIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwuIEFzaW1pc21vIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgZGUgQnVyZ29zIGEgcmVhbGl6YXIgbGFzIHRyYW5zZm9ybWFjaW9uZXMgbmVjZXNhcmlhcyBkZSBmb3JtYXRvLCBubyBkZSBjb250ZW5pZG8sIHBhcmEgZ2FyYW50aXphciBsYSBwcmVzZXJ2YWNpw7NuIHkgZWwgYWNjZXNvIGVuIGVsIGZ1dHVyby4NCg0KRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLg0KDQpMYSBjZXNpw7NuIGRlIGRlcmVjaG9zIGRlIGVzdGEgb2JyYSBzZSBlbmN1ZW50cmEgc3VqZXRhIGEgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3Iu