2026-09-08T09:51:28Zhttps://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 Arevalillo-Herráez, Miguel Chicote Huete, Guillermo Boticario, Jesús G. 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. eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Affective computing Physiological sensors Nonintrusive Learner modelling User-centred systems 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