RT info:eu-repo/semantics/article
T1 An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations
A1 Serrano Mamolar, Ana
A1 Arevalillo-Herráez, Miguel
A1 Chicote Huete, Guillermo
A1 Boticario, Jesús G.
K1 Affective computing
K1 Physiological sensors
K1 Nonintrusive
K1 Learner modelling
K1 User-centred systems
K1 Informática
K1 Computer science
AB Previous research has proven the strong influence of emotions on student engagement andmotivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, butthere is no standard method for predicting students’ affects. However, physiological signals havebeen widely used in educational contexts. Some physiological signals have shown a high accuracyin detecting emotions because they reflect spontaneous affect-related information, which is freshand does not require additional control or interpretation. Most proposed works use measuringequipment for which applicability in real-world scenarios is limited because of its high cost andintrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-costand nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By usingboth inter-subject and intra-subject models, we present an experimental study that aims to explorethe potential application of Hidden Markov Models (HMM) to predict the concentration state from4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skintemperature. We also study the effect of combining these four signals and analyse their potential usein an educational context in terms of intrusiveness, cost and accuracy. The results show that a highaccuracy 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 affectdetection, fail at the same task.
PB MDPI
YR 2021
FD 2021-03
LK http://hdl.handle.net/10259/7376
UL http://hdl.handle.net/10259/7376
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024