Key result
HMM-based intra-subject models using physiological signals achieved high accuracy in predicting concentration states, whereas inter-subject models failed at the same task.
Why the study?
Emotion recognition is increasingly relevant in education, but standard methods are lacking and existing physiological measuring equipment has limited real-world applicability due to high cost and intrusiveness.
Intra-subject HMM models using physiological signals can accurately predict student concentration, whereas inter-subject models are ineffective.
Answers are grounded in this paper and cite their sources.
Intra-subject physiological HMM models may aid personalized concentration monitoring; leaves open scalable inter-subject applications pending validation.
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.
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Serrano-Mamolar et al. (2021) studied Concentration state in educational contexts. Hidden Markov Models (HMM) based intra-subject models using physiological signals vs. Inter-subject models was evaluated on Accuracy in predicting concentration state. HMM-based intra-subject models using physiological signals achieved high accuracy in predicting concentration states, whereas inter-subject models failed at the same task.
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