Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts From Non-Intrusive Physiological Signals Inreal-World Situations

Version 1 : Received: 31 December 2020 / Approved: 4 January 2021 / Online: 4 January 2021 (12:52:44 CET)

How to cite: Serrano-Mamolar, A.; Arevalillo-Herráez, M.; G. Boticario, J. An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts From Non-Intrusive Physiological Signals Inreal-World Situations. Preprints 2021, 2021010039 (doi: 10.20944/preprints202101.0039.v1). Serrano-Mamolar, A.; Arevalillo-Herráez, M.; G. Boticario, J. An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts From Non-Intrusive Physiological Signals Inreal-World Situations. Preprints 2021, 2021010039 (doi: 10.20944/preprints202101.0039.v1).

Abstract

Emotion recognition is becoming very relevant in educational scenarios, since previous research has proven the strong influence of emotions on the student's engagement and motivation. There is no standard method for stating student's affect, but physiological signals have been widely used in educational contexts. Physiological signals have been proved to offer high accuracy in detecting emotions because they reflect spontaneous affect-related information, and which is fresh and do not require an additional control or interpretation. However, most proposed works use measuring equipment that limit its applicability in real-world scenarios because of their high cost and their intrusiveness. Expensive material means an economic challenge for schools and reduce the scalability of the experiments. Intrusive equipment can be uncomfortable for the students which can lead to errors in the collected data. In this work, we analyse the feasibility of developing a low-cost non-intrusive device that integrates easy-to-capture signals that guarantee high detection accuracy. The advantage of the approach also lies in using user’s centred information sources (intra-subject) in real-world situations, which provide better detection accuracy, by offering models adapted to each subject. To this end, 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 study the multi-fusion of every possible combination of these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. 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 work concludes that the implementation of a low-cost wrist-worn device for recognising relevant emotions from each student is possible and open the way to a wide range of practical applications in the context of adaptive learning systems.

Subject Areas

Affective Computing; Physiological sensors; Non-intrusive; Learner Modelling; User-centred systems

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