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.
