Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.

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Traffic accidents due to drivers falling asleep while driving is an important cause of death, and effective techniques for coping with this phenomenon are needed. In this paper we present a simple, yet effective algorithm, based on heart rate variability analysis. Results of the experimental validation are reported: two test sessions were performed, one in domestic environments and one in realistic driving environments by using a dynamic driving simulator. The results confirm the results obtained in the previous work (Groppo et al., 2020), and the capability of the proposed algorithm in predicting sleep while driving using a physiological signal that can be collected at the wrist of the driver.INDEX TERMS Drowsiness onset detection, sleep onset, accident prevention.

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