On the use of ECG and EMG Signals for Question Difficulty Level Prediction in the Context of Intelligent Tutoring Systems A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the testu0027s questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the selfperceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1score of 75.49% for the personalised singleparticipant models and a classification F1score of 64.10% for the global models.

Deep Learning in Physiological Signal Data: A Survey Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deeplearning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deeplearning approach that we review are the input data type, deeplearning task, deeplearning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deeplearning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deeplearning concept perspective such as training architecture and dataset sources.