Deep Learning for Cognitive Load Monitoring: A Comparative Evaluation

The Cognitive Load Monitoring Challenge organized in the UbiT-tention 2020 workshop tasked the research community with the problem of inferring a user's cognitive load from physiological measurements recorded by a low-cost wearable. This is challenging due to the subjective nature of these physiological characteristics: In contrast to related problems involving objective measurements of physical phenomena (e.g., Activity Recognition from smartphone sensors), subjects' physiological response patterns under cognitive load may be highly individual, i.e., expose significant inter-subject variance. However, models trained on datasets compiled in laboratory settings should also deliver accurate classifications when applied to measurements from novel subjects. In this work, we study the applicability of established Deep Learning models for time series classification on this challenging problem. We examine different kinds of data normalization and investigate a variant of data augmentation.