A Deep Learning Approach to Recognize Cognitive Load using PPG Signals

Physiological data are nowadays frequently used to recognize the affective state of subjects while performing different tasks. Automatic recognition of a stressful state as a consequence of a high level of cognitive load is significant to prevent illnesses like depression, anxiety and sleep disorders that are often due to excessive workload. The spread of wearable sensors that are increasingly reliable and comfortable makes them easy to use in day-life activities. However, due to the nature of experiments that involve subjects, the cardinality of the acquired data is often low, making difficult to train deep learning methods from the scratch. In this paper we consider the photopletismography (PPG) that measures the blood volume registered just under the skin, which can be used to obtain the heart rate of the subject. It is well known that PPG data are particularly relevant to detect high level of arousal that is activated by stress. We show that, converting monodimensional photopletismography (PPG) data into bidimensional signals it is possible to apply a pretrained CNN, obtaining deep features that outperform handcrafted ones in classification tasks, especially introducing feature selections strategies to avoid curse of dimensionality.