Deep physiological arousal detection in a driving simulator using wearable sensors

Driving is an activity that requires considerable alertness. Insufficient attention, imperfect perception, inadequate information processing, and sub-optimal arousal are possible causes of poor human performance. Understanding of these causes and the implementation of effective remedies is of key importance to increase traffic safety and improve driver's well-being. For this purpose, we used deep learning algorithms to detect arousal level, namely, under-aroused, normal and over-aroused for professional truck drivers in a simulated environment. The physiological signals are collected from 11 participants by wrist wearable devices. We presented a cost effective ground-truth generation scheme for arousal based on a subjective measure of sleepiness and score of stress stimuli. On this dataset, we evaluated a range of deep neural network models for representation learning as an alternative to handcrafted feature extraction. Our results show that a 7-layers convolutional neural network trained on raw physiological signals (such as heart rate, skin conductance and skin temperature) outperforms a baseline neural network and denoising autoencoder models with weighted F-score of 0.82 vs. 0.75 and Kappa of 0.64 vs. 0.53, respectively. The proposed convolutional model not only improves the overall results but also enhances the detection rate for every driver in the dataset as determined by leave-one-subject-out cross-validation.