Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks

Drowsy driving poses a significant risk to road safety, necessitating the development of reliable drowsiness detection systems. In particular, the advancement of Artificial Intelligence based neuroadaptive systems is imperative to effectively mitigate this risk. Towards reaching this goal, the present research focuses on investigating the efficacy of physiological indicators, including heart rate variability (HRV), percentage of eyelid closure over the pupil over time (PERCLOS), blink rate, blink percentage, and electrodermal activity (EDA) signals, in predicting driver drowsiness. The study was conducted with a cohort of 30 participants in controlled simulated driving scenarios, with half driving in a non-monotonous environment and the other half in a monotonous environment. Three deep learning algorithms were employed: sequential neural network (SNN) for HRV, 1D-convolutional neural network (1D-CNN) for EDA, and convolutional recurrent neural network (CRNN) for eye tracking. The HRV-Based Model and EDA-Based Model exhibited strong performance in drowsiness classification, with the HRV model achieving precision, recall, and F1-score of 98.28%, 98%, and 98%, respectively, and the EDA model achieving 96.32%, 96%, and 96% for the same metrics. The confusion matrix further illustrates the model's performance and highlights high accuracy in both HRV and EDA models, affirming their efficiency in detecting driver drowsiness. However, the Eye-Based Model faced difficulties in identifying drowsiness instances, potentially attributable to dataset imbalances and underrepresentation of specific fatigue states. Despite the challenges, this work significantly contributes to ongoing efforts to improve road safety by laying the foundation for effective real-time neuro-adaptive systems for drowsiness detection and mitigation.