I. Introduction
With an increasing number of cars and trucks on the roads, there is an increase also in related problems such as traffic, air pollution and car crashes. According to the National Highway Traffic Safety Administration, in 2016 there were over 7 million car crashes in the U.S.A. alone [1]. An earlier study showed that driver status factors, such as inattention, drunk driving, or sleeping while driving amounted to over 45% of the reported crashes [2]. As consequence, a key element in the reduction of such accidents has been to monitor driver mental states, such as, attention level, mental workload, fatigue, drowsiness, and stress, to name a few. To this end, diverse driver status monitoring (DSM) systems have been proposed. Depending on their approach, DSM systems can be divided into two main categories: (i) systems that rely on the analysis of images captured from the driver to detect head position, facial direction, blinking rate and eye lid movements, to infer the driver’s status; and (ii) system that rely on acquiring physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), breathing signal, and electroencephalogram (EEG).