## 2 Related Work  
### Understanding Distracted and Drowsy Driving Using Physiological Signals  
Several studies have addressed the problem of driver state modeling using physiological markers. However, in most scenarios, only a single condition was targeted thus, making most approaches relatively limited to generalize. Two of the very first and most insightful papers to study the problem were the works published by [PERSON] et al. in 2010 ([PERSON] et al., 2011) and [PERSON] et al. ([PERSON] et al., 2011) in 2011. The authors in both papers formulated the problem of driver modeling as an assessment of cognitive workload and showed its strong relation to heart-rate and heart-rate variability under the context of driving. Of special interest are their findings on evaluating the impact of simulated scenarios compared to real-life driving, as it was shown that in-lab driving setups can sufficiently replicate real-life driving conditions in several cases. Specifically as discussed in ([PERSON] et al., 2011), the simulated setup could cause the same physiological reactions to the participants both in terms of heart-rate and skin conductance when compared to the experiments conducted with real-life data.  
While many works have targeted cognitive load since the aforementioned papers where published ([PERSON] et al., 2011; [PERSON] et al., 2012; [PERSON] et al., 2013) due to its ability to encapsulate information related to both distraction and drowsiness, fewer studies have tried to decouple the two conditions and study them independently.  
The work proposed by [PERSON] et al. (2017) in 2017 showed that learning jointly electrocardiogram (ECG) and electroencephalogram (EEG) information could lead to promising results with respect to drowsiness detection, while more recently in 2019, [PERSON] et al. (2019) were the first to dig a bit deeper on the strength of ECG signals to categorize different levels of alertness by identifying specific features of importance.  
Similarly to drowsiness detection, very limited are the research efforts on detecting distracted behaviors using explicitly physiological data. [PERSON] et al. (2015) in 2015 compared the performance of ECG and EMG data for modeling distracted driving. The authors used conventional features and classifiers and got promising results on both detection and discrimination across different types of distractors. [PERSON] et al. (2018) showed in 2018 that cepstral ECG analysis could offer informative and robust signal representations towards detecting inattention in a subject independent manner. On the same line, [PERSON] et al. (2019) in 2019 showed that wavelet analysis of galvanic skin response (GSR) is also highly sensitive to distracted behavior. The authors however, did not compare their findings to any heart rate-based methods, despite their popularity in the broader area of physiological-based driver modeling.  
[PERSON] et al. (2019) were probably the first to study the two conditions independently but under a unified machine-learning framework. The authors explored both attention and alertness together based on a multi-class classification scheme using multiple physiological modalities such as BVP, skin conductance, skin temperature and respiration data. However, no experiments were conducted to investigate the classification strength of the individual signals and no signal-based comparisons were made.