## 6 Conclusions  
In this paper, we explore different physiological markers and machine learning approaches on their ability to describe distracted and drowsy driving. For our analysis, we compiled a dataset of 45 subjects and we recorded their BVP, respiration, skin conductance and skin temperature responses while participating in a simulated driving setup. Based on our analysis, the contribution of this publication can be summarized through the answers on the following three research questions:  
* **Which physiological indicators are most indicative of drowsy and distracted behavior? ** With respect to drowsiness detection, BVP and respiration indicators proved to be the two signals that are mostly associated with the task. In particular, the combination of the raw BVP and respiration measurements leads to maximum drowsiness detection performance in terms of AUC score with a value of 88%, when processed through a spatio-temporal deep CNN-LSTM model. Second best performance, with a score of 75% AUC, is achieved by a subset of BVP related features when processed through the same modeling architecture, while respiration related data and features, lead to the third best performance with a score of 74% AUC. Skin conductance and temperature signals and features lead to significantly inferior performance, with their AUC scores fluctuating around 50%. With regard to distraction detection, BVP proved to be again highly associated with the task. All feature sets extracted from that signal marginally outperform all the alternative feature combinations when processed through the same spatio-temporal CNN-LSTM architecture, by achieving AUC scores in the range between 79% to 82%. The rest of the evaluated feature sets, which consist of various combinations of the remaining physiological markers and their statistical features, always perform around 60% AUC. Hence, showing their relation to distracted behavior but also highlighting their weakness on robustly capturing the condition when used exclusively. * **Are there specific statistical features coming from different signals that are particularly informative? ** Our analysis discussed in Section 3.4 and further evaluated in Sections 5.2 and 5.3, identified several features of importance related to the two conditions. More specifically, we train two DT classifiers targeted on the individual tasks, using all the available data, and we perform an entropy-based evaluation of all the available features on their importance towards detecting the two conditions. Figure 11 in the Appendix A illustrates the importance of all features in terms of information gain after training the two models. Based on this analysis, we select the five most informative statistical features, presented in Table 2. As it can be observed, all five features are related to BVP and in particular three are extracted from the time domain and describe patterns related to BVP IBIs and two are extracted from the spectral domain and are related to the spectral power of the signal in different frequency bands. BVP IBI related statistics alone show great performance on both tasks as they lead to the second best performance in both drowsiness detection with 75% AUC and distraction detection with 80% AUC. Interestingly, when combined with the frequency related features, the new feature set performs quite poorly on