## 5 Results
### Drowsy Driver Modeling  
For this experiments we split the data into the two following classes; recordings made during the morning session (8 a.m. to 11 a.m.) are labeled as 'alert', while recordings made during the afternoon session (4 p.m. to 8 p.m.) are marked as 'drowsy'. We perform a 10-fold cross validation across the participants using 20% of the users for testing and the rest 80% for training.  
As shown in Figure 4 from all the evaluated feature sets, the combination of \"_BVP and respiration_\" signals is by far the most efficient approach, achieving an AUC performance of 88%. These results are partially in line with the findings presented in Section 3.4, which identified that specific BVP statistics are highly related to both tasks. On the other hand, in contrast to the features identified through the analysis of Section 3.4, we observe that respiration related data are also highly associated with drowsiness. In particular, the \"_top3 IBI BVP features_\" set along with all the \"_respiration related data_\" are responsible for the second and third best AUC scores with 75% and 74%, respectively. However, when we combine all BVP features, the performance drops significantly to 61%. We believe that the significant increase in feature space along with the decrease in available data after sub-sampling the signals to 8 Hz is partially responsible for that observation, since the network parameters do not have access to the required amount of information in order to get properly trained. In addition, it is highly possible that several BVP features are not actually good descriptors of drowsiness, thus adding noise to the input instead of actually assisting to the final decision. On the other hand, it seems that just joining the two raw information streams of BVP and respiration is sufficient for the network to capture the important characteristics of the signals. We believe that this could potentially relate to the fact that both signals have a periodic behavior and we know for a fact that characteristics related to IBIs for the BVP signal and to rate and amplitude for the respiration signal, are of special importance to the task. This can be confirmed by the high performance observed when using explicitly the top3 IBI BVP features or the set of respiration related data accordingly. The rest of the evaluated feature combinations do not offer any significant value on this task showing performance that is comparable to random guess.  
Focusing on the results of Table 3, we see that the CNN-LSTM model performs significantly better compared to all the baseline classifiers when trained only on the raw BPV and respiration data. The very poor performance of the baseline classifiers is indicative of how challenging the targeted problem is, while at the same time highlights the superiority of the deep spatio-temporal classifier compared to the traditional and more popular alternatives  
Figure 5: ROC curves and AUC scores for different feature sets on the task of distraction detection using the CNN-LSTM network.  
on the task of physiological-based drowsy driver behavior modeling. In particular, all baseline classifiers perform very poorly in terms of sensitivity with very high specificity scores. In other words, these models fail marginally to identify drowsy behavior. However, it is very unlikely to identify someone who is actually alert as drowsy. On the contrary, the CNN-LSTM model outperforms by far all baselines in terms of sensitivity with a score of 93%, while it provides worse but reasonable results in terms of specificity with a score of 71%. This means that the chance of correctly identifying drowsy behavior with this model is quite high, even though in approximately three out of ten times an alert driver will be wrongfully identified as drowsy.