## 5 Results
### Multitask Learning for Joint Driving Behavior Modeling  
Dedicating a single machine learning model to each condition of interest has been traditionally the most popular and effective approach of dealing with problems related to human behavior modeling. However, in several cases we are interested in predicting conditions that coexists and may overlap. Our assumption is that overlapping conditions may share a common ground in terms of physiological reactions caused to the drivers. To that end we evaluate different machine learning methods on their ability to jointly predict driver's state in terms of distraction and alertness. For these experiments we use the combination of the seven temporal features that performed the best for the individual tasks. Hence, each training feature vector consists of the raw recordings from BVP and respiration plus the five BVP features identified through the analysis of Section 3.4. Figure 3 illustrates all the deep learning methods evaluated. For the cases of SVM, RF and KNN we formulate the problem similarly to the deep Scheme D model, i.e., as a 4-class classification task. In order to have a fair comparison against the different approaches, we evaluate all models on their ability to correctly identify drowsiness and distraction as independent tasks. In the case of multi-class models (ScemeD, SVM, KNN, RF) in particular, the results are evaluated as two binary classification problems and not as a traditional four-class task. Formulating evaluation as such, allows for a one to one comparison against the multitask methods (Shemes A, B, C) and avoids diluting the characterization ability of the different classifiers with respect to the individual conditions, while still learning shared parameters between tasks. Figure 6 shows the ROC curves for all the deep learning-based models for both tasks. Solid lines correspond to the drowsiness detection task, while dotted lines to distraction detection. Schemes A and B offer the most balanced results across the two detection problems. In particular, the two models perform comparably well to the CNN-LSTM distraction model (Figure 5), while providing the second and third best results for the drowsiness task in terms of AUC when compared to the alternatives of Figure 4. However, despite the fact that the performance is acceptable for both tasks, it always remains inferior to the condition-targeted classifiers. This could be partially an effect related to the limited amount of data available to properly tune the model parameters. Another possible explanation could be that other than the IBI BVP features that have a proven value on characterizing both conditions, the rest of the input features are less robust across tasks, thus hindering the model's ability to converge at a higher overall score. Nonetheless, it is clear that Schemes A and B offer overall the best modeling performance across all the approaches that jointly learn representations for the two conditions. These architectures are the only ones that have dedicated layers for each task, while their parameters are being updated based on an optimization error that takes into account both \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Distraction Detection**} & & \\  & **SVM** & **KNN** & **RF** & **CNN-LSTM** \\ \hline Sensitivity (\%) & 53 & 69 & 70 & 70 \\ Specificity (\%) & 72 & 52 & 50 & 74 \\ Average Recall (\%) & 62.5 & 60.5 & 60 & **72** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distracted vs. not-distracted classification using the top5 BVP features as selected by the analysis in Figure 5 and Table 2.  
individual performances. Based on these results, we can see that the number of shared layers does not have a significant impact on the task-specific performance, even though this might change as the available training data increase. Scheme C, which has all layers shared across the two tasks, performs the worst in terms of AUC.