## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Joint Condition Learning**} \\ \cline{3-10}  & & **SVM** & **KNN** & **RF** & **Scheme** & **Scheme** & **Scheme** & **Scheme** \\  & & & & **A** & **B** & **C** & **D** \\ \hline \multirow{2}{*}{**Drowsiness Detection**} & Sensitivity (\%) & 45 & 25 & 24 & 77 & 73 & **82** & 69 \\  & Specificity (\%) & 64 & 78 & 91 & 68 & 72 & 37 & 63 \\  & Average Recall (\%) & 54.5 & 51.5 & 57.5 & **72.5** & **72.5** & 59.5 & 66 \\ \hline \multirow{2}{*}{**Distraction Detection**} & Sensitivity (\%) & 51 & 58 & 70 & 75 & 78 & **84** & 78 \\  & Specificity (\%) & 73 & 52 & 50 & 71 & 68 & 56 & 66 \\ \multirow{2}{*}{**Drowsiness Detection**} & Average Recall (\%) & 62 & 55 & 60 & **73** & **73** & 70 & 72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Task-based performance on joint condition learning of drowsiness and distraction. the drowsiness detection task leading to almost random performance with 50% AUC while it offers the best results on drowsiness detection with 82% AUC. It is not clear yet why adding the frequency features to the input feature set harms the classifier so abruptly with respect to drowsiness detection and this is something that we would like to investigate further in the future. In addition to the BVP features, respiration related statistics showed strong association with drowsy driving. In particular, combining the raw respiration data with temporal features describing the temporal characteristics of the signal leads to a 74% AUC, which is the third highest score achieved by the single-task deep model for drowsiness detection. Specifically the features extracted from respiration correspond to: respiration amplitude, respiration period, respiration rate, respiration rate epoch mean (where an epoch is 5 min of data), respiration rate mean (br/min) and respiration rate std dev (br/min). * **Is it possible to jointly tackle the problems of drowsiness and distraction detection and how such a framework can be formulated? **  
Overall, our experiments showed that deep CNN-LSTM-based methods significantly outperform all other evaluated traditional machine learning alternatives, which have the lion's share in evaluations presented in the related literature (RFs, KNN, SVM). In particular, the single-task CNN-LSTM model leads to a maximum performance of 88% AUC with 82% average recall for the drowsiness detection task and to 82% AUC with 72% average recall for the distraction detection task. Second best performance however across both tasks, is recorded by the joint condition learning multitask schemes with a branching architecture (Schemes A and B of Figure 3). Our evaluations highlight the potential of multitask learning towards directly addressing such abstract conditions with overlapping physiological responses. Schemes A and B offer results directly comparable to the corresponding single-task CNN-LSTM model for the distraction detection task with ~79.5% AUC and a slightly improved 73% average recall. At the same time, for the drowsiness detection task the models achieve a ~76.5% AUC and 72.5% average recall. Even though performance is lower in terms of AUC and sensitivity compared to the single-task CNN-LSTM model in the case of drowsiness detection, the classifier still performs notably higher compared to all other evaluated methods on the task. In general, we argue that building multitask learning models with dedicated layers on every targeted task is the method that showed the most promising results, on joint condition learning. Avoiding branching and having only shared layers across tasks led to the worst results as the model struggled to effectively distinguish between conditions, since the learned features could not scale equally across tasks. The multi-class approach also offered inferior results compared to multitask learning as the division of training data into multiple groups had a negative impact on the final result.