# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 3 Results
### Decision Tree Classification
#### 3.2.2 Multivariate Classification  
In the second classification approach, insights from the univariate classifications are combined to improve accuracy and robustness. Accordingly, the best performance was obtained by classifying the data based on the slope inside window 6 for T\({}_{\text{nose}}\), window 4 for T\({}_{\text{wrist}}\), and window 6 for HR. In a first, very strict classification approach, we imposed that for a certain simulation, the slope inside each of these windows had to be negative to classify the driver as drowsy. Accordingly, the order of the decisions was not of importance to this approach. In a second, milder classification approach, we assigned the label \"drowsy\" when at least two out of these three constraints were met. The performance of both classification types was also presented in Table 2 above. We note that the number of observations  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & **Windows** & **Sens. (\%)** & **Spec. (\%)** & **Acc. (\%)** & **N\({}_{\text{drowsy}}\)** & **N\({}_{\text{non-drowsy}}\)** \\ \hline Univariate: T\({}_{\text{nose}}\) & 1 \& 6 & 60.0 & 77.8 & 68.4 & 10 & 9 \\ Univariate: T\({}_{\text{wrist}}\) & 2 \& 4 & 77.8 & 100.0 & 88.9 & 9 & 9 \\ Univariate: HR & 6 & 88.9 & 50.0 & 70.6 & 9 & 8 \\ Multivariate: “strict” & 6, 4 \& 6 \({}^{\star}\) & 50.0 & 100.0 & 75.0 & 8 & 8 \\ Multivariate: “mild” & 6, 4 \& 6 \({}^{\star}\) & 100.0 & 77.8 & 89.5 & 10 & 9 \\ \hline \hline \end{tabular} \({}^{\star}\) Window number for T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), HR respectively.  
\end{table}
Table 2: Performance of the different classifications based on information about T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), and HR. The “strict” multivariate classification refers to the fact that for all three variables, the imposed constraints must hold for a driver to be classified as drowsy. The “mild” classification represents the case when a driver is labeled as drowsy when only two out of three constraints are met. The sensitivity, specificity, and accuracy are expressed as a percentage. N\({}_{\text{drowsy}}\) and N\({}_{\text{non-drowsy}}\) indicate the number of samples used in each case.  
Figure 5: Receiver operating characteristic (ROC) curve and corresponding area under the curve (AUC) for the univariate classification based on T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), and HR.  
was not the same for both analyses. As mentioned in Section 2.2., the data of three simulations were missing information about T\({}_{\rm wrist}\) or HR. Therefore, the first classification considered 16 out of 19 simulations. However, simulations with missing data can be considered in the second classification if the two available variables had the same sign of the slope inside the respective windows. Since this was the case for all three incomplete datasets, all 19 simulations were considered. Accordingly, we note that applying the strict constraints resulted in a specificity of 100.0% but also a relatively low sensitivity of 50.0%. Furthermore, an accuracy of 89.5% was observed under the mild constraints.