# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 4 Discussion
### Decision Tree Classification  
The first univariate classification based on information about the skin temperature of distal body parts (nose and wrist) shows promising results. The rather straightforward, predetermined structure of the decision trees, results in an accuracy between 68.4% and 88.9%. Moreover, AUC-values between 0.833 and 0.975 were observed. For both temperature variables, the specificity is higher than the sensitivity. This can be explained because two constraints have to be met for labeling the driver as \"drowsy\", whereas he/she can be labelled as \"non-drowsy\" after only one decision in the decision tree (see Figure 3). Moreover, we note that the performance for the classification based on T\({}_{\rm wrist}\) are better than the performance of T\({}_{\rm nose}\). When analyzing the raw data of both variables, we observed smaller changes in T\({}_{\rm wrist}\) from sample to sample. Obviously, this was related to the much higher sampling frequency of T\({}_{\rm wrist}\) (see Section 2.2. Data Collection). On the other hand, it has been shown that the temperature of the nose is also more variable. For example, it has been shown that the nose temperature changes periodically due to respiration [30]. Alternatively, an increased sensitivity and accuracy is observed when we only consider the information of window 6 for T\({}_{\rm nose}\). From this, we conclude that the decrease in temperature is a more prominent feature for distinguishing between drowsy and non-drowsy drivers. Nevertheless, only considering this feature leads to more incorrect \"drowsy\" classifications, which is not surprising since the physiology behind temperature changes in relation to sleep onset suggests that both an increase and decrease occur. When considering the heart rate, a negative slope in window 6 is observed for 8 out 9 drivers who reached the SSS threshold and were labelled as \"drowsy\". In addition, such a decrease was also observed in half of the \"non-drowsy\" participants. Given the simplicity of the decision criterion, an overall accuracy of 70.6% stresses the significance of the decreasing trend in HR in our test population.  
In the last section of the results, two classifications based on T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\) and HR are presented. When the constraints for all three variables have to be met in order to label a driver as \"drowsy\", only four out of eight drivers who actually experienced drowsiness were labeled correctly. In contrast, no drivers were falsely labelled as being drowsy. Accordingly, the use of such stringent constraints can be applied to situations where false positive classifications are unacceptable. In other words, this methodology can be used when it is important to be certain that a driver labelled as drowsy, is actually drowsy. Nevertheless, a sensitivity of only 50.0% is not sufficient to distinguish between drowsy and non-drowsy drivers consistently. Evidently, using less stringent constraints results in a larger number of \"drowsy\" labels. Hence, all 10 drowsy drivers were labeled correctly. While the specificity is now decreased, we note that still 7 out of 9 non-drowsy drivers are correctly identified. This last classification resulted in an accuracy of 89.5%.  
When comparing the different classification approaches, we note that the univariate classification for T\({}_{\text{wrist}}\) and the second multivariate classification resulted in similar accuracies (88.9% and 89.5%). Moreover, both results cannot be compared directly because the number of samples differs. The fact that a single temperature variable performs equally well as the multivariate classification indicates that including the heart rate for classifying the data does not add significant value to the current analysis.