## 4 Discussion  
In this study, a novel method for driver stress evaluation based on thermal IR imaging and supervised machine learning approaches was described. Thermal IR imaging and ECG were acquired on ten subjects, while performing an experiment on a driving simulator using the software City Car Driving v.1.5 [35]. The experimental session consisted of 45 min of urban context driving with pre-established weather and traffic conditions. Electrocardiography (ECG) signals were used to infer the stress condition of the drivers. Among the variety of indices derived from the ECG signals, stress was considered [18]. In this study, the SI was evaluated in consecutive 30 s time windows by the software Kubios [36]. In the same temporal window, six representative features from average thermal signals on four ROIs (i.e., nose tip, left and right nostrils, glabella) were extracted. The thermal signals were automatically determined by a real-time tracking procedure. The tracking relied on state-of-the-art computer vision algorithms applied on visible images and the optical co-registration between the visible and thermal imaging devices ensuring high performance on signal processing and speed of extraction. Indeed, the high performances were highlighted by the percentage of correctly processed frames which reached an average of 94.66% by the confidence index for face classification that was 0.90 and by the average processing time that was only 0.09 s/frames. A multivariate machine learning approach based on Support Vector Regression (SVR) with Radial Basis Function (RBF) kernel was employed to estimate the ECG-based SI through peculiar thermal features extracted from facial ROIs. Those ROIs were chosen on the basis of their physiological importance for stress detection [43]. A total amount of 18 thermal features (six features for each ROIs) were computed and used as predictors, while the SI, evaluated through the ECG signals, was considered as the regression output. A leave-one-subject-out cross-validation was employed to test the generalization of the regression. This procedure was iterated for all the subjects and further statistical analyses were performed on the out-of-training-sample estimation of SI. Such a metric was labeled as SICross. The correlation between SI and SICross was \(r=0.61\) (\(p=\
eg 0\)) thus indicating a good estimation of the SI through the considered thermal features (Figure 6a). A feature-based analysis was performed to investigate the relevance of each feature (Figure 6b). Concerning the nose tip region, the most contributing features to the SI estimation were the kurtosis, the skewness and the standard deviation. The weights associated to the kurtosis and skewness had negative values, thus indicating an inverse relation between SI and the features' trends. The opposite trend was observed for the weights associated to the standard deviation. This pattern seems to be correlated with sweating or vasoconstriction phenomena, occurring with increasing stress [51; 52]. In fact, an increase in the standard deviation and a reduction of the kurtosis and skewness parameters (i.e., flatness and asymmetry of the distribution of the related signal [53]) can be associated to a decrease of uniformity of the signal, thus indicating the presence, for instance, of \"cold spots\" typically present during sweating and vasoconstriction processes [19]. Concerning the nostrils region, instead, a strong inverse relation between the weight associated to the standard deviation and the SI was found. Since the thermal signals from nostrils are highly related to the breathing function, a lower signal variation (revealed by a decrease in the standard deviation) could be associated to a high breathing rate [54]. This result is in accordance with the findings from References [55; 56], where it was shown that stress is associated with an increased respiratory rate. Finally, referring to the glabella region, the most relevant feature to the SI estimation was the 90 th percentile, i.e., the value below which 90% of data falls.