# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
###### Abstract  
Numerous reports state that drowsiness is one of the major factors affecting driving performance and resulting in traffic accidents. In the past, methods to detect driver drowsiness have been developed based on physiological, behavioral, and vehicular features. In this pilot study, we test the use of a new set of features for detecting driver drowsiness based on physiological changes related to thermoregulation. Nineteen participants successfully performed a driving simulation, while the temperature of the nose (T\({}_{\text{nose}}\)) and wrist (T\({}_{\text{wrist}}\)) as well as the heart rate (HR) were monitored. On average, an initial increase in temperature followed by a gradual decrease was observed in drivers who experienced drowsiness. For non-drowsy drivers, no such trends were observed. In addition, HR decreased on average in both groups, yet the decrease in the drowsy group was more distinct. Next, a classification based on each of these variables resulted in an accuracy of 68.4%, 88.9%, and 70.6% for T\({}_{\text{nose}}\), T\({}_{\text{wrist}}\), and HR, respectively. Combining the information of all variables resulted in an accuracy of 89.5%, meaning that ultimately the state of 17 out of 19 drivers was detected correctly. Hence, we conclude that the use of physiological features related to thermoregulation shows potential for future research in this field. driver drowsiness; thermoregulation; distal skin temperature; decision tree +
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controlled setups. Alternatively, the driver's behavior is also monitored for drowsiness detection. Features such as eye closure, blinking frequency, yawning, and head-nodding can be extracted by applying camera vision and face recognition techniques (e.g., [11; 12]). The advantage of using cameras is that the measurements are completely unobtrusive. When the camera is placed near the sun visor or the rear mirror, the driver's field of view is not obstructed at all. However, issues with lighting conditions have been associated with these techniques [7]. Thirdly, features extracted from the vehicle itself are also widely used to monitor driver drowsiness. The angle of the steering wheel, the pressure applied to the acceleration and brake pedal, distance to the vehicle in front and a lot of other vehicular features have been used in recent studies [13; 14; 15]. Currently, commercial applications built into cars, busses, and trucks are already available on the market. These so-called advanced driver assistance systems (ADAS) are designed to automate, adapt, and enhance vehicles for safer and better driving. Along with these ADAS, there is an ongoing evolution towards autonomous driving. Apart from the technical aspects of self-driving vehicles, the component of human attention and control should not be neglected. For instance, commercial and transport airplanes fly autonomously for the majority of the time, yet the pilots still have to monitor the plane's status continuously and must be ready for intervention to assure a smooth and safe flight. Similarly, low levels of autonomous driving also requires an attentive driver [16]. Moreover, legal implications related to fully autonomous driving are expected to influence integration on the market more than the technical challenges [17]. In this paper, we focus on a new set of features to monitor driver drowsiness based on physiological changes related to thermoregulation. The functional link between temperature regulation and sleep onset has been studied for a long time. In regulating temperature, the body is typically considered in terms of a core and an outer shell [18]. The core body temperature (T\({}_{\text{C}}\)) is maintained within a specific narrow range (around 37 \({}^{\circ}\)C), whereas the outer shell can vary over multiple degrees. To illustrate, in thermo-neutral conditions, distal skin temperature is reported to be 7-8 \({}^{\circ}\)C below the core [19]. However, at a room temperature of 35 \({}^{\circ}\)C, the hands and feet are only 3-4 \({}^{\circ}\)C cooler.