# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 4 Discussion
### Comparison to State-of-the-Art Methods for Drowsiness Detection  
When comparing the performance of these classifications to related work on detecting driver drowsiness, we first note that, to our knowledge, no other studies make use of distal skin temperatures in their classification. Furthermore, only the patent by [PERSON] et al. [28] presents a method based on similar signals, namely distal ear temperature and heart rate. Here, different conceptual approaches are described as well as the performance of one such method for a training and validation set; respective accuracies of 87.5% and 72.7% were obtained. On the other hand, multiple studies present methodologies to detect or monitor drowsiness based on information about the heart rate. More specifically, features related to heart rate variability (HRV) are most often used. For instance, [PERSON] and [PERSON] [31] present a detection algorithm with an accuracy, sensitivity, and specificity of 95% each. In this study, two-minute-long drowsy and alert events were classified based on the ratio of low (0.04-0.15 Hz) to high frequency (0.15-0.40 Hz) variability in the heart rate obtained from photoplethysmography. In another study, ECG measurements were used to detect drowsiness based on HRV as well as respiratory frequency [32]. Here, a positive predictive value (PPV), sensitivity, and specificity of 96%, 59%, and 98%, respectively, was reported. In addition, an accuracy of around 78% corresponds to these results. In contrast to the two prior studies which used behavioral signs as a reference for the state of the driver, [PERSON] et al. validated their HRV-based drowsiness detection with EEG measurements [33]. Here, the algorithm was successful if drowsiness was indicated in a period of 15 min before sleep onset (NREM sleep stage 1). In 12 out of 13 subjects, sleep onset was detected in this timeframe. In comparison to these studies, our classification based on only the heart rate performs less well (an accuracy of 70.6%). However, HRV is interpreted as a measure for the dynamic control of the heart by different parts of the nervous system. In this study, the heart rate was interpreted as a part of the heat balance inside the human body. More specifically, HR represents the rate of aerobic heat production inside the body. Therefore, we also consider HR in combination with other components of the heat balance. Nonetheless, using ECG measurements in future research could make it possible to combine or compare these different approaches. To put our own results further into perspective to the state-of-the-art, a couple of recent studies that used completely different variables and features are discussed hereafter. Firstly, [PERSON] et al. [34] present a smartwatch-based wearable EEG system for driver drowsiness detection. They designed a headband with three dry electrodes, instead of 21 electrodes used in the standard 10-20 system. In their analysis,they distinguished between \"alert\" and \"drowsy\" epochs that were labelled based on the percentage of eye closure (PERCLOS) and the number of adjustments on the steering wheel. The authors reported an average accuracy of 91.25% and 91.92% for detecting the alert and drowsy epochs, respectively. Secondly, [PERSON] et al. [12] developed a method to determine PERCLOS from images of a dome camera in a bus. Their results showed a matching rate between their method and a ground truth measure for PERCLOS of 85.02% and 95.18% for normal and fatigued drivers, respectively. Since their method was based on commercial technology that is already implemented in busses, monitoring bus drivers can be done unobtrusively. Thirdly, the work by [PERSON] et al. [13] presents yet a completely different approach. The fatigue level, scored by experts, was linked with data about the steering wheel angles (SWA) recorded under real driving conditions. The system was able to run on-line and performed with an accuracy of 78.01% in distinguishing drowsy from non-drowsy participants.