# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
## 4 Discussion
### Current Limitations and Future Perspectives  
Given the preliminary nature of this pilot study, several limitations and suggestions for future work are listed in this section. Firstly, more advanced methodologies and equipment will improve the quality of the obtained measurements. For instance, now a simple driving simulator is used as a basis for the experiments. Nowadays, there are simulators available with a driving experience very close to driving a real car. Nevertheless, when working with a simulator, lacking the danger of causing an accident is thought to affect the driver in his behavior [35]. On the other hand, purposely sending out drowsy drivers on the road would be unethical and illegal. Using wearable technology for ambulatory monitoring of the driver in real-life conditions might be a good alternative for this issue. Furthermore, applying a crossover study design in which each participant performs the driving test in drowsy and non-drowsy conditions would allow for a pairwise comparison of the individual direction and magnitude of the changes in thermoregulation. This is not possible with the current data since each driver only performed the test one time. Another way to improve the experimental work is by working with more advanced measures for determining the state of the driver. Now, each driver evaluates his/her state of alertness based on the SSS. Presumably, individual differences in the interpretation of each level of the SSS causes variability in the data. The use of EEG measurements (brain activity) could provide a solution to this issue. As mentioned, EEG is used as a gold standard in sleep research. Alternatively, scoring the driver's state based on an expert's opinion is also done in the literature. An expert can be trained in scoring the driver based on behavioral expressions. Although such a score is also the result of human interpretation, the variability due to a difference in the scorer is eliminated. A final remark on the applied methodology is the timing of the simulations. Currently, one fixed moment throughout the day is selected to perform the driving simulation. By executing the measurements at different moments, a generalization of the results is possible. For instance, [PERSON] et al. [33] used data collected at 11:00 and after lunch. However, we have to keep in mind that the theory behind this method is related to actual sleep onset and does not relate directly to short drops in attention throughout the day. Accordingly, future research on the proposed methodology will focus on this specific type of event. Related to the timing of these events, we expect that the observed physiological changes do not only occur at night. For instance, night shift workers have an altered sleep-wake cycle. In general, diurnal changes in body temperature and heart rate follow the daily activity-rest cycle [36]. Secondly, we discuss a number of limitations related to data analysis. At the moment, we applied a very straightforward method to determine significant differences in drowsy and non-drowsy drivers. Subdividing the measurements in a fixed amount of time windows was an inevitable part of the analysis. As mentioned, this is due to a difference in the length of the experiments. By applying the windows to the data and extracting the average value and the slope, a lot of detail, as well as information about the timing is lost. Nevertheless, this did not prevent us from identifying significant trends in the data and successfully distinguishing between drowsy and non-drowsy drivers. A follow-up study could either perform experiments of the same length or apply a time series approach to detect drowsiness over time and pinpoint key moments in the transition from an alert state to a drowsy one. Additionally, to allow for such a methodology to be applied in real-time and real-world situations, future research has to focus on using techniques such as recursive estimation and online time-series analysis to determine the increases and decreases in the variables in real-time.