Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions

Abstract

Driver sleepiness is a contributing factor in many road fatalities. A long-standing goal in driver state research has therefore been to develop a robust sleepiness detection system. It has been suggested that various heart rate variability (HRV) metrics can be used for driver sleepiness classification. However, since heart rate is modulated not only by sleepiness but also by several other time-varying intra-individual factors such as posture, distress, boredom and relaxation, it is relevant to highlight not only the possibilities but also the difficulties involved in HRV-based driver sleepiness classification. This paper investigates the reliability of HRV as a standalone feature for driver sleepiness detection in a realistic setting. Data from three real-road driving studies were used, including 86 drivers in both alert and sleep-deprived conditions. Subjective ratings based on the Karolinska sleepiness scale (KSS) were used as ground truth when training four binary classifiers (k-nearest neighbours, support vector machine, AdaBoost, and random forest). The best performance was achieved with the random forest classifier with an accuracy of 85%. However, the accuracy dropped to 64% for three-class classification and to 44% for subject-independent, leave-one-participant-out classification. The worst results were obtained in the severely sleepy class. The results show that in realistic driving conditions, subject-independent sleepiness classification based on HRV is poor. The conclusion is that more work is needed to control for the many confounding factors that also influence HRV before it can be used as input to a driver sleepiness detection system.

Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and Partial Automated Real-Road Driving

“…High sensitivity and specificity have been achieved by some of the laboratory studies that included subject dependent modeling [20], [24], [27]. Dropped performance has been observed by several studies when implementing subject independent modeling with subject-wise cross validation [24], [25], due to large individual variations in HRV. Several studies applied methods for personalization for detection algorithms to reduce the influence of personal variation [21], [22], [25].…”

“…Dropped performance has been observed by several studies when implementing subject independent modeling with subject-wise cross validation [24], [25], due to large individual variations in HRV. Several studies applied methods for personalization for detection algorithms to reduce the influence of personal variation [21], [22], [25]. A recent controlled real-road study [25] suggests that the assessment accuracy can suffer from other influential factors on real roads.…”

“…Several studies applied methods for personalization for detection algorithms to reduce the influence of personal variation [21], [22], [25]. A recent controlled real-road study [25] suggests that the assessment accuracy can suffer from other influential factors on real roads. One study has included an automated driving task with simulated level 2 automated driving [24], however it was not compared with manual driving.…”

Survey and Synthesis of State of the Art in Driver Monitoring

“…However, HRV data vary both between individuals and over time for each individual, depending on both internal and external factors. Therefore, the many confounding factors that also influence HRV must be accounted for in order to use HRV as an indicator of drowsiness [ 76 ]. The breathing activity is an indicator of drowsiness, as changes in breathing rate or inspiration-to-expiration ratio occur during the transition from wakefulness to drowsiness [ 77 ].…”

Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences

“…ey found that physiological signals presented obvious individual characteristics, while subjectindependent drowsiness classifiers that ignored individual differences performed worse [44]. Similarly, Persson, et al used HRV measurements to build a drowsiness classifier based on machine learning and pointed out that the accuracy decreased dramatically for a new driver because of individual differences [45]. Yan et al [41] analyzed driver fingerprinting differences in drowsiness-detection models and found that differences in measurement distribution among drivers could be beyond differences caused by drowsiness when all drivers' measurements were mixed to train models, which decreased the correlation between measurements and drowsiness.…”