Effect of drowsiness on driving performance variables of commercial vehicle drivers

Slow eyelid closure as a measure of driver drowsiness and its relationship to performance

“…Strong relationships have also been described previously between PERCLOS and simulated driving performance (r-values of 0.40 to 0.78) using different methods for measuring PERCLOS (Dingus et al, 1987;Wierwille, 1999;Mortazavi, Eskandarian & Sayed, 2009). In real-world driving, a brief period of inattention or driver distraction at a critical point in time may result in running off the road and failure to respond quickly enough to avoid a crash (O'Hanlon & Betty, 1977;Shiferaw, Stough & Downey, 2014).…”

“…These include driverrelated physiological measures, such as ocular measures, including blink duration and slow eyelid closures (Wierwille & Ellsworth, 1994;Åkerstedt, Peters, Anund, & Keckland, 2005;Anund et al, 2008;Åkerstedt et al, 2010) and electroencephalography (EEG) algorithms (Cajochen et al, 1999;Horne & Reyner, 1996), or vehicle-related measures, such as lane departure measures (May & Baldwin, 2009;Mortazavi, Eskandarian, & Sayed, 2009). Eyelid closure appears to be a promising method for indicating driver drowsiness in real-time (Dinges, Mallis, Maislan, & Walker-Powell, 1999;Ftouni et al, 2012).…”

“…Such devices can be considered advantageous as they are comparably unobtrusive to the driver, and can assess drowsiness in real-time. Automated measures of oculography have been found to be highly related to simulated driving metrics in well-rested (Mortazavi, Eskandarian, & Sayed, 2009) and sleep-deprived healthy individuals 6 (Johns, et al, 2007;Anderson et al, 2013;Ftouni et al, 2013). However, the current evidence supporting the effectiveness of automated technologies for detecting drowsiness and performance impairment is scarce, and such devices require further validation in sleep deprived subjects, particularly among commercial drivers.…”

Wristband-Type Driver Vigilance Monitoring System Using Smartwatch

“…Then the membership grade is further update by finding the different between the cluster center and the weighted means (8).…”

“…Friedrichs et al [7] extracted features from the steering and lane data under real driving conditions to investigate the correlation of the drowsiness related features. Mortazavi et al [8] identified two phases of drowsiness-related degradation in steering control as well as significant patterns in lateral position variations corresponded with the level of drowsiness. However, large inter-and intra-individual differences in driving patterns provide a difficult task in accurately classifying driver vigilance level.…”