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‘PERCLOS’ is the percentage of eyelid closure over the pupil over time and reflects slow eyelid closures (“droops”) rather than blinks (Dinges & Grace, 1998).

The PERCLOS drowsiness metric was established in a 1994 driving simulator study as the proportion of time in a minute that the eyes are at least 80 percent closed (Wierwille et al., 1994). Eyes wide open represented 0% and eyes closed represent 100%. Today there are three PERCLOS measures in use:

• P70, the proportion of time the eyes where closed at least 70%;

• P80, the proportion of time the eyes closed at least 80%; and

• EYEMEAS (EM), the mean square percentage of the eyelid closure rating.

It has to be noted that in the study by Wierwille et al. (1994), and the related technical brief from the Federal Highways Administration the face of the participant was monitored and recorded in order to detect eyelid closes, and then trained human scorers viewed the recordings and rated the degree to which the drivers’ eyes were closed from moment to moment. The challenge related to the PERCLOS metrics is the automatic measurement of the eyelid position; however, successful attempts to measure eyelid position (and derive PERCLOS from it) are reported by Dinges & Grace, (1998), where a CCD camera monitors the face of the driver. The PERCLOS metrics are measured directly and estimated with non-parametric methods for detecting drowsiness in drivers. Dinges & Grace (1998) used connected-component and support vector machine to verify eye blinks. The driver performance data was correlated with PERCLOS measurement to judge whether the driver is drowsy.

The main weaknesses of the PERCLOS measure will now be discussed. Termed “PERCLOS”, the device and detection technique relies on the percentage of slow- eyelid closures during a several-minute period. Fast eye blinks (about 100 ms duration) or micro blinks is an important measure for detecting micro sleep during

driving and the PERCLOS system was not capable of measuring micro sleep (Hargutt, 2003). The PERCLOS system needs to measure eye parameters from the front of the eye (Dinges & Grace, 1998). The study by Kithil et al.(2000) suggested that the PERCLOS detection rate was overstated because a percentage of the population is not conducive to eye-reflectance techniques, or because the PERCLOS technology is unable to work during bright daylight or for drivers with reflective dark glasses.

Another potential disadvantage of PERCLOS is that it is a slow eyelid closure system requiring a restricted field of view. Consequently, if the user is operationally required to move around frequently, the system cannot capture the user’s eyes with the use of single camera array. Detection of drowsiness in an operational environment, in which the user’s head moves requires an array of cameras or modified system that would be mounted to the head of the user, making it obtrusive and restricting the individual’s overall field of view. An automated on-line drowsiness system that relies on slow eyelid closures as the input variable is not ideal in low humidity environments, where users are likely to close their eyes slowly ( and keep them closed over a period of time) in an attempt to moisten the eye (Kithil et al. 2000). An automated slow eyelid closure system, based on video images only, cannot differentiate between eyes closed due to drowsiness or eyes closed due to a rewetting (wet eye) of the eyes, which can potentially result in false positives.

2.9. Summary

According to the literature, both EOG and EEG are valid indicators of drowsiness. Drowsiness is characterized by increased blink detection, decreased blink amplitude and increased blink frequency and EOG can be used to measure changes in these

parameters. Most of the drowsiness detection method literature has indicated eye blink and eye movement related measures are generally consistent for detection of drowsiness. According to Hargutt (2000), different eye blink parameters can be used for classifying different stages of drowsiness. Hargutt, (2000) suggested that analysis of eyelid movements is one of the most promising approaches to predict driver status. It is widely accepted that eye-lid parameters are valid in the case of extreme stages of drowsiness. Little is known about whether it is possible to discriminate between different driver states ranging from full wakefulness to sleepiness on the basis of the time course of these parameters.

A series of experiments conducted during Hargutt’s studies conclude that time on task was varied, revealing that eyelid movements are physiologically controlled by at least two different processes. Therefore, closure time/speed and blinking frequency must be treated as two different parameters. Increased blink frequency indicates reduced vigilance, which is the first stage in the drowsiness process, and the blink duration and blink amplitude indicate increased drowsiness. Hargutt’s method is able to identify a stage of vigilance decrements with only a slight decrease in performance as well as the final stage shortly before the driver falls asleep. The driver often does not notice that he is in the early stage in which there may be a high probability of inadequate reactions to sudden critical events. The differentiation between drowsiness and vigilance decrements are important when thinking about possible countermeasures.

Some of the findings of this study were similar to other studies in that the operational definition identified a higher number of male drowsiness d drivers/riders than female, and more drowsiness d drivers/riders less than 29 years of age compared with older age groups. Other studies also found that most early morning fatigued drivers/riders were less than 29 years of age, and fatigued drivers/riders over 50 years of age were involved in more afternoon crashes than in early morning crashes.

Drowsiness was in many cases not found in the EEG even though a change in the eye parameters was detected. It could thus be concluded that the eye parameters were better than EEG for an early detection of drowsiness. EEG signal interpretation is a very challenging task; such signals are complex and difficult to process.

The next chapter reviews some of the systems that have been developed, based on the physiological effects reported in this chapter.