Driver Drowsiness Detection and Measurement Methods - Driver Drowsiness Detection: Systems and Solutions - page 8

Table 2.2 Stanford Sleepiness Scale

Va l u e Description

1 Feeling active, vital, alert or wide awake

2 Functioning at high levels, but not at peak; able to concentrate

3 Relaxed, awake but not fully alert; responsive

4 Little foggy

5 Foggy, beginning to loose track; having difficulty staying awake

6 Sleepy, woozy, fighting sleep; prefer to lie down

7 Cannot stay awake, sleep onset appears imminent

Table 2.3 Karolinska
Sleepiness Scale verbal cues

Level Sleepiness level

1 Extremely alert

2 Very alert

3 Alert

4 Rather alert

5 Neither alert nor sleepy

6 Some signs of sleepiness

7 Sleepy but no difficulty staying awake

8 Sleepy with some effort to keep alert

9 Extremely sleepy, fighting sleep

￿ Visual Analogue Scale (VAS) [ 65 ]: asks subjects to rate their “sleepiness” using

a scale spread along a 100 mm wide line. Suggestions for sleep deprivation state
range from “just about asleep” (left end) to “as wide awake as I can be” (right
end). Subjects place a mark on the line expressing how sleepy they feel they are.
Sleepiness level is measured by the distance in millimeters from one end of the
scale to the mark placed on the line. The VAS is convenient since it can be rapidly
administered as well as easily repeated.

The MSLT and MWT were developed for neurophysiological assessment and are
sensitive to both acute and chronic sleep loss. These types of tests cannot be
administered and monitored without special training and under special conditions
and can only be performed on healthy subjects [ 7 , 45 ]. Moreover, the practicality
of these tests for assessing crashes is very small but some portions of them, such
as slightly modified “nap tests” in combination with questionnaires, have been used
for that purpose [ 51 ].
Other subjective measurement methods worth mentioning are: Sleep-Wake
Activity Inventory [ 54 ], Pittsburgh Sleep Quality Index [ 6 ] and Sleep Disorders
Questionnaire [ 14 ]. All of them are not quite practical for assessment of crash
situations but their ability to monitor individuals over extended periods of time,
combined with subjects' self-reporting provides valuable information towards better
understanding of sleep deprivation and its manifestation in humans.