## 2 Fatigue in Working Contexts
#### Technologies for Human Fatigue Estimation and Detection
However, the 10 min PVT is often considered impractical for operational settings because of its duration, and the shorter version seems to be too short to detect relevant deterioration in vigilant attention in subjects with moderate impairment whose performances deteriorate only later during a test, whereas the longer versions may be unnecessarily long for other subjects who apparently fully alert or severely impaired [98]. Another version of PVT is the adaptive PVT (PVT-A), which is modified PVT with duration dependent on the subject's performance [98], which in contrast with the fixed duration of PVT and PVT-B, its duration is variable. In a validation experiment, the test duration of the PVT-A averaged less than 6.5 min (SD 2.4) for a training data set and 6.4 min (SD 1.7) for a validation data set. Moreover, PVT-A was shown to be highly accurate, sensitive, and specific relative to 10 min PVT performance. Future studies are needed to show its feasibility and usefulness in professional screeners and operational environment as a fitness for duty test [68]. Considering all the aspects cited so far, as emerged in the recent literature the most comprehensive way to detect fatigue relative seems to be continuous monitoring of the operator. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment. Particularly, the validation of measure of slow eyelid closures (slow eye blinks), referred to as Percentage of eyelid closure (PERCLOS), proportion of time that the eyes are closed over a certain interval, is evaluated in different studies [96; 100; 101; 102]. In fact, PERCLOS has been found to be the most reliable and valid measure of a person's alertness level among many drowsiness detection measures [27; 68]. It measures the percentage of eyelid closure over the pupil over time and reflects slow eyelid closures (droops), in order to reflect momentary fluctuations of vigilance. To validate PERCLOS, [PERSON] et al., (1998; 2002) [96; 100] systematically evaluated the validity of a number of putative sleepiness-detection technologies. These included brain wave (electroencephalogram [EEG]) algorithms, eye-blink rate devices, a measure of slow eyelid closures (i.e., PERCLOS), and a head position sensor, as well as individuals' ratings of their sleepiness. In a series of tightly controlled, double-blind experiments, they evaluated the extent to which each technology detected the alertness of subjects over a 40-h period of wakefulness, as measured by PVT lapses of attention--a well-validated measure of behavioral alertness. Human-scored PERCLOS proved superior to all other detection technologies in blindly predicting when PVT lapses of attention were occurring across the 42 h awake time each subject underwent. Nevertheless, it is important to underline that the PERCLOS method and its technological needs in the current configuration is difficult to be implemented in the production workplace. In fact, the industrial work operations can represent a research frontier as it is represented that the worker is widely variable in anthropometric and psychological conditions, so devices able to fix a specific point in the workplace (something like it is done in the cars sleepiness sensors) is not possible since the worker moves itself in the workplace and establish a reference on what the fatigue means for a subject not necessarily means define an absolute measure valid for all the subjects. Given these preliminary considerations a reactive possible solution to this issue is described at the end of this paper where the possible future developments are depicted to make applicable this fatigue measurement also to some non-static workers as the production or the yard workers.