FEDERAL HIGHWAY ADMINISTRATION • OFFICE OF MOTOR CARRIERS

FHWA Contact: Ron Knipling, HCS-30, (202) 366-2981; NHTSA Contact: Paul Rau, NRD-13, (202) 366- 0418

PERCLOS: A Valid Psychophysiological

Measure of Alertness As Assessed by

Psychomotor Vigilance

Introduction

Driver fatigue is a significant risk factor in commercial transportation. Fatigue from

combinations of sleep loss, night driving, and prolonged work time contributes 

substantially to the number of motor vehicle crashes. Over the past decade, driver

drowsiness/fatigue has been the subject of intensified Department of Transportation

interest and activity; one major initiative has been to explore technology that will

enhance commercial motor vehicle (CMV) driver fatigue management.

This Tech Brief summarizes an Intelligent Transportation System (ITS) study titled

Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the

Basis for Alertness Management. The study was funded in part by FHWA’s Office of

Motor Carriers and managed by the National Highway Traffic Safety Administration

(NHTSA). The project’s goal was to evaluate the validity and reliability of several

drowsiness-detection measures and technologies in a controlled laboratory setting, 

and to analyze the effects of alerting stimuli on drivers’ alertness levels.

Of the drowsiness-detection measures and technologies evaluated in this study, the

measure referred to as “PERCLOS” was found to be the most reliable and valid 

determination of a driver’s alertness level. PERCLOS is the percentage of eyelid closure

over the pupil over time and reflects slow eyelid closures (“droops”) rather than blinks.

A 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) Based on research by Wierwille and colleagues (1994), FWHA and NHTSA

consider PERCLOS to be among the most promising known real-time measures of 

alertness for in-vehicle drowsiness-detection systems.

The results of this research support the development of a “first-ever” real-time 

drowsiness detection sensor that would measure the percentage of eyelid closure 

over the pupil, over time (i.e., PERCLOS).

Purpose

Because this study was concerned with finding a reliable and valid drowsinessdetection system, FHWA and NHTSA evaluated several promising drowsiness-detection

technologies. These operator-centered, in-vehicle, fatigue-monitoring technologies

record various biobehavioral dimensions of an operator, such as a feature of the eyes,

face, head, heart, or brain electrical activity, while the operator is driving.

Practical systems for in-vehicle alertness monitoring may consist of a continuous 

indicator display of a driver’s alertness level, provided by in-vehicle fatigue-monitoring

technology, as well as warning messages to notify a driver when drowsiness is detected

and alerting stimuli to restore driver alertness and performance. Thus, this research

project was divided into two experiments:

Tech Brief

The vision of the Office of

Motor Carriers is to help

move people, goods, and

commercial motor vehicles

on our Nation’s highways in

the most efficient, economical, and crash-free manner

possible. The OMC research

and technology program

focuses on improving safety

in interstate commercial

motor vehicle operations and

serves a trucking and motor

coach industry that carries

more than 40 percent of all

intercity freight.

Studies are conducted in the

following areas: commercial

driver human factors, health,

and performance needs; 

new and emerging driver

and vehicle technologies;

safety-related data collection

and analysis needs; and 

performance-based changes

to the Federal Motor Carrier

Safety Regulations.

The OMC’s technology

research promotes safety by

identifying, collecting, and

communicating information

about technological

advances.

Office of Motor Carrier Research

and Standards

400 Seventh Street, SW

HCS-30; Room 3107

Washington, DC 20590
• To evaluate the validity, sensitivity, and reliability of

specified operator-centered, fatigue-detection

devices for predicting vigilance lapses in alert and

drowsy subjects; and 

• To analyze effects of a combination of auditory and

vibrotactile alerting stimuli on alertness lapses.

Research Methodology

Experiment 1

To obtain estimates of the validity of the selected

drowsiness-detection technologies, researchers 

conducted a controlled laboratory experiment.

Fourteen adult males remained awake in a laboratory

for 42 hours, while working on a computerized test

battery every 2 hours. The tests included a 20-minute

psychomotor vigilance task (PVT), that required 

subjects to sustain attention and respond to a 

randomly appearing light on a computer screen by

pressing a button. PVT performance lapses refer to

the times when a subject failed to respond to the task

in a timely manner (i.e., < 500 msec.); lapses were

recorded each minute throughout the trial and then

totaled for the entire 20 minutes. 

PVT lapses were selected as the validation criterion

variable because driving is a vigilance task requiring

psychomotor reactions, and psychomotor vigilance

has been previously validated to be very sensitive to

fatigue from night work and sleep loss. Thus, PVT

lapses were a valid index for evaluating candidate

technologies.

Six technologies were tested during the research 

program: two electroencephalographic (EEG) algorithms, which detect changes in brain wave activity;

two eye blink monitors, which measure fatigue based

on eye blink activity; a head-position monitoring

device, which detects fatigue based on head motion;

and PERCLOS. These six technologies yielded a total

of nine drowsiness metrics, since both PERCLOS and

the head-position monitoring device had more than

one. PERCLOS had three drowsiness metrics: 

• P70, the proportion of time the eyes were closed at

least 70 percent; 

• P80, the proportion of time the eyes were closed at

least 80 percent; and 

• EYEMEAS (EM), the mean square percentage of the

eyelid closure rating.

Researchers simultaneously recorded PVT performance and the output of these drowsiness-detection

technologies every 2 hours throughout the 42-hour

period. The study design permitted an estimate of the

degree of similarity, or coherence, between PVT 

performance lapses and the alertness/drowsiness 

output of each technology. Each technology was

time-locked to PVT performance to test coherence

between PVT lapses and the technology’s drowsiness

metric. In order for a technology to demonstrate a

high coherence with PVT lapses, its drowsiness 

metric had to demonstrate regular covariation with

performance lapses across the entire 42-hour period,

within and between subjects.

To collect the PERCLOS measure, a low-light, closedcircuit television was used to monitor the subject’s

entire face and to record eyes and eyelids during PVT

performance testing. Video recordings were collected

on all 14 subjects, 10 of which were scorable (in 4

subjects, glare from other equipment prevented 

PERCLOS scoring). Trained scorers viewed the recordings and rated the degree to which the drivers’ eyes

were closed from moment to moment.

Experiment 2

The second part of the study evaluated the effects of

alerting stimuli on PVT performance and PERCLOS

scores. Experiment 2 was conducted 6 months after

Experiment 1 was completed. Researchers wanted to

find out if systematically delivered alerting stimuli

could reduce drowsiness and its consequences for

hypovigilance. Four of the original 14 subjects 

participated in this experiment, which also required

the subjects to remain awake in a laboratory for 

42 hours while performing a pyschomotor vigilance

task every 2 hours.

90

80

70

60

50

40

30

20

10

0

2 4 6 8 10 12 14 16 18 20

13 17 21 01 05 09 13 17 21 01

performance bout

time of day (h)

PVT lapses

8:40 a.m.

Figure 1.

Mean number of psychomotor vigilance 

task performance lapses per 20-minute 

test bout, across 20 test bouts.
During this experiment, auditory and vibrotactile

alerting stimuli were delivered during each 20-

minute PVT performance bout, but were not contingent on actual PVT lapses. Subjects received more 

frequent stimuli during the hours when PVT lapses

were expected to be the greatest, as recorded in

Experiment 1 (i.e., after 22-26 hours of waking).

Vibrotactile stimuli were delivered through a handheld box with a duration of 5 seconds. Auditory 

stimuli consisted of three different recorded messages

received through headsets.

Findings

Experiment 1

The study design effectively induced fatigued 

performance due to the combined influence of sleep

loss and circadian nocturnal phase. As expected, the

average number of PVT lapses increased significantly

during the 42-hour test trial, as shown in figure 1.

Nearly all of the technologies showed potential for

detecting drowsiness by predicting lapses in at least

one subject or a subset of subjects, but only PERCLOS

correlated highly with PVT lapses both within and

between subjects. PERCLOS not only had the highest

coherence of the technologies tested, but correlated

more highly with PVT lapses than did the subjects’

ratings of their own sleepiness. 

Complete data for any given technology were often

available on only a subset of the 14 subjects due to

unscorable results, a limited amount of certain 

equipment, and unreliable data storage/retrievability

for some devices.

Bout to Bout Coherence

Bout-to-bout coherence refers to the correlation

between the total number of visual performance 

lapses in a 20-minute PVT test bout and the drowsiness-detection results from a given technology. Each

subject had 20 test bouts for comparison across the

42-hour test period. Bout-to-bout coherence was 

calculated within each subject using the technology

available for that subject. Table 1 provides boutto-bout coherence measures for individual subjects

and technologies. PERCLOS had the highest average

bout-to-bout coherence of all of the devices.

Minute to Minute Coherence

Minute-to-minute coherence refers to the correlation

between the total number of performance lapses in

each minute of every 20-minute PVT bout and the

results of a given drowsiness detection technology.

Minute-to-minute coherence was consistently lower

than bout-to-bout coherence. (Minute-to-minute 

statistics are not shown in this Tech Brief.) It appeared

that all technologies had a better prediction of PVT

lapses when sampling a longer (20-minute) rather

than briefer (1-minute) period.

Lower Lapsers vs. Higher Lapsers

As expected, all subjects eventually became sleepy

Table 1. 

Bout-to bout coherence for individual subjects (Pearson correlation coefficients).

EEG Head-Position Eye Blink

PERCLOS Algorithms Metrics Monitors

Subject P70 P80 EM 1 2 1 2 1 2

1 0.89 0.92 0.89 * * 0.83 0.82 0.10 *

2 0.85 0.83 0.84 * 0.40 * * 0.71 0.20

3 0.95 0.97 0.95 * * 0.91 0.85 0.90 *

4 0.84 0.83 0.83 * * -0.54 0.20 0.54 0.77

5 0.94 0.94 0.95 0.54 * * * -0.10 *

6 0.95 0.96 0.94 0.57 0.95 * * 0.39 0.54

7 0.92 0.92 0.92 0.36 0.31 * * 0.54 0.32

8 0.55 0.67 0.70 0.66 0.84 * * 0.50 0.85

9 0.78 0.77 0.71 * * 0.23 0.13 0.67 *

10 0.95 0.97 0.95 * * 0.87 0.65 -0.48 *

11 * * * * * * * 0.31 0.79

12 * * * * * * * 0.34 *

13 * * * * * * * 0.14 *

14 * * * * * * * 0.17 *

* Data not available or remaining to be analyzed.
Researcher

This study was performed by

David F. Dinges, Ph.D.,

Department of Psychiatry,

University of Pennsylvania

School of Medicine; and

Richard Grace, Ph.D., Carnegie

Mellon Research Institute.

Contract No. DTNH22-93-D07007.

Distribution

This Tech Brief is being distributed according to a standard

distribution. Direct distribution

is being made to the Regions

and Divisions.

Availability

The study final report is now

available. Copies may be

obtained from the National

Technical Information Service,

Telephone: (703) 605-6000. 

Key Words

driver fatigue, drowsiness,

fatigue, fatigue management,

drowsiness-detection, PERCLOS,

driver performance.

Notice

This Tech Brief is disseminated

under the sponsorship of the

Department of Transportation

in the interest of information

exchange. The Tech Brief provides a synopsis of the study’s

final publication. The Tech

Brief does not establish policies

or regulations, nor does it

imply FHWA endorsement of

the conclusions or recommendations. The U.S. Government

assumes no liability for its 

contents or their use.

and experienced increased PVT lapsing during the 42-hour period of waking. 

However, a subset of the subjects accounted for a disproportionate number of PVT

lapses. Subjects who had an average of 38 or more PVT lapses per bout were referred

to as “higher lapsers” (HL). These subjects would begin lapsing almost immediately

after beginning the task. Subjects with an average of 33 or less lapses per bout were

“lower lapsers” (LL). LL subjects maintained alertness for some time before beginning

to lapse. HL subjects were 42 percent of the subjects, but accounted for 69 percent of

all PVT lapses.

When bout-to-bout coherence was analyzed to compare data for HL subjects and LL

subjects, researchers found that some technologies were inconsistent when assessing

the drowsiness levels between the two groups. For example, one of the eye-blink 

technologies had a significantly lower bout-to-bout coherence for the LL subjects than

for the HL subjects. Importantly, PERCLOS yielded comparably high bout-to-bout 

coherence between the two groups. Despite large inter-subject variability in PVT lapse

rates, PERCLOS appeared to reliably predict lapsing across both groups.

Experiment 2

PVT lapse data and PERCLOS P80 data were compared between Experiments 1 and 2

for each of the four subjects. Comparison of PVT lapses in each minute prior, during,

and following stimulation revealed no evidence that auditory or vibrotactile stimuli

had much effect on PVT lapses. The alerting stimuli provided in this experiment did not

markedly reduce lapses in drowsy subjects beyond the minute in which the alert

occurred, suggesting only a narrow window of opportunity for a drowsy driver to 

safely leave the roadway.

Another notable finding was the intra-subject consistency in the time-course of the 

progression of drowsiness during the 42-hour test periods. Individual subjects exhibited

drowsiness progressions in Experiment 2 that were highly similar to those shown in

Experiment 1, even though the experiments were conducted 6 months apart. Although

there were only four repeat subjects, this finding supports the notion of significant,

durable differences in individual susceptibility to drowsiness during sleep deprivation.

Indeed, each subject had a characteristic “signature” of drowsiness progression. Finally,

PERCLOS reliably correlated with vigilance lapses in this second experiment.

Further Research

Attempts to transition an on-line, automated version of PERCLOS to a realistic 

over-the-road environment are currently underway. NHTSA has developed models of

drowsy driver performance by relating PERCLOS and driving performance using 

over-the-road data from CMV drivers. FHWA and NHTSA are developing plans to

understand the operational safety benefits of a drowsiness detection sensor in actual

driving environments.

Through the ITS Intelligent Vehicle Initiative, NHTSA is currently managing a follow-up

to this study to evaluate the effectiveness of various potential elements of the 

driver-vehicle interface (DVI) of in-vehicle driver alertness monitoring devices. Some of

the components that will be assessed are real-time gauges, informational alarms and

warnings, and alerting stimuli. The study will make recommendations regarding 

optimal DVI design elements for driver alertness monitors.

Reference

Wierwille, WW, Ellsworth, LA, Wreggit, SS, Fairbanks, RJ, Kirn, CL: Research on vehiclebased driver status/performance monitoring: development, validation, and refinement

of algorithms for detection of driver drowsiness. National Highway Traffic Safety

Administration Final Report: DOT HS 808 247, 1994.

October 1998

Publication No. FHWA-MCRT-98-006