An Evaluation of Emerging Driver Fatigue 

Detection Measures and Technologies 

Final Report 

June 2009

FOREWORD 

Operator fatigue and sleep deprivation have been widely recognized as critical safety issues that 

cut across all modes in the transportation industry. FMCSA, the trucking industry, highway 

safety advocates, and transportation researchers have all identified driver fatigue as a highpriority commercial vehicle safety issue. Fatigue affects mental alertness, decreasing an 

individual’s ability to operate a vehicle safely and increasing the risk of human error that could 

lead to fatalities and injuries. Sleepiness slows reaction time, decreases awareness, and impairs 

judgment. Fatigue and sleep deprivation impact all transportation operators (airline pilots, truck 

drivers, and railroad engineers, for example). 

Adding to the difficulty of understanding the fatigue problem and developing effective 

countermeasures to address operator fatigue is the fact that the incidence of fatigue is 

underestimated because it is so hard to quantify and measure. Obtaining reliable data on fatiguerelated crashes is challenging because it is difficult to determine the degree to which fatigue 

plays a role in crashes. Fatigue, however, can be managed, and effectively managing fatigue will 

result in a significant reduction in related risk and improved safety. 

This study focuses on recent developments in mathematical models and vehicle-based operator 

alertness-monitoring technologies. The major objective of this paper is to review and discuss 

many of the activities currently underway to develop unobtrusive, in-vehicle, real-time drowsy 

driver detection and fatigue-monitoring/alerting systems. 

Although this report can be helpful to the general public in understanding driver fatigue detection 

measures and technologies, the report is primarily targeted towards commercial motor carrier 

fleets and their drivers. 

This publication is considered a final report and does not supersede another publication. 

NOTICE 

This document is disseminated under the sponsorship of the Department of Transportation in the 

interest of information exchange. The United States Government assumes no liability for its 

contents or use thereof. 

The contents of this report reflect the views of the contractor who is responsible for the accuracy 

of the data presented herein. The contents do not necessarily reflect the official policy of the 

Department of Transportation. 

This report does not constitute a standard, specification, or regulation. 

The United States Government does not endorse products or manufacturers named herein. Trade 

or manufacturers’ names appear herein only because they are considered essential to the object 

of this document. 

Technical Report Documentation Page 

1. Report No. 

FMCSA-RRR-09-005 

2. Government Accession No. 3. Recipient's Catalog No. 

4. Title and Subtitle: 

An Evaluation of Emerging Driver Fatigue Detection Measures 

and Technologies 

5. Report Date: 

June 2009 

7. Author(s): 6. Performing Organization Code 

Lawrence Barr, Stephen Popkin, and Heidi Howarth 

8. Performing Organization Report No. 

9. Performing Organization Name and Address: 10. Work Unit No. 

Volpe National Transportation Systems Center 

55 Broadway, Kendall Square 

Cambridge, MA 02142 

11. Contract or Grant No.

SA-1J/CB136 

13. Type of Report and Period Covered 

Final Report 

January 2005 – November 2006 

12. Sponsoring Agency Name and Address: 

U.S. Department of Transportation 

Federal Motor Carrier Safety Administration, 

Office of Analysis, Research and Technology 

1200 New Jersey Ave. SE 

Washington, DC 20590 

14. Sponsoring Agency Code

FMCSA 

15. Supplementary Notes: 

Robert J. Carroll was the FMCSA contracting officer technical representative for this project. 

16. Abstract: 

Operator fatigue and sleep deprivation have been widely recognized as critical safety issues that cut across 

all modes in the transportation industry. FMCSA, the trucking industry, highway safety advocates, and 

transportation researchers have all identified driver fatigue as a high priority commercial vehicle safety 

issue. Fatigue affects mental alertness, decreasing an individual’s ability to operate a vehicle safely and 

increasing the risk of human error that could lead to fatalities and injuries. Sleepiness slows reaction time, 

decreases awareness, and impairs judgment. Fatigue and sleep deprivation impact all transportation 

operators (airline pilots, truck drivers, and railroad engineers, for example). 

Adding to the difficulty of understanding the fatigue problem and developing effective countermeasures to 

address operator fatigue is the fact that the incidence of fatigue is underestimated because it is so hard to 

quantify and measure. Obtaining reliable data on fatigue-related crashes is challenging because it is 

difficult to determine the degree to which fatigue plays a role in crashes. Fatigue, however, can be 

managed, and effectively managing fatigue will result in a significant reduction in related risk and 

improved safety. 

This study focuses on recent developments in mathematical models and vehicle-based operator alertness 

monitoring technologies. The major objective of this paper is to review and discuss many of the activities 

currently underway to develop unobtrusive, in-vehicle, real-time drowsy driver detection and fatiguemonitoring/alerting systems. 

17. Key Words: 

Alertness, Circadian, Commercial Motor Vehicle, Driver, 

Drowsiness, Fatigue, Highway Safety, Sleepiness, Truck 

18. Distribution Statement 

19. Security Classif. (of this report)

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages: 

55 

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized. 
SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FRO

Symbol When You Know Multiply By To Find Symbol Symbol When You Know Multiply By 

LENGTH LENGTH 

in inches 25.4 millimeters mm mm millimeters 0.039 incft feet 0.305 meters m m meters 3.28 feeyd yards 0.914 meters m m meters 1.09 Yami miles 1.61 kilometers km km kilometers 0.621 mi

AREA AREA 

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 squft2 square feet 0.093 square meters m2 m2 square meters 10.764 squyd2 square yards 0.836 square meters m2 m2 square meters 1.195 squac acres 0.405 hectares ha ha hectares 2.47 acrmi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 squ

VOLUME VOLUME 

fl oz fluid ounces 29.57 milliliters ml ml milliliters 0.034 flugal gallons 3.785 liters l l liters 0.264 gaft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cuyd3 cubic yards 0.765 cubic meters m3 m3 cubic meters 1.307 cu

MASS MASS 

oz ounces 28.35 grams g g grams 0.035 oulb pounds 0.454 kilograms kg kg kilograms 2.202 poT short tons (2000 lbs) 0.907 megagrams Mg Mg megagrams 1.103 sho

TEMPERATURE (exact) TEMPERATURE (exac

°F Fahrenheit 5(F-32)/9 Celsius °C °C Celsius 1.8 C + 32 

temperature or (F-32)/1.8 temperature temperature 

ILLUMINATION ILLUMINATION 

fc foot-candles 10.76 lux lx lx lux 0.0929 fl foot-Lamberts 3.426 candela/m2 cd/m2 cd/m2 candela/m2 0.2919 

FORCE and PRESSURE or STRESS FORCE and PRESSURE or S

lbf pound-force 4.45 newtons N N newtons 0.225 psi pound-force per square inch 6.89 kilopascals kPa kPa kilopascals 0.145 

* SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
TABLE OF CONTENTS 

LIST OF ACRONYMS .............................................................................................................. IV

1. MATHEMATICAL MODELS OF FATIGUE AND PERFORMANCE........................1

1.1 TW O-PROCESS MODEL.............................................................................................1 

1.2 SLEEP/WAKE PREDICTOR MODEL ........................................................................1 

1.3 SYSTEM FOR AIRCREW FATIGUE EVALUATION MODEL ...............................2 

1.4 INTERACTIVE NEUROBEHAVIORAL MODEL .....................................................3 

1.5 SLEEP, ACTIVITY, FATIGUE, AND TASK EFFECTIVENESS MODEL...............4 

1.6 FATIGUE AUDIT INTERDYNE MODEL..................................................................4 

1.7 CIRCADIAN ALERTNESS SIMULATOR MODEL ..................................................5 

2. TECHNICAL AND DESIGN GUIDELINES ....................................................................7

2.1 USER ACCEPTANCE ASSESSMENT METHODOLOGY .......................................7 

2.2 SCIENTIFIC/ENGINEERING GUIDELINES...........................................................10 

2.3 IMPLEMENTATION ISSUES TO CONSIDER ........................................................11 

3. SUMMARY AND ASSESSMENT OF FATIGUE DETECTION AND 

MONITORING TECHNOLOGIES .................................................................................12

3.1 ATTENTION TECHNOLOGY, INC..........................................................................12 

3.1.1 Device Background, Functionality, and Use .................................................. 12 

3.1.2 Research Findings........................................................................................... 13 

3.1.3 Future Directions ............................................................................................ 13 

3.2 DELPHI ELECTRONICS AND SAFETY .................................................................14 

3.2.1 Device Background, Functionality, and Use .................................................. 14 

3.2.2 Research Findings........................................................................................... 15 

3.2.3 Future Directions ............................................................................................ 15 

3.3 SEEING MACHINES .................................................................................................16 

3.3.1 Device Background, Functionality, and Use .................................................. 16 

3.3.2 Research Findings........................................................................................... 17 

3.3.3 Future Directions ............................................................................................ 18 

3.4 SMART EYE AB ........................................................................................................18 

3.4.1 Device Background, Functionality, and Use .................................................. 18 

3.4.2 Research Findings........................................................................................... 18 

3.4.3 Future Directions ............................................................................................ 19 

i 
3.5 SENSOMOTORIC INSTRUMENTS GMBH ............................................................19 

3.5.1 Device Background, Functionality, and Use .................................................. 19 

3.5.2 Research Findings........................................................................................... 20 

3.5.3 Future Directions ............................................................................................ 20 

3.6 APPLIED SCIENCE LABORATORIES....................................................................20 

3.6.1 Device Background, Functionality, and Use .................................................. 20 

3.6.2 Research Findings........................................................................................... 21 

3.6.3 Future Directions ............................................................................................ 21 

3.7 LC TECHNOLOGIES, INC. .......................................................................................22 

3.7.1 Device Background, Functionality, and Use .................................................. 22 

3.7.2 Research Findings........................................................................................... 22 

3.7.3 Future Directions ............................................................................................ 23 

3.8 JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY...............23 

3.8.1 Device Background, Functionality, and Use .................................................. 23 

3.8.2 Research Findings........................................................................................... 23 

3.8.3 Future Directions ............................................................................................ 23 

3.9 RENSSELAER POLYTECHNIC INSTITUTE..........................................................24 

3.9.1 Device Background, Functionality, and Use .................................................. 24 

3.9.2 Research Findings........................................................................................... 25 

3.9.3 Future Directions ............................................................................................ 25 

3.10 THE GEORGE WASHINGTON UNIVERSITY .......................................................26 

3.10.1 Device Background, Functionality, and Use .................................................. 26 

3.10.2 Research Findings........................................................................................... 26 

3.10.3 Future Directions ............................................................................................ 26 

3.11 FORD MOTOR COMPANY ......................................................................................27 

4. UPDATE ON EMERGING TECHNOLOGIES: ADVANCED SENSOR 

DEVELOPMENT FOR ATTENTION, STRESS, VIGILANCE, AND 

SLEEP/WAKEFULNESS MONITORING .....................................................................31

4.1 FUZZY FUSION OF PHYSIOLOGICAL INDICATORS FOR HYPOVIGILANCERELATED ACCIDENT PREDICTION .....................................................................32 

4.2 INDIVIDUAL DIFFERENCES IN PREDICTED DRIVING PERFORMANCE 

FROM SENSORY DATA AND SUBJECTIVE RATINGS ......................................32 

4.3 ADVISORY SYSTEM FOR TIRED DRIVERS ........................................................33 

4.4 SENSOR FOIL—CONCEPT FOR ASSESSING A DRIVER’S BODY POSTURE 

AND MOVEMENTS ..................................................................................................34 

4.5 OPTALERTTM—A SYSTEM FOR MONITORING EYE AND EYELID 

MOVEMENTS BY INFRARED REFLECTANCE OCULOGRAPHY....................35 

ii 
4.6 NON-INVASIVE MULTI-PARAMETER ESTIMATION OF CIRCADIAN PHASE35 

4.7 THE IMPORTANCE OF NANOSENSORS...............................................................36 

4.8 SLEEP CONTRACTS: A PROCEDURE FOR MANAGING DAY-TO-DAY 

FATIGUE RISK ..........................................................................................................36 

5. CONCLUSIONS .................................................................................................................38

REFERENCES.............................................................................................................................39

LIST OF TABLES 

Table 1. Framework of User Acceptance Elements and Criteria for the Evaluation of New and 

Emerging Vehicle Technologies ......................................................................................8 

Table 2. Technology Comparison and Criteria Evaluation—Scientific Engineering 

Guidelines.......................................................................................................................28 

Table 3. Technology Comparison and Criteria Evaluation—User Acceptance Elements ............29 

LIST OF FIGURES 

Figure 1. Attention Technology DD850 Driver Fatigue Monitor..................................................13 

Figure 2. Delphi DSM Installed in Vehicle ...................................................................................14 

Figure 3. Delphi DSM In-Vehicle Display....................................................................................15 

Figure 4. Seeing Machines faceLAB..........................................................................................17 

Figure 5. faceLAB Installation in Vehicle..................................................................................17 

Figure 6. SMI InSight Driver Monitoring System Setup............................................................20 

Figure 7. ASL ETS-PC II In-Vehicle Eye Tracking System.........................................................21 

Figure 8. RPI Fatigue Monitor Prototype ......................................................................................25 

iii 
LIST OF ACRONYMS 

AECS Average Eye Closure Speed 

ANN Artificial Neural Network 

APL Applied Physics laboratory 

ASL Applied Science Laboratories 

ASTiD Advisory System for Tired Drives 

AVR Am plitude-velocity ratio 

BLINKD Eye-blink duration 

CAS Circadian Alertness Simulator model 

DDDS Drowsy Driver Detection System 

DFM Driver Fatigue Monitor 

DSM Driver State Monitor 

EEG Electroencephalography 

EU European Union 

FAID Fatigue Audit InterDyne model 

FMCSA Federal Motor Carrier Safety Administration 

FOT Field operational test 

GWU George Washington University 

IR Infrared 

KSS Karolinska Sleepiness Scale 

LED Light-em itting diode 

NHTSA National Highway Traffic Safety Administration 

R&D Research and Development 

RPI Rensselaer Polytechnic Institute 

SAFE System of Aircrew Fatigue Evaluation model 

SAFTE Sleep, Activity, Fatigue and Task Effectiveness model 

SDLAT Standard deviation of lateral position 

SEFO Sensor foil 

SENSATION Advanced Sensor Development for Attention, Stress, Vigilance 

and Sleep/Wakefulness Monitoring 

TOVA Test of Variables Attention 

USDOT U.S. Department of Transportation 

iv 
EXECUTIVE SUMMARY 

The primary mission of the Federal Motor Carrier Safety Administration (FMCSA) of the U.S. 

Department of Transportation (USDOT) is to promote the safety of commercial motor vehicle 

transportation and to prevent commercial vehicle-related fatalities and injuries. An important 

element in meeting FMCSA’s strategic safety objectives is an emphasis on the safety 

performance of commercial drivers to ensure they are physically qualified to operate commercial 

motor vehicles safely while staying mentally alert. The authors completed a survey study 

to identify, review, and evaluate emerging driver alertness detection measures, models, and

 technologies that can be used to provide 24-hour fatigue 

monitoring capability. 

Operator fatigue and sleep deprivation have been widely recognized as critical safety issues that 

cut across all modes in the transportation industry. FMCSA, the trucking industry, highway 

safety advocates, and transportation researchers have all identified driver fatigue as a highpriority commercial vehicle safety issue. Fatigue affects mental alertness, decreasing an 

individual’s ability to operate a vehicle safely and increasing the risk of human error that could 

lead to fatalities and injuries. Sleepiness slows reaction time, decreases awareness, and impairs 

judgment. Fatigue and sleep deprivation impact all transportation operators (such as airline 

pilots, truck drivers, and railroad engineers) (Sullivan 2003). 

Adding to the difficulty of understanding the fatigue problem and developing effective 

countermeasures to address operator fatigue is the fact that the incidence of fatigue is 

underestimated because it is so hard to quantify and measure. Obtaining reliable data on fatiguerelated crashes is challenging because it is difficult to determine the degree to which fatigue 

plays a role in crashes. For instance, if a motorist is unharmed in a crash, the resulting increased 

arousal following the incident usually masks the impairment that could assist investigating 

officers in attributing the crash to drowsiness. As a result, sleepiness as a contributing factor in 

motor vehicle crashes is underreported in crash databases that are based on police accident 

reports. Moreover, because sleep deprivation increases the likelihood of attention lapses, 

drowsiness or fatigue may play a role in crashes attributed to other causes as well. An 

investigator may report that a crash was caused by a driver running a red light, whereas in reality 

the crash occurred because the driver was not appropriately vigilant due to his or her state of 

sleepiness and fatigue (Sullivan 2003). And finally, drivers tend to be poor judges of their own 

level of drowsiness; that is, they cannot reliably predict when they are impaired to the point of 

falling asleep at the wheel. 

In addition to the lack of available data and the problem of underestimation, Rosekind (1998) 

identifies three formidable challenges in successfully addressing the issue of fatigue in the 

commercial motor vehicle industry. First of all, operational requirements are diverse; that is, the 

trucking industry incorporates a wide range of driving requirements. Factors such as work 

schedules, duty times, rest periods, recovery opportunities, and respoU to customer needs can 

vary widely. Second, there are considerable individual differences among operators. For 

example, age can have a significant effect on the quality and quantity of sleep an individual 

might obtain or need, on a person’s ability to cope with rotating shift schedules or night work, 

and on the risk for sleep disorders. And finally, the interaction of the principal physiological 

factors that underlie fatigue, namely the homeostatic drive for sleep and circadian rhythms, is a 

v 
complex issue. These challenges preclude a single, simple solution to the fatigue problem and 

suggest that it may be impractical to expect that fatigue can be completely eliminated from 24-

hour transportation operations. Fatigue can, however, be managed, and effectively managing 

fatigue will result in a significant reduction in related risk and improved safety (Rosekind 1998). 

Successfully reducing fatigue-related risks in transportation will require some innovative 

concepts and evolving approaches. Available and emerging technology approaches have great 

potential as a relevant and effective tool to address fatigue. Dinges and Mallis (1998) described 

the following four categories of operator-centered alertness monitoring and fatigue detection and 

prediction technologies: 

1. Readiness-to-perform and fitness-for-duty technologies. These systems attempt to 

assess reaction time, psychomotor tracking, or vigilance and alertness capacity of an 

operator before the work is performed (i.e., prior to the start of the work shift). The 

primary objective of fitness-for-duty tests is to establish whether the operator is fit by 

evaluating his/her functional work capability at the start of a given work shift and the 

ability to work through the duration of the entire duty period. These tests generally fall 

into one of two groups: performance-based tests (e.g., measuring reaction time) or tests 

that measure ocular physiology (Krueger 2004; Hartley et al. 2000). 

2. Mathematical models/algorithm technologies. This approach involves the application of 

mathematical models that predict operator alertness and performance at different times, 

based on interactions of the amount of sleep obtained or missed, on circadian factors, on 

the present workload, and on related temporal antecedents of fatigue (Dinges and Mallis 

1998; Dinges 1997). These highly complex algorithms allow for individual patterns of 

sleep, work, and rest to be entered into a system that will then produce outputs describing 

how levels of performance will be affected by the individual’s sleep/work history. These 

models make up the subclass of operator-centered technologies that includes those devices 

that seek to monitor sources of fatigue, such as how much sleep an operator has obtained, 

and combine this information with a mathematical model designed to predict performance 

capability over a period of time and to predict when future periods of increased sleepiness 

will occur (Dinges and Mallis 1998; Krueger 2004; Hartley et al. 2000). 

3. Vehicle-based performance technologies. These technologies mainly include those that 

measure vehicle performance parameters (e.g., steering movements, vehicle speed, or the 

movements of the vehicle within the lane markers on the roadway), and they infer driver 

behavior by monitoring the continuity of steering wheel movements and/or vehicle speed, 

or by examining the driver’s ability to maintain adequate lane-tracking movements while 

steering the vehicle (Hartley et al. 2000). Vehicle-based performance technologies have a 

sound basis in research, which revealed that vehicle control and driver performance are 

impaired by fatigue. These technologies generally involve no intrusive monitoring 

devices, and the output relates to the actual performance of the driver controlling the 

vehicle (Krueger 2004). 

4. Vehicle-based operator alertness/drowsiness/vigilance monitoring technologies. 

These technologies exemplify the most common approaches currently used to monitor 

driver fatigue, and they monitor—usually on-line and in real time—some biobehavioral 

aspect of the operator such as eye gaze, eye closure, pupil occlusion, head position and 

movement, brain wave activity, heart rate, etc. To be practical and useful as a driver 

vi 
assistance system, these devices must acquire, interpret, and feed back information in 

real-world driving environments. There is a tremendous need to demonstrate the efficacy 

of operator-based on-board fatigue monitoring technologies in a real-world naturalistic 

driving environment, and a significant amount of effort is ongoing in this area. 

This study focuses on recent developments in mathematical models and vehicle-based operator 

alertness monitoring technologies. The major objective of this paper is to review and discuss 

many of the activities currently underway to develop unobtrusive, in-vehicle, real-time drowsy 

driver detection and fatigue-monitoring/alerting systems. An on-board device that monitors a 

driver’s level of fatigue and drowsiness in real time is an important component of a 

comprehensive and effective fatigue management program. People suffering from fatigue exhibit 

certain behaviors that are easily observable from facial features such as the eyelid closures. 

Observable behaviors that typically reflect a person’s level of fatigue include eyelid, pupil, and 

head movement, as well as facial expression (Ji et al. 2004). To make use of these observable 

cues of sleepiness, a considerable amount of research is currently aimed at developing 

noninvasive techniques for assessing a driver’s alertness level through the visual observation of 

his/her physical condition using a remote camera and state-of-the-art technologies in computer 

vision. Recent progress in machine vision research and advances in computer hardware 

technologies have made it possible to measure head pose, eye gaze, and eyelid movement with a 

high degree of accuracy using video cameras. In a conference sponsored by the U.S. Department 

of Transportation on ocular measures of driver alertness, Wierwille (1999) identified computer 

vision as the most promising noninvasive technology for monitoring driver alertness. This report 

also provides a review of several biomathematical models of human fatigue and performance 

that have been developed and commercialized in recent years. 

In the following section, the key features of several mathematical models that have been 

developed to predict operator alertness and performance are summarized. Next, the general 

design guidelines for a practical in-vehicle fatigue detection and monitoring system are outlined 

in Section 3. An overview of several promising technologies that are currently in use or that will 

be available in the near future is presented in Section 4. Some of the important technical features 

and operational characteristics are described in this section, as well as a brief summary of the 

development status for each of the systems. In addition, a methodological framework is offered 

for assessing the user interface needs and acceptance criteria of truck operators and fatigue 

managers for on-board alertness detection and monitoring technologies. And finally, Section 5 

highlights several emerging technologies that were presented in an international conference in 

Basel, Switzerland in May 2006. 

vii 

1. MATHEMATICAL MODELS OF FATIGUE 

AND PERFORMANCE 

Biomathematical models that quantify the effects of circadian and sleep/wake processes on the 

regulation of alertness and performance have been developed in an effort to predict the 

magnitude and timing of fatigue-related respoUs in transportation operations (Dinges et al. 

2004). These models of fatigue and performance typically use input information about sleep 

history, duration of wakefulness, work and rest patterns, and circadian phase to predict 

sleepiness, performance capability, and/or fatigue risk. They are being applied to assess 

vulnerability to fatigue and performance degradation as a function of time of day, to design and 

evaluate work/rest schedules, to plan work and sleep in operational missions, to assist in 

determining the timing of countermeasures to anticipated performance deficits, and for accident 

assessment and policy-making (e.g., hours-of-service regulations). 

In respoU to the escalating interest in biomathematical models to predict operator alertness and 

performance, and also given the rapid development and commercialization of these models, a 

workshop was held in 2002 to examine and review the state-of-the-art fatigue and performance 

models. This section summarizes and describes the key features of seven biomathematical 

models that have been recently developed and/or made commercially available. These models 

were presented at the 2002 Fatigue and Performance Modeling Workshop. 

1.1 TWO-PROCESS MODEL 

The two-process model of sleep regulation (Borbely 1982) is at the core of many 

biomathematical models that address the regulation of fatigue and performance. The model is 

based on the interaction of its two constituent processes, the homeostatic Process S and the 

circadian Process C, which generate the timing of sleep and waking. It assumes that the level of 

the sleep/wake-dependent Process S rises during waking and declines during sleep, and that it 

interacts with a circadian Process C. In addition to the timing of sleep, changes of daytime 

vigilance are accounted for by the interaction of the homeostatic and circadian processes. 

The two-process model has inspired a considerable number of experiments and stimulated the 

establishment of other models of neurobehavioral functions. The attractiveness of the approach is 

due to its physiological appeal and its mathematical simplicity. The model allows the simulation 

of the timing of both sleep and wakefulness and sleepiness and alertness. It was created using 

laboratory data from a number of experiments, including power spectral analysis of 

electroencephalographic (EEG) slow wave activity during non-REM sleep. Model validity has 

been demonstrated in successful simulations under various sleep conditions (Achermann 2004). 

1.2 SLEEP/WAKE PREDICTOR MODEL 

The Sleep/Wake Predictor Model (Akerstedt et al. 2004), also referred to as the Three-Process 

Model of Alertness, is a computer model that uses the timing of work and/or sleep hours as input 

and uses both a circadian and a homeostatic component (amount of prior wake and amount of 

1 
prior sleep) to predict subjective alertness and performance in daily life. The purpose of the 

Sleep/Wake Predictor Model is to provide the following: 

 An integrated and quantitative description of the main factors that affect alertness and 

alertness-related performance 

 A method for predicting alertness from knowledge of sleep/wake patterns or only work 

patterns 

 Quantitative support for evaluation of work/rest schedules in terms of alertness, fatigue, 

performance impairment, and accident risk 

 A quantitative tool for monitoring the state of alertness and fatigue-related error risk 

 An educational tool for teaching sleep/wake regulation and fatigue consciousness 

 A tool for generating research hypotheses regarding sleep/wake regulation and its 

coUquences 

Using subjective alertness data from a number of experiments of altered sleep/wake patterns, the 

model predicts alertness on the basis of four parameters or components: S, C, W, and U. Process 

S is an exponential function representing the time since awakening, and the model assumes an 

exponential drop in alertness during the waking period and an exponential rise in alertness after 

sleep. Process C represents sleepiness due to circadian influences and has a sinusoidal form with 

an afternoon peak at approximately 5:00 p.m. Process W is an exponential sleep inertia factor 

that simulates the wakeup process. The final component in the model is Process U, for 

“ultradian,” that simulates the afternoon dip in alertness. No assumptions are made other than 

that of a normal eight-hour sleep when starting the simulation. 

The target markets identified for use of the Sleep/Wake Predictor Model include researchers in 

sleep/wake regulation and in shift work; occupational specialists and hygienists who need 

information on human physiology and performance in relation to work hours; government 

organizations in charge of safety, health, and work-hour regulations; and company schedulers 

and planning staff who want to evaluate the fatigue and performance effects of particular work 

schedules, such as those used by the road, sea, air, and rail transport sectors. The model software 

interface provides screen output including sleep variables (latency, duration, bedtime), alertness 

and performance curves, accident risk, as well as printer file outputs for all curves and start and 

end times of all sleep periods. 

1.3 SYSTEM FOR AIRCREW FATIGUE EVALUATION MODEL 

The System for Aircrew Fatigue Evaluation (SAFE) Model was developed primarily for use in 

aviation operations based on laboratory experiments and was subsequently adjusted using data 

collected during actual long-haul flights (Belyavin and Spencer 2004). In its initial form, the 

model was based on a series of laboratory experiments that investigated the effects of irregular 

patterns of work and rest on performance during conditions of partial isolation. The experiments 

were specifically designed to provide irregular patterns of work and rest that avoided overall 

sleep deprivation. In addition, they permitted efficient estimates to be made of changes in 

performance at different times of day, during work periods of varying duration. Subjective 

2 
assessments of alertness were obtained by monitoring performance on the Digit Symbol 

Substitution Task (among other tasks) at two-hour intervals throughout the waking periods. The 

model consists of two components, one related to the circadian rhythm and the other to the 

sleep/wake process. 

A significant amount of work has been done to collect information on aircrew fatigue and levels 

of alertness in flight so that the model could be confidently applied to air operations. The diary, 

questionnaire, and sleep log studies that were completed helped to identify factors relevant to 

aviation operations that influence the subjective levels of alertness, in addition to those defined 

in the basic model. These have been incorporated into the latest version of SAFE, and include the 

following: 

 Multiple sectors—A factor related to the number of flights undertaken within a given 

duty period 

 Time on task—A factor related to the duration of the duty period 

 Cumulative fatigue—A factor representing a decline in alertness associated with 

coUcutive duties and with the build-up in fatigue over long tours of duty 

 Early starts—A model correction to allow for reduced levels of alertness throughout duty 

periods starting before 9:00 a.m. 

 Daytime sleep—A factor to reduce the amount of recuperation provided by sleep taken 

outside the normal nighttime period or at unusual times 

 In-flight rest—An algorithm to predict the duration of in-flight sleep as a function of the 

timing and duration of the rest period (i.e., the time that the pilot is away from the 

controls) 

Results of the model are displayed graphically in two-week time frames, and estimates of sleep 

times are an optional display. Alertness levels during duty periods are presented on a color-coded 

display. 

1.4 INTERACTIVE NEUROBEHAVIORAL MODEL 

The Interactive Neurobehavioral Model estimates neurobehavioral performance, which is 

determined by a linear combination of circadian, homeostatic, and sleep inertia components. 

Development of the model was targeted for scientific researchers; the National Aeronautics and 

Space Administration; Department of DefeU schedulers; shift/duty schedulers in the trucking, 

aviation, and railway industries; and agencies that regulate duty hours, such as the Federal 

Aviation Administration. The model’s software works on a PC and allows the user to input both 

light levels and sleep/wake times for the specific schedule to be simulated. Text output files, 

which can be read in any graphical program, are generated to predict performance and alertness 

levels and minimum core body temperature. In addition, the software is able to generate graphs 

of the input protocol and output results (Dinges et al. 2004). 

Validation assessments of the Interactive Neurobehavioral Model have been performed by 

comparing model predictions with neurobehavioral data collected in laboratory studies of human 

3 
subjects involving varying light patterns, simulated jet lag, sleep deprivation, and non-24 hour 

schedules. 

1.5 SLEEP, ACTIVITY, FATIGUE, AND TASK EFFECTIVENESS MODEL 

The Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) Model was developed for use in 

both military and industrial settings, and current users include the U.S. Air Force and the Federal 

Railroad Administration (Balkin et al. 2004). The model has been applied to the construction of a 

Fatigue Avoidance Scheduling Tool (Eddy and Hursh 2001), which is designed to help optimize 

the operational management of aviation ground and flight crews, but is not limited to this 

application. 

The fundamental structure of the SAFTE Model incorporates the three recognized components that 

influence neurobehavioral performance and that are included in most models: homeostatic, 

circadian, and sleep inertia. Overall “effectiveness” is a linear sum of these three components. 

Sleep times and duration are generated based on either real-world data or an “auto sleep” 

algorithm. The model assumes that sleep occurs between 10:00 p.m. and 6:00 a.m.; however, these 

times can be adjusted in the software interface to represent actual sleep schedules. The software 

interface provides the schedule input and predictions (e.g., cognitive and physical performance) in 

graphical and tabular form, parameter tables used for adjusting the model, and description boxes 

for schedules and events. The primary model output is performance effectiveness. 

The current version of the model is based on data collected on college-age students during 

laboratory studies. The model has been validated against literature studies and laboratoryconducted sleep deprivation research, and future plans include validation against actual 

performance of railroad engineers. Currently the SAFTE Model does not include the effects of 

physical work, workload, or level of interest in task. Two additional limitations are that the 

model does not provide an estimate of group variance about the average performance prediction 

and it does not incorporate any individual difference parameters, such as age, chronotype 

(morningness/eveningness), and individual sleep requirements. 

1.6 FATIGUE AUDIT INTERDYNE MODEL 

The Fatigue Audit InterDyne (FAID) Model (Dawson et al. 2004) can be used to quantify the 

work-related fatigue associated with any duty schedule using hours of work (i.e., start/end times 

of work periods) as the sole input. The researchers who developed FAID believe that models 

requiring actual or estimated sleep times as one of several inputs pose difficulties for 

organizations that want to estimate the effects of fatigue in workplace settings (as opposed to 

laboratory-based studies). In work-related situations, reliance on actual sleep times as a model 

input is problematic because it is difficult, costly, and time-consuming to collect objective 

measures of sleep/wake times for every possible duty schedule. Also, reliance of estimated sleep 

times as an input is problematic because it requires subjective judgment. Hence, a major 

advantage of FAID is that it does not require actual or estimated sleep/wake records as an input. 

Rather, it assigns a “recovery value” to time away from work based on the amount of sleep that 

is likely to be obtained in non-work periods, depending on the length of those periods and the 

time of day that they occur. 

4 
In FAID, a duty schedule is viewed as a time-varying function whereby an individual is 

considered to be either working or not working. The model is based on the conceptual notion that 

the fatigue level associated with a duty schedule is determined by a balance between fatigue 

accumulated during work periods and the amount of recovery obtained during non-work periods. 

The fatigue levels of work periods and the recovery values of rest periods are dependent upon 

their length, circadian timing, and “recency” over the seven-day preceding work history. 

Essentially, longer work periods are more fatiguing than shorter work periods, and nighttime 

work periods are more fatiguing than daytime work periods. In addition, the fatigue values of 

work periods and the recovery values of non-work periods are weighted such that more recent 

work and non-work periods make a greater relative contribution to the overall fatigue score than 

less recent periods. 

In summary, the FAID Model was created from existing research collected during controlled 

laboratory experiments on the performance-diminishing effects of wakefulness and the 

restorative effects of sleep. It was developed to be used in workplace settings to quantify the 

work-related fatigue associated with past, current, and proposed duty schedules. Since the model 

uses hours of work as the only input parameter, it enables estimates of the work-related fatigue 

associated with any duty schedule to be made using information that is readily available in 

schedule and/or payroll records. Additional research efforts are being applied to provide further 

validation of FAID in laboratory, simulator, and workplace settings and to modify the model to 

improve its predictive power. 

1.7 CIRCADIAN ALERTNESS SIMULATOR MODEL 

The Circadian Alertness Simulator (CAS) Model was developed by Circadian Technologies, Inc. 

as a practical tool for assessing the risk of diminished alertness in the workplace and for reducing 

the rate of fatigue-related accidents, injuries, and fatalities at work. Applications include work 

schedule optimization, fatigue-related accident investigation, and employee lifestyle education 

training. CAS has been used as a fatigue assessment system in several fatigue management 

projects in the railroad and trucking industries, and it has been proven to be an effective tool in 

employee fatigue reduction programs (Aguirre et al. 2004). 

CAS uses actual work patterns to simulate chronic fatigue levels. Based on the documented work 

schedules of employees, sleep and alertness patterns are estimated and a cumulative fatigue risk 

score is calculated across multiple days or weeks. The current version of CAS is tailored for 

irregular work schedules in the transportation industry. Future versions of the model will include 

regular rotating schedules and jetlag applications consisting of travel across time zones. 

The CAS Model is based on the two-process model of sleep regulation (Borbely 1982), where 

sleep timing and duration are determined by the relationships between circadian factors (phase, 

period, amplitude), homeostatic components (sleep and wake duration), and alertness. In the 

CAS Model, the circadian component is assumed to have a sinusoidal function with a 24-hour 

period. The homeostatic component is assumed to have an exponential increase during sleep and 

an exponential decrease during wakefulness; that is, it recognizes that recuperation is more rapid 

in the initial hours of sleep and that loss of alertness is more rapid during the initial hours of 

wakefulness, with the growth of alertness limited by an upper threshold. 

5 
For the majority of applications in which the precise hours of sleep and wakefulness are not 

known, the CAS software creates an estimated sleep/wake pattern based on the actual work 

pattern of the individual. In cases where actual data on sleep/wake patterns are available, these 

data can be entered directly into the model and the sleep estimation algorithm is bypassed. Using 

either simulated or actual sleep, a continuous alertness curve ranging from 0 (low alertness) to 

100 (high alertness) is computed. In default mode, the CAS software simulates sleep and 

alertness for an average individual; however, the model settings can be adjusted to simulate data 

for specific individual characteristics such as chronotype (morningness/eveningness), habitual 

wakeup time, habitual sleep length, napping capability, or sleep flexibility. Finally, a cumulative 

fatigue score is calculated for each individual or group of individuals based on sleep measures 

and the actual work/rest pattern. The fatigue score ranges from 0 (no fatigue) to 100 (extreme 

fatigue). It quantifies overall fatigue risk across a given period and is intended to be applied as a 

measure for assessment of operational fatigue. 

To test and demonstrate the validity of CAS, it was applied to compute fatigue risk using the 

work/rest and accident data from three trucking operations. Specifically, the cumulative fatigue 

score was used as a metric to help reduce fatigue-related accident risk. The model developers 

reported that the use of such a fatigue risk assessment tool significantly reduced the rate and 

severity of heavy-truck accidents. Also, truck drivers involved in DOT-recordable or high-cost 

crashes were found to have significantly higher CAS fatigue risk scores than accident-free drivers. 

In summary, Circadian Technologies states that CAS meets the following three criteria necessary 

for a mathematical model of fatigue to be operationally useful: (1) It combines the complex 

interactions between factors such as length of shift, time of day, sequence of shift rotations, 

number of coUcutive work days, and other factors that influence employee fatigue into a single 

fatigue risk score on which day-to-day work scheduling and overtime assignment decisions can 

be made; (2) it demonstrates a correlation between the fatigue risk score calculated by the model 

and the rate of fatigue-related accidents in the workplace; and (3) it provides evidence that 

employee work/rest scheduling decisions made by managers using the model can result in a 

reduction in accident rate and costs. 

6 
2. TECHNICAL AND DESIGN GUIDELINES 

The guidelines and criteria used to assess the potential efficacy of the candidate alertness 

monitoring technologies and devices relate to the functional characteristics and operational 

properties of the device or technology. In most cases, this list lacks the technical details 

necessary to be considered a functional requirements specification. Nevertheless, addressing user 

acceptance by employing a structured assessment methodology and evaluating devices against 

scientific criteria is vital to ensure that any proposed device or technology is qualified for its 

intended purpose of monitoring, unobtrusively and in real time, driver alertness and thereby 

potentially helping to mitigate motor vehicle crashes related to driver drowsiness. A list of 

possible implementation issues is also presented. 

2.1 USER ACCEPTANCE ASSESSMENT METHODOLOGY 

Regardless of the projected safety benefit for any given vehicle drowsy detection technology, 

successful deployment is unlikely if users do not deem the device acceptable. Past work in this 

area (Dinges and Mallis 1998; Whitlock 2002; and Bekiaris et al. 2004a) has specified a number 

of criteria that should be considered when evaluating the user acceptance of such technologies. 

Building on these efforts where practical, DOT has conceptualized a methodology to 

systematically assess user acceptance for the purposes of the evaluation of various new and 

emerging vehicle technologies. This methodology is largely based on the NHTSA ITS Strategic 

Plan for 1997–2002 (NHTSA 1997) and has evolved and been expanded iteratively for USDOT 

projects involving field operational tests (FOTs) (e.g., Boyle et al. 2001; Barr et al. 2002). In this 

approach, acceptance is dependent upon the degree to which a driver perceives the benefits 

derived from a system as being greater than the costs. If a system’s potential for safety is not 

perceived to outweigh its costs, it is likely that the system will not be purchased, or will be 

purchased but not utilized. Conversely, if device use is perceived to enhance safety potential and 

driving skill, then there is a chance that those users will feel comfortable engaging in riskierthan-usual driving behavior or otherwise become over-reliant on the system. For a complete 

evaluation of the safety and user acceptance of such technologies, it is important that each of 

these outcomes be assessed. 

The assessment methodology was developed by deconstructing user acceptance into five broad 

conceptual elements: ease of use, ease of learning, perceived value, driver behavior, and 

advocacy. Systematic evaluation of these elements requires collecting a variety of data in order 

to address multiple criteria, each of which is based on human factors principles as applied to 

such technologies. Data sources may include objective measures of performance (MOP), such as 

those related to driving performance, as well as subjective measures, such as surveys to assess 

driver attitudes pre- and post-device exposure. Although laboratory and bench testing may be 

useful in the initial design and evaluation stages, in order to fully address user acceptance and 

obtain useful and representative data, longitudinal field-testing of safety technologies is 

ultimately necessary. 

Field operational tests offer the benefits of a naturalistic driving setting and the ability to assess 

usage and acceptance over time, given prolonged exposure to the technology. As part of 

7 
developing an experimental design, statistical power and effect size should be assessed a priori. 

Additionally, multiple weeks of data collection for both control and experimental driver groups, 

as well as baseline and device exposure conditions, are typically required. Subsequent analysis of 

the objective FOT data, combined with survey analysis to substantiate driver attitudes, integrates 

multiple data sources with the goal of convergent support for the user acceptance of a device. 

Table 1 depicts the user acceptance elements as part of a framework, listing underneath each the 

related criteria for evaluation. As indicated above, the five user acceptance elements are ease of 

use, ease of learning, perceived value, driver behavior, and advocacy. These elements and 

criteria are further described in the text that follows the table, but will likely need to be tailored 

to the functionality of a specific device, as well as the particular goals of the FOT, when used. 

Table 1. Framework of User Acceptance Elements and Criteria for the Evaluation of 

New and Emerging Vehicle Technologies 

Ease of Use Ease of Learning Perceived Value Driver Behavior Advocacy 

Design & usability of 

device controls 

Utility of instructions/ 

training 

Alertness & 

alertness 

management 

enhancement 

Control inputs Willingness to 

endorse 

Use patterns Ability to retain 

knowledge of device 

use 

Driving skill 

enhancement 

Awareness/ 

behavioral 

adaptation 

Purchase 

interest/intent 

Understanding of 

device state 

Time to learn Safety Driving style 

Driver 

accommodation/ 

variations in 

information 

processing 

Health concerns Lifestyle 

Demands on driver/ 

channel capacity 

Confidentiality 

concerns 

Understanding of 

warnings/ 

discriminability of 

alerts 

Tolerance of false or 

nuisance warnings 

Ease of use encompasses the degree to which drivers find a technology understandable, usable, 

and intuitive in its operation and maintenance. Full consideration should be given to the human 

factors design, usability, and maintenance of a device. Testing may initially take place in a 

laboratory setting to ensure the accommodation of inherent variability in driver anthropometry 

and cognitive and physical capabilities, as well as proper operation within various driving 

environments. Further design and usability evaluation in the field and longitudinally will aid in 

refining the functionality of such technologies and assessing driver device use patterns over time. 

Additionally, given various device states, it is critical to determine the degree to which drivers 

understand the capabilities and limitations of a system, its operational parameters, and what 

driver actions are expected in assorted situations. 

8 
The degree to which a device accommodates individual drivers (i.e., individual differences in 

perceptual, information processing, physical, and cognitive skills) by promoting correct 

interpretation of their output must also be evaluated. This includes assessing the demands of 

attending to the output of an in-vehicle device; a safety technology should not contribute to 

driver stress or workload. As device feedback typically takes the form of an auditory warning or 

alert, it is vital that various outputs are easily comprehended, discriminated, and do not conflict 

with those provided by other safety technologies. Finally, in order to facilitate trust in the safety 

benefit of such devices, it is crucial that false and/or nuisance alarms are minimized, while 

maximizing “hits” (i.e., correct assessments of driver status). 

Ease of learning as a part of evaluating user acceptance seeks information regarding how well a 

device is utilized in its intended manner, as well as what is done with such acquired knowledge 

over time. If a device is well designed, then its operation should be congruent with a user’s 

mental model and therefore easily understood, recalled, and retained. Furthermore, device output 

should be both intuitive and easily comprehended. Basic testing of such parameters may be 

conducted in a simulated setting for the evaluation of short-term outcomes. However, only a 

longitudinal study in field conditions allows for the assessment of learning and unintended 

device usage. User understanding of the applications of device feedback, both reactively and 

proactively, in the case of fatigue management planning, is critical for the success of such 

technologies. 

The perceived value element of user acceptance assesses the degree to which drivers perceive a 

safer and/or more alert driving environment as a function of device use. Ideally, the driver is able 

to utilize such safety-enhancing technologies to facilitate alert vehicle operation, in conjunction 

with successfully integrating device feedback more broadly into a personal fatigue management 

program. An additional aspect of perceived value is the degree to which drivers report that these 

innovations enhance driving performance and safety on the road. When assessing these criteria, it 

is important to consider the undesirable outcome of drivers’ inadvertent or purposeful over-reliance 

on such technologies to maintain alert and safe vehicle operation. Perceived value may also be 

impacted to the extent that drivers understand and are informed about device functioning and what 

aspects of driver behavior the device monitors. For example, if real or perceived health risks are 

associated with the technology, drivers will weigh such costs against other perceived benefits. 

Additionally, users may be concerned about data confidentiality to the extent that devices are used 

to monitor, store, and possibly transmit information regarding their driving behavior. 

Alterations in driver behavior may occur as a function of device usage over time. Ideally, these 

changes are intended, positive, and have a permanent impact on safe vehicle operation and driver 

lifestyle. Evaluating a driver’s allocation of cognitive and temporal resources to maintain safe 

driving serves to ensure that driving behavior is not negatively affected by devices requiring 

excessive time and cognitive resources to monitor and react to. Additionally, it is important to 

assess the degree to which driver awareness of and exposure to device feedback over time yields 

behavioral adaptation. Examples include the extent to which device output is integrated into 

driving behavior and the potential benefits and/or risks of using a technology in an unintended 

manner. Further, user acceptance should focus on alterations in driving style (i.e., habits, 

patterns) that are brought about by modifications to sleep/wake patterns as a result of responding 

to device output. More broadly, it is important to assess whether extended exposure to such 

safety devices leads to overall lifestyle changes with regard to fatigue management. 

9 
Advocacy is measured in terms of the extent to which drivers consider endorsing or purchasing a 

safety device, and it is a critical and final component of user acceptance. Ultimately, regardless 

of a potential safety benefit, and even in spite of perceived benefits on the part of the driver, if a 

technology is not attainable by the intended users, it will not succeed in the marketplace. 

Therefore, the willingness of drivers both to purchase a safety device (whether on an individual 

or commercial basis) and to endorse it to others is a vital aspect of successful deployment. 

Where possible, data collection should be planned to include specific objective and subjective 

measures that individually address and map to each criterion and user acceptance element as 

outlined above. The criteria used to evaluate the technologies presented in this paper were based 

on the work of Dinges and Mallis (1996), Whitlock (2002), and Bekiaris et al. (2004a). 

Ultimately, acceptance attitudes are determined based on the outcomes of statistical analyses that 

address each of the tailored and specific criteria, as related to stated research objectives. 

2.2 SCIENTIFIC/ENGINEERING GUIDELINES 

 The device should measure what it is supposed to, operationally (e.g., eye blinks, heart 

rate, etc.). The device should also measure what its manufacturer purports to measure, 

conceptually. For example, a device that claims to measure alertness should provide 

comprehensive evidence of the relationship between the measured variable (e.g., 

PERCLOS, EEG) and the purported variable (alertness). 

 The device should monitor driver behavior in real time. 

 The device should be consistent in its measurement over time, and it should measure the 

same event (operationally and conceptually) for all drivers. 

 The device should be able to operate accurately and reliably in both daytime and 

nighttime illumination conditions. 

 For devices that produce audible warnings, it should be possible to hear the auditory output 

under all driving conditions at a level that is not startling to the user. The volume of 

auditory output should be adjustable over a reasonable range, approximately 50dB to 90dB. 

 The device should be able to operate accurately and reliably in the expected truck cab 

vibration environment. 

 The device should be able to operate over the expected range of truck cab temperature 

and humidity conditions, in both air-conditioned and non-air-conditioned environments. 

 The device should be designed to maximize sensitivity and specificity. In other words, it 

should accurately and reliably detect reduced alertness when it is actually present (i.e., 

minimize missed events or false negatives), and it should accurately and reliably identify 

safe driving and operator vigilance when it is actually present (i.e., minimize false alarms 

or false positives). 

 The device should be able to operate continually and robustly over time with only normal 

maintenance and replacement costs. 

10 
2.3 IMPLEMENTATION ISSUES TO CONSIDER 

In addition to the functional criteria listed above, a number of implementation issues should be 

considered. These issues are not necessarily regarded as requirements against which candidate 

devices are evaluated, but they are important considerations in the successful implementation 

and practical use of a device. The user implementation issues include: 

 The ergonomics implications of device control and display design and location are 

critical. The system should not obstruct the driver’s view of the road scene nor should it 

obstruct vehicle controls and displays required for the primary driving task. It should be 

designed such that allocation of driver attention to the system displays and controls 

remains compatible with the demand of the driving situation. 

 Issues related to privacy of the data acquired by the device (i.e., who has access to data) 

must be considered. 

 Acceptance or “buy-in” from stakeholders—drivers, fleet operators/managers, trucking 

associations, and labor unions—must be obtained. 

 Issues related to automatic vs. manual activation and deactivation of the device must be 

considered. 

11 
3. SUMMARY AND ASSESSMENT OF FATIGUE DETECTION 

AND MONITORING TECHNOLOGIES 

Technological approaches have continued to emerge in recent years that hold promise for 

detecting and monitoring dangerous levels of driver drowsiness. While some enduring systems 

and devices have been available as prototypes for a decade or more, many of these technologies 

are now in the development, validation testing, or early implementation stages. Previous studies 

have reviewed available fatigue detection and prediction technologies and methodologies 

(Krueger 2004; Hartley et al. 2000). This paper builds on previous work by providing updated 

information on state-of-the-art emerging fatigue detection and alertness monitoring technologies. 

Significant advances in video camera and computer processing technologies coupled with robust, 

non-invasive eye detection and tracking systems have made it possible to characterize and 

monitor a driver’s state of alertness in real time under all types of driving conditions. In this 

section, some currently available drowsy driver monitoring devices, as well as technologies that 

will be available in the near future for commercial transport applications, are identified and 

described. This information was compiled using a combination of several methods, including 

literature searches of technical and scientific journals as well as the Internet, e-mail, and phone 

correspondence with researchers, engineers, and product managers involved in developing these 

technologies, and on-site visits and technical demonstrations. In each case, the information 

presented is categorized in terms of the device’s background, functionality, and use; relevant 

research findings; and future directions for device development. It is also noted if no information 

was available to fit a particular heading. 

3.1 ATTENTION TECHNOLOGY, INC. 

3.1.1 Device Background, Functionality, and Use 

Attention Technology, Inc. has designed and developed the DD850 Driver Fatigue Monitor 

(DFM), the only real-time, on-board drowsiness monitor that is currently being tested in an 

extensive field operational test. The DFM is a video-based drowsiness detection system for 

measuring slow eyelid closure. It is designed to mount on the vehicle’s dashboard just to the 

right of the steering wheel, and it provides a continuous real-time measurement of eye position 

and eyelid closure (Grace 2001; Bierman et al. 1999). The camera module is mounted on a 

rotating base to allow the driver to adjust the camera angle. The field of view is large enough to 

accommodate normal head movement. The device has a visual gauge that represents the driver’s 

drowsiness level and emits an audible warning when the driver reaches a preset drowsiness 

threshold. The gauge employs six light-emitting diodes (LEDs); the first three LEDs are amber, 

representing moderate drowsiness levels, and the second three are red, representing severe 

drowsiness. With a three-stage alarm, the device provides real-time, immediate feedback to the 

driver. The DFM is pictured in Figure 1. 

Specifically, the DFM estimates PERCLOS, which is the proportion of time the eyes are closed 

80% or more over a specified time interval. PERCLOS has been demonstrated in both driving 

and non-driving tasks to be a valid indicator of drowsiness and performance degradation due to 

drowsiness. 

12 
Figure 1. Attention Technology DD850 

Driver Fatigue Monitor 

The DFM uses a structured illumination approach to identify the driver’s eyes (Grace). This 

approach obtains two coUcutive images of the driver using a single camera. The first image is 

acquired using an infrared (IR) illumination source that produces a bright-eye image (i.e., a 

distinct glowing) of the driver’s pupils. The second image uses an IR illumination source at 

different wavelength to produce an image with dark pupils. These two images are essentially 

identical except for the brightness of the pupils in the image. A third image calculates the 

difference of these two images, enhancing the bright eyes and eliminating all image features 

except for the bright pupils. The driver’s eyes are identified in this third image by applying a 

threshold to the pixel brightness. The bright pupil effect utilized by the DFM is a simple and 

effective eye tracking approach for pupil detection based on a differential lighting scheme. The 

high contrast between the pupils and the rest of the face can significantly improve the eye 

tracking robustness and accuracy. However, this technique also has disadvantages and 

limitations. 

3.1.2 Research Findings 

The success of the bright pupil technique strongly depends on the brightness and size of the 

pupils, which are often functions of face orientation, external illumination interference, and the 

distance of the subject from the camera (Liu et al. 2002). For real-world, in-vehicle applications, 

sunlight can interfere with IR illumination, reflections from eyeglasses can create confounding 

bright spots near the eyes, and sunglasses tend to disturb the IR light and make the pupils appear 

very weak. Thus, the DFM is intended for use in commercial operations involving nighttime 

driving. 

An on-road FOT of the DFM, co-sponsored by FMCSA and the National Highway Traffic 

Safety Administration (NHTSA), is currently in progress to determine the safety benefits of 

using the device to measure the alertness of commercial truck drivers. The FOT consists of 37 

vehicles equipped with the device and 102 truck drivers, each driving for 17 weeks. 

3.1.3 Future Directions 

None specified. 

13 
3.2 DELPHI ELECTRONICS AND SAFETY 

3.2.1 Device Background, Functionality, and Use 

Delphi is currently developing an automotive-grade, real-time, vision-based driver state 

monitoring system that aims to improve safety by preventing drivers from falling asleep or from 

being overly distracted. According to a corporate white paper (GJ Witt, personal communication, 

2005), the system integrates two products, the ForeWarn Drowsy Driver Alert and the ForeWarn 

Driver Distraction Alert, into a comprehensive Driver State Monitor (DSM). The DSM is a 

computer vision system that uses a single camera mounted on the dashboard directly in front of 

the driver and two IR illumination sources. The installation is shown in Figure 2. This figure 

shows the camera mounted on top of the dashboard directly above the center of the steering 

wheel and one of the IR illumination sources embedded in the dashboard next to the speedometer 

on the right side of the steering wheel. Upon detecting and tracking the driver’s facial features, 

the system analyzes eye closures and head pose to infer fatigue or distraction level. While Delphi 

considered drowsiness and distraction measures other than the ones based on computer vision 

(driving performance and heart/respiration rates, for example), computer vision was regarded as 

the preferred approach because it offers the most direct indication of early oUt of sleepiness and 

distraction, and is also seen as an excellent platform to be shared with other vision-based driver 

assistance applications in the future. 

The DSM evaluates eye closures and the forward attention level of the driver over time using a 

single high-fidelity imaging sensor. The camera and the near-IR illumination sources are 

unobtrusive, and the system provides feedback regarding the driver’s distraction and drowsiness 

levels as well as an audible warning alert. It provides extended eye-closure warning for closures 

longer than 2.5 seconds, and it also provides extended distraction warning for non-forward gaze 

states in excess of 2.5 seconds. The system also features automatic driver detection (selfcalibration) and does not require training. The oUt of fatigue is detected before the driver falls 

asleep at the wheel. 

Figure 2. Delphi DSM Installed in Vehicle 

The fatigue detection algorithm predicts AVECLOS, the percentage of time the eyes are 

estimated to be fully closed over a one-minute interval. AVECLOS is a simple binary measure 

indicating whether or not the driver’s eyes are fully closed. This is a less complex measure of 

14 
drowsiness than PERCLOS, and, as a result, it permits the use of an automotive-grade data 

processor as opposed to a high-grade PC processor required for PERCLOS. Validation testing at 

Delphi has shown very close correlation between AVECLOS and PERCLOS (Pearson 

correlation coefficient = 0.95). 

3.2.2 Research Findings 

DSM is being developed for the automobile and commercial vehicle market; thus it is critical to 

ensure that the system works in a real-world automotive environment. Some of the automotive 

requirements addressed during the design and development of DSM included a wide range of 

illumination conditions and operating temperatures, coverage of the 95th percentile ellipsoid of 

driver head positions, available camera locations, system cost, heat dissipation, allowable levels 

of IR irradiation, and subject variability. The production version of DSM will not have a display, 

though the configuration currently being used for experimental testing and product development 

incorporates a display showing a video image of the driver’s face/head and eye tracking region 

as well as color-coded outputs regarding levels of distraction and fatigue. The display is pictured 

in Figure 3, showing the driver’s face with his eyes closed and the red bars on the feedback 

monitor indicating a severe level of fatigue. The DSM is presently installed in a Volvo 

demonstration vehicle, where it has been shown to be a reliable predictor of drowsiness under all 

illumination conditions and for drivers wearing eyeglasses and most types of sunglasses. 

Figure 3. Delphi DSM In-Vehicle Display 

3.2.3 Future Directions 

None specified. 

15 
3.3 SEEING MACHINES

3.3.1 Device Background, Functionality, and Use 

Seeing Machines is engaged in the research, development, and production of advanced computer 

vision systems for research in human performance measurement, advanced driver assistance systems, 

transportation, biometric acquisition, situational awareness, robotics, and medical applications. As 

described in product information on their website (www.seeingmachines.com/facelab.htm), their 

signature product, faceLAB, provides head and face tracking as well as eye, eyelid, and gaze 

tracking for human subjects using a non-contact, video-based sensor. faceLAB has a flexible and 

mobile tracking system and a wide field of view that enables analysis of naturalistic behavior, 

including head pose, gaze direction, and eyelid closure, in real time under real-world conditions 

without the use of wires, magnets, or headgear. Thus, it is a tool that has great promise for analyzing 

driver behavior in simulators and test vehicles. 

Drowsiness can be determined in real time with faceLAB’s comprehensive blink analysis and 

PERCLOS assessment, including delivery of raw data on the details of eyelid behavior. 

Measurements are taken on eyelid position, rather than bright pupil or corneal occlusion. Instead 

of using traditional corneal reflection techniques, input is obtained using a stereo camera pair. In 

addition, faceLAB allows each subject to be automatically calibrated, and the data are reusable 

in subsequent experiments. 

Seeing Machines’ faceLAB has been employed extensively as a PC-based research tool. It is 

installed in several driving simulators, including the National Advanced Driving Simulator at the 

University of Iowa and at the University of Minnesota, and it has been used in test track 

experiments at NHTSA’s Vehicle Research Test Center. In addition, a single-camera system is 

installed on a Volvo Safety Truck demonstration vehicle. The PC-based version of faceLAB is 

shown in Figure 4, which depicts a person facing a computer monitor that displays his face and a 

close-up view of his eyes along with six graphs showing the real-time device output. The two 

cameras are attached to a mounting bracket just below the computer monitor and are aimed 

directly at the person’s face. The two-camera system installed in a vehicle for road testing is 

shown in Figure 5. The two cameras and the IR illuminator are clearly visible mounted on the 

dashboard above the steering wheel. 

16 
Figure 4. Seeing Machines faceLAB

Figure 5. faceLAB Installation in Vehicle 

3.3.2 Research Findings 

faceLAB operates and maintains tracking integrity through a range of lighting and movement 

conditions. It is not sensitive to sudden movement or obstruction, and it recovers immediately if 

a subject leaves the field of view. The device reportedly works in bright sunlight or at night, with 

subjects close to the camera or several feet away. It also works with or without contact leUs and 

with most eyeglasses. For example, if the subject is wearing sunglasses and his eyes cannot be 

seen, faceLAB can still find the head pose, eyes, and mouth, and can, according to Seeing 

Machines, continue to produce reliable data. The device reportedly works very well in the 

simulator environment. 

17 
3.3.3 Future Directions 

Currently, Seeing Machines is developing a prototype in-vehicle system for Volkswagen that 

will include a warning alert feature and will be tested under real-world conditions. The prototype 

will be available in approximately one year. 

3.4 SMART EYE AB 

3.4.1 Device Background, Functionality, and Use 

Smart Eye AB, headquartered in Göteborg, Sweden, was formed in 1999 with a mission to provide 

the general public, industry, and research institutions with computer-vision-based software that 

enables computers and machines to detect human face/head movement, eye movement, and gaze 

direction. Online product information in 2006 (www.smarteye.se/downloads.aspx) indicated that 

Smart Eye has developed and tested Smart Eye Pro 3.0, a remote and unobtrusive sensor that 

measures face and eye movement for a variety of applications, including transportation safety 

research (drowsiness, alertness) and simulators. 

Smart Eye Pro 3.0 is a machine vision system that estimates head pose using a simple and robust 

method based on tracking of individual facial features and a three-dimensional (3D) head model. 

The initial head model is generic and adapted to the user. The head modeling is initialized using 

a simple step-by-step application in which the user benchmarks basic and prominent features 

(e.g., nose, mouth) in the face images. Once the system runs in tracking mode, the 3D feature 

locations are determined from their previous locations and a motion model. If tracking is 

suddenly lost, a fast face detection procedure allows the system to quickly re-acquire the 

subject’s face and resume tracking. While the face is being tracked, gaze direction and eyelid 

positions are determined by combining image edge information with 3D models of the eye and 

eyelids. The system is flexible and can accommodate up to four high-speed 60-Hz cameras. 

Thus, a major advantage is that eye and head tracking can continue even if one camera is fully 

occluded or otherwise non-operational. This also allows for large head motions (translation and 

rotation). Another important property of the system is that it is easily adaptable to various 

measurement situations with flexible camera mount positions. Smart Eye manufactures the 

image-processing based sensor but does not develop an algorithm that monitors drowsiness. To 

support a validation test, Smart Eye could provide a PERCLOS algorithm along with their 

camera system. 

3.4.2 Research Findings 

Smart Eye Pro 3.0 is able to provide accurate eye tracking under all illumination conditions, 

from bright sunlight to complete darkness, by using an innovative active IR lighting approach 

that suppresses the effects of sunlight and shadows. It also reportedly works well for drivers who 

have low-contrast eyes and who wear contact leUs, eyeglasses, and most types of sunglasses. 

A system consisting of a single camera and PC-based processor is currently being tested in 

Europe by Volvo, Volkswagen, BMW, and all the European truck manufacturers. Smart Eye 

reports that they have been receiving very favorable reviews from these on-road field tests with 

18 
regard to the system’s ability to monitor the driver’s eye and head movement and gaze direction 

in both sunlight and darkness, as well as for drivers wearing eyeglasses. 

3.4.3 Future Directions 

Smart Eye’s long-term plan is to go into serial production and have the device available as an 

option for the automobile market in three years. 

3.5 SENSOMOTORIC INSTRUMENTS GMBH 

3.5.1 Device Background, Functionality, and Use 

SensoMotoric Instruments GmbH (SMI), based in Berlin, Germany, is currently developing 

InSight, an advanced, non-invasive, computer-vision-based operator monitoring system that 

measures head position and orientation, gaze direction, eyelid opening, and pupil position and 

diameter. Product information provided to the authors via e-mail (E. Schmidt, personal 

communication, 2005) shows that it is a high-speed system that uses a sampling rate of 120 Hz 

for head pose and gaze measurement, 120 Hz for eyelid closure and blink measurement, and 60 

Hz for combined gaze, head pose, and eyelid measurement. To determine a driver’s state of 

alertness, InSight calculates PERCLOS. The system is purported to employ automatic and 

robust tracking algorithms that allow accurate driver state monitoring under all lighting 

conditions from sunlight to darkness. The system also features very simple and fast user 

calibration to accommodate multiple drivers. 

Figure 6 shows the InSight system setup for in-vehicle driver monitoring. The photo is taken 

from the passenger side of the vehicle and shows the driver seated with his hands on the steering 

wheel and the camera and IR illumination hardware mounted on the dashboard above the 

steering wheel. The present configuration is a PC-based system that uses one high-speed, 

dashboard-mounted camera and three sources of low-intensity controlled IR illumination. The 

system provides a comparatively large tracking range because it uses a small tracking area on the 

face (eye and nose features only) for measuring head/face position and orientation. Their system 

is suitable for 24-hour operation. It measures head position and orientation, eye position, and 

eyelid closure for a more refined prediction of PERCLOS, and subsequently driver drowsiness. 

19 
Figure 6. SMI InSight Driver Monitoring System Setup 

3.5.2 Research Findings 

The eye closure measurement accuracy is 1 mm. As mentioned, InSight is currently a PCbased research platform, although extensive studies have been conducted with truck drivers and 

passenger cars. SMI has also conducted studies comparing simulator and on-road performance to 

detect drowsiness and micro-sleeps. They have been working with Volkswagen and BMW to test 

the device in the real-world driving environment. 

3.5.3 Future Directions 

SMI’s plan was to partner with an OEM by the summer of 2005 to develop the technology into a 

production-quality, automotive-based drowsiness detection and monitoring system. 

3.6 APPLIED SCIENCE LABORATORIES 

3.6.1 Device Background, Functionality, and Use 

Applied Science Laboratories (ASL) has been designing and developing eye tracking systems 

and devices for over 30 years for applications in fields such as human factors and ergonomics, 

marketing research, psychology and cognition, and education and training. According to product 

information provided to the authors (V. Salem, personal communication, 2005) their video-based 

eye trackers utilize the pupil/corneal reflection technique for measuring eye movements. In most 

applications, ASL devices operate with a bright pupil image. This has been chosen because of 

the advantages it provides over a dark pupil image. ASL has found that the bright pupil image is 

less affected by eyelashes, light-colored eyes, dark environments, contact leUs, eyeglasses, and 

distance from the camera. On the other hand, a disadvantage of the bright-pupil technique is that 

it is not as robust in an outdoor environment, since sunlight can interfere with infrared 

illumination. In those cases, ASL uses a specially enhanced dark-pupil technique in which the 

optics are designed specifically for outdoor use and are robust even in bright sunlight. 

20 
Many of ASL’s eye tracking devices for outdoor applications (including in-vehicle driving 

purposes) utilize a camera mounted on an adjustable, lightweight headband. These devices 

provide unrestricted freedom of movement and can accommodate all types of subjects including 

those wearing sunglasses, but nevertheless may be considered rather obtrusive. ASL does, 

however, manufacture a remote, non-contacting eye tracking system better suited for in-vehicle, 

real-time monitoring of driver alertness. This system, the ETS-PC II, is pictured in Figure 7, 

which shows the eye tracker secured to the dashboard above the center console to the right of the 

steering wheel. 

The ASL technology that is developed for in-vehicle transportation applications is primarily a 

PC-based research instrument. The camera-based system is non-invasive, but the computer must 

be present in the vehicle to process the data. Also, the eye tracking system does not include an 

algorithm for detecting and alerting a drowsy driver. 

3.6.2 Research Findings 

The ETS-PC II, is designed to provide a full 90 horizontal and 45 vertical field of view for 

accurate measurement of the driver’s eye movement and line of sight. The eye is reliably 

reacquired if the head is turned over 90 and then returns. It features automatic and continuous 

search for the eye as well as automatic head movement compensation and parallax correction. 

Furthermore, the system can reportedly integrate eye gaze data with a time stamp to other data 

signals from the vehicle, such as steering position or vehicle speed. ASL states that the device 

works in all driving conditions from bright sunlight to total darkness. 

3.6.3 Future Directions 

None specified. 

Figure 7. ASL ETS-PC II In-Vehicle Eye Tracking System 

21 
3.7 LC TECHNOLOGIES, INC. 

3.7.1 Device Background, Functionality, and Use 

LC Technologies, Inc. has developed an eye tracking technology that is both an eye-operated 

computer for control and communication and a device for monitoring and recording eye motion 

and related eye data (Cleveland 1999). Product information available online (www.eyegaze.com) 

in 2006 indicate that the technology, called the Eyegaze Analysis System, is a hands-off, 

unobtrusive, remote human-computer interface that can be used to track a user’s gaze point or 

allow an operator to interact with his or her environment using only his or her eyes. The device 

reportedly has a number of human factors research applications, including monitoring mental 

alertness and determining fitness for duty. 

The Eyegaze Analysis System is a tool for measuring, recording, playing back, and analyzing 

what a person is doing with his/her eyes. It includes all the basic video equipment, computer 

hardware, and Eyegaze software necessary to develop and run custom eye tracking applications. 

In the laboratory, gaze point tracking measurements are made unobtrusively via a remote video 

camera mounted below a computer monitor. The Eyegaze Analysis System tracks the subject’s 

gaze point on the screen automatically and in real time, with gaze point measurements made at a 

rate of 60 Hz. Gaze direction is determined using the Pupil Center Corneal Reflection (PCCR) 

method. A small, low-power infrared LED located at the center of the camera lens illuminates 

the eye and provides a direct reflection off the cornea of the eye. 

For its use as a drowsy driver detection and warning system, the Eyegaze Analysis System can 

be housed in the vehicle cab to warn and alert drivers when they are becoming drowsy and losing 

alertness on the road. The goal of the system is to monitor the driver’s eye point-of-regard, 

saccadic and fixation activity, and percentage eyelid closure reliably, in real time, and under all 

anticipated driving conditions. Recent effort at LC Technologies has been directed toward 

developing the camera/sensor instrument, but no significant work has been done to advance the 

Eyegaze Analysis System for transportation applications. 

3.7.2 Research Findings 

The concept for the Driver Eye Monitor is for the miniature video camera to be mounted on a 

motorized gimbal that keeps the camera pointed at and focused on the eye. The camera must 

have a clear, unobstructed view of the subject’s eye to make an accurate gaze measurement. In 

most cases, eye tracking works with eyeglasses and contact leUs since the calibration procedure 

accounts for the refractive properties of the leUs. However, eyeglasses tilted significantly 

downward, hard contact leUs, and sunglasses may cause problems for the device by reflecting 

the LED off the surface of the glass back into the camera. In addition, sunlight contains high 

levels of infrared light and obscures the lighting from the device’s LED, degrading the image of 

the eye. 

It has been tested in a real-world environment on LC Technologies’ RV platform; however, the 

purpose of the test was to track the eye movements of the passenger, who was attempting to 

operate a computer with the eyes only in a hands-free mode. The device has not been validated 

against any measures of driver drowsiness such as PERCLOS or eye gaze behavior. It reportedly 

22 
works well in sunlight and for drivers who wear most types of sunglasses (reflective sunglasses 

present a problem). 

3.7.3 Future Directions 

None specified. 

3.8 JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY 

3.8.1 Device Background, Functionality, and Use 

Johns Hopkins University Applied Physics Laboratory (2005) is developing a small sensor 

system that will alert drivers when they are in danger of falling asleep at the wheel or 

experiencing some level of impairment from fatigue. The Drowsy Driver Detection System 

(DDDS) is a device containing a transceiver similar to those used in automatic door entry 

systems that operate at safe microwave frequency and power levels. It detects drowsiness prior to 

the driver’s falling asleep. It issues warnings that can begin as the driver becomes sleepy and 

intensify as the system detects increasing drowsiness. The system is non-invasive and it collects 

specific driver data under all conditions, including bright sunlight, through the use of a Doppler 

radar system. The technique can monitor and quantitatively measure the speed, frequency, and 

duration of eyelid closure, rate of heartbeat and respiration, and pulse rate by analyzing the 

Doppler components in the reflected signal. 

The final design size of the DDDS is projected to be approximately 3×3×2 inches for the 

transceiver (small enough to fit above the windshield) and about 1×2×3 inches for the support 

electronics. Based on the relatively simple design of the system and the projected low cost of 

production, the device is expected to be cost-effective to use. This technology is appealing from 

the standpoint of its ability to unobtrusively monitor driver behavior under all types of driving 

conditions and for all drivers. However, the technology has not advanced beyond the basic 

research phase of development. 

3.8.2 Research Findings 

Testing has shown good correlation between measurements obtained with the DDDS and those 

taken using the validated PERCLOS methodology. But it should be noted that these tests were 

performed on only one subject. 

3.8.3 Future Directions 

Substantial additional work to develop the algorithm, collect data in the laboratory on several 

experimental subjects, and correlate the device output to a validated measure of drowsiness such 

as PERCLOS is required before a prototype system will be ready for real-world testing. 

23 
3.9 RENSSELAER POLYTECHNIC INSTITUTE 

3.9.1 Device Background, Functionality, and Use 

Researchers at Rensselaer Polytechnic Institute (RPI) have developed a prototype computer 

vision system for monitoring driver vigilance (Ji et al. 2004; Ji et al. 2005; Ji and Yang 2002). 

The main components of the system include a remotely located charge coupled device (CCD) 

video camera, a specially designed hardware system for real-time image acquisition and for 

controlling the illuminator and alarm system, and various computer vision algorithms for 

simultaneous, real-time non-intrusive monitoring of various visual bio-behaviors that typically 

characterize a driver’s level of vigilance. 

Many current approaches and systems that use computer vision technologies for extracting 

characteristics of a driver focus on using only a single visual cue such as eyelid movement, line 

of sight, or head orientation to characterize the driver’s state of alertness. The system relying on 

a single visual cue may encounter difficulty when the required visual features cannot be acquired 

accurately or reliably. For example, eyeglasses can cause glare and may be opaque to light, 

making it impossible for a camera to monitor eye movement. Another potential problem with the 

use of a single visual cue is that the obtained visual feature may not always be indicative of a 

driver’s state of mental alertness. For example, an irregular head movement or gaze direction 

(e.g., briefly looking behind or at the rear-view mirror) may potentially produce a false alarm for 

such a system. RPI contends that all those visual cues, however imperfect they may be 

individually, if combined systematically can reduce the uncertainty and ambiguity in the 

information from a single source and provide an accurate characterization of a driver’s level of 

vigilance. Thus, the system they are developing can simultaneously and unobtrusively monitor in 

real time several visual behaviors that typically characterize a person’s level of alertness while 

driving. These visual cues include eyelid and gaze movement, pupil movement, head movement, 

and facial expression. The parameters computed from these visual cues are subsequently 

combined probabilistically using a Bayesian Networks model to form a composite index that can 

robustly, accurately, and consistently characterize a driver’s alertness level. 

Eye detection and tracking are accomplished using active near-infrared illumination to brighten 

the subject’s face to produce the bright pupil effect. This ensures a high-quality image under 

varying real-world conditions, including poor illumination, daylight, and darkness, and it 

minimizes any interference with the subject’s ability to drive. For implementation of their system 

in a vehicle, RPI proposes to use two miniature CCD cameras embedded on the dashboard. The 

first camera is a narrow-angle camera focused on the driver’s eyes to monitor eyelid and gaze 

movements, and the second camera is a wide-angle camera aimed at the driver’s head to track 

and monitor head movement and facial expression. There are several ocular measures to 

characterize eyelid movement, such as eye blink frequency, eye closure duration, eye closure 

speed, and PERCLOS. 

The RPI computer vision system focuses on real-time computation of PERCLOS and average 

eye closure speed (AECS) to characterize eye movement. While PERCLOS is purported to be 

the most valid ocular parameter for measuring driver drowsiness, studies at RPI indicate that 

AECS differs for drowsy versus alert subjects and is therefore also a potentially useful and valid 

indicator of driver drowsiness. In addition to measuring eyelid movement, RPI has developed 

24 
methods for determining three-dimensional face (head) orientation, eye gaze estimation and 

tracking, and facial expression (e.g., mouth movement, yawning). All these measures are 

potentially valuable in detecting a driver’s level of alertness and attention. The prototype of 

RPI’s fatigue monitor, showing the images from the eye and face cameras, as well as the realtime output of the composite fatigue index, is presented in Figure 8. 

3.9.2 Research Findings 

Experimental studies in a simulated (laboratory) environment under various illumination 

conditions—using eight subjects (both in a rested, alert condition and in a sleep-deprived 

condition) of different genders, ages, and ethnic backgrounds—were conducted to validate the 

drowsiness monitoring system. The validation consisted of two parts; the first involved the 

validation of the measurement accuracy of their computer vision techniques, and the second 

assessed the validity of the parameters that were computed to characterize drowsiness. Subjects 

were asked to perform a Test of Variables Attention (TOVA), a 20-minute psychomotor reaction 

test requiring the subjects to sustain attention and respond to a randomly appearing light on a 

computer screen by pressing a button. From the validation tests, RPI researchers found that: (1) 

PERCLOS and TOVA respoU time were closely correlated; (2) their fatigue monitor accurately 

measured PERCLOS, a valid characterization and predictor of drowsiness; and (3) the composite 

fatigue index computed by their fatigue algorithm was highly correlated with the subject’s 

TOVA respoU time. On the basis of these experimental results, it was concluded that this 

monitor system is reasonably robust, reliable, and accurate in characterizing and predicting 

human drowsiness. 

25 

Figure 8. RPI Fatigue Monitor Prototype 

3.9.3 Future Directions 

RPI has conducted both basic research and algorithm development. Their technological approach 

and prototype are promising, but limited to the laboratory environment. To advance their 

technology to a workable, on-board, automotive-grade system, RPI would need to collaborate 

with an industry partner. 
3.10 THE GEORGE WASHINGTON UNIVERSITY 

3.10.1 Device Background, Functionality, and Use 

Researchers at The George Washington University’s (GWU) Center for Intelligent System 

Research are developing a method for detecting drowsiness in drivers based on an Artificial 

Neural Network (ANN) (Sayed et al. 2001; Eskandarian and Sayed 2005). The ANN observes 

the steering angle patterns and classifies them into drowsy- and non-drowsy-driving intervals. 

Unlike other drowsiness detection systems that are based on measuring driver physical and/or 

physiological data (EEG data and eye movements, for example) and that require obtrusive wires, 

cameras, monitors, or other devices attached to or aimed at the driver, the ANN method 

measures and analyzes vehicle performance output data only. Thus, because of the non-invasive 

nature of this method and because it is not dependent on environmental conditions such as 

ambient light, neural network technology is potentially a very practical method for in-vehicle 24-

hour fatigue/drowsiness monitoring. 

Neural networks are very sophisticated nonlinear modeling techniques capable of modeling 

extremely complex functions. Neural networks are relatively easy to use compared with other 

types of nonlinear statistical models. With their ability to derive meaning from complicated or 

imprecise data, they can be used to extract patterns and detect trends that are too complex to be 

noticed either by humans or by other computer techniques. 

Steering wheel movements have been of particular interest in studies of driver alertness. Various 

researchers have shown that changes in steering activity, computed over fixed time intervals, are 

correlated with a driver’s state of impairment. The hypothesized relationship between driver state 

of alertness and steering wheel position is that, in an alert state, drivers make small amplitude 

movements of the steering wheel, corresponding to small adjustments in vehicle trajectory; in a 

drowsy state, on the other hand, these steering wheel movements become less precise and larger 

in amplitude, resulting in sharp changes in trajectory. 

3.10.2 Research Findings 

GWU researchers trained and tested the ANN by conducting a driving simulator experiment. 

Twelve drivers, each under a different level of sleep deprivation, were tested. Results showed 

that the ANN method detected drowsiness with an accuracy of 90%. On several occasions during 

the simulator experiment, drivers fell asleep while driving and crashed the vehicle. For all the 

crashes that occurred, a data set representing 3.5 minutes of driving immediately preceding the 

crash was presented to the ANN. The ANN detected drowsy-driving behavior before the crash 

occurred and generated a warning in all cases. 

3.10.3 Future Directions 

This technology is still in the early research stage of development. Based on the work completed 

thus far, researchers at GWU have identified and recommended the following areas for further 

research: 

26 
27 

 Conduct further refining and validation of the algorithm 

 Capture an individual driver’s steering activity while drowsy 

 Conduct additional simulator experiments to validate the algorithm, test additional road 

conditions, and test a more diversified group of drivers 

 Test the ANN technology on the road in an instrumented vehicle, and refine the 

algorithm based on the road test data 

 Conduct research on warning systems integrated with the detection system 

3.11 FORD MOTOR COMPANY 

Ford Motor Company is currently teaming with Volvo to develop an in-vehicle unobtrusive 

fatigue monitoring system. The system is being developed for use in passenger vehicles, but 

could be adapted to the commercial vehicle platform. Ford is currently in the patent application 

process and could not, therefore, disclose any details of the system’s technology approach or 

operating principles. They did state, however, that their device would be ready for on-road 

testing in about one year. 

The framework presented in the two tables below provides a straightforward means of 

comparing and contrasting the various technologies described in this paper. As an example, the 

technologies, presented in the table columns, are evaluated against a number of criteria listed in 

the rows—the major scientific/engineering guidelines, in Table 2, and the major user acceptance 

elements in Table 3. The single, capital letters in the grids indicate whether the device meets the 

criterion: a Y means “Yes,” the device meets the criterion; an N means “No,” the device does not 

meet the criterion; and a U is for “Undetermined,” meaning that that the criterion has not been 

scientifically evaluated and more data are needed. 
28 

Table 2. Technology Comparison and Criteria Evaluation—Scientific Engineering Guidelines 

(Key—to whether criterion has been met: Y=Yes, N=No, U=Undetermined) 

Scientific/ 

Engineering 

Guidelines 

Criteria 

Attention 

Technology 

Driver 

Fatigue 

Monitor 

Delphi 

Driver 

State 

Monitor 

Seeing 

Machines 

faceLAB

Smart 

Eye 

Pro 3.0 

SMI 

InSight

ASL 

ETSPC II 

LC Tech. 

Eyegaze 

Analysis 

System 

Joh

Hopk

AP

DDD

Environmental Device should 

operate reliably and accurately in all illumination conditions 

N U U U U U U U 

Environmental Device should 

operate over expected range of truck cab temperature, humidity, and vibration conditions 

Y Y Y Y Y Y Y U 

Reliability Device should be 

designed to minimize both missed events and false alarms 

U U U U U U U U 

Anthropometric Device should 

accommodate multiple drivers with minimal re-calibration 

Y Y Y Y Y U U U 

Engineering Design 

Device should be robust and require only normal maintenance and replacement 

U U U U U U U U 
29 

Table 3. Technology Comparison and Criteria Evaluation—User Acceptance Elements 

(Key—to whether criterion has been met: Y=Yes, N=No, U=Undetermined) 

User 

Acceptance 

Elements

Criteria 

Attention 

Technology 

Driver 

Fatigue 

Monitor

Delphi 

Driver 

State 

Monitor

Seeing 

Machines 

faceLAB

Smart 

Eye 

Pro 3.0

SMI 

InSight

ASL 

ETSPC II

LC Tech. 

Eyegaze 

Analysis 

System

Joh

Hopk

AP

DDD

Ease of Use Device must not be 

invasive Y Y Y Y Y Y Y Y 

Ease of Use Device must 

accommodate corrective eyeglasses and most types of sunglasses 

N Y Y Y Y U U U 

Ease of Use Device should 

present a warning alert to the driver 

Y Y Y Y Y N N N 

Ease of Use Device must 

monitor driver behavior in real time 

Y Y Y Y Y Y Y Y 

Ease of Use Device should 

require minimal training Y Y Y Y Y Y Y Y 

Ease of Learning 

Assessment of the time it takes users to feel proficient with the device 

U U U U U U U U 

Ease of Learning 

Assessment of user ability to retain and recall information regarding device functionality 

U U U U U U U U 

Perceived Value 

Device should provide feedback to driver regarding alertness level 

Y Y U U U N N U 
30 

User 

Acceptance 

Elements

Criteria 

Attention 

Technology 

Driver 

Fatigue 

Monitor

Delphi 

Driver 

State 

Monitor

Seeing 

Machines 

faceLAB

Smart 

Eye 

Pro 3.0

SMI 

InSight

ASL 

ETSPC II

LC Tech. 

Eyegaze 

Analysis 

System

Joh

Hopk

AP

DDD

Perceived Value 

Is there a perceived safety benefit or increased risk associated with device use? 

U U U U U U U U 

Advocacy Assessment of 

user’s intent to purchase device 

U U U U U U U U 

Advocacy Willingness of user 

to recommend device use to others 

U U U U U U U U 

Driver Behavior 

Device must not distract the driver from driving task or from responding to other safety devices 

Y Y Y Y Y Y Y Y 

Driver Behavior 

Assessment of behavioral adaptation to the device over time 

U U U U U U U U 
31 

4. UPDATE ON EMERGING TECHNOLOGIES: 

ADVANCED SENSOR DEVELOPMENT FOR ATTENTION, 

STRESS, VIGILANCE, AND SLEEP/WAKEFULNESS 

MONITORING 

The Advanced Sensor Development for Attention, Stress, Vigilance and Sleep/Wakefulness 

Monitoring (SENSATION) project is a four-year, 17-million-Euro effort focused on exploring a 

wide range of micro and nanosensor technologies, with the aim of achieving unobtrusive, costeffective, real-time monitoring, detection, and prediction of human physiological state in relation 

to wakefulness, fatigue, and stress anytime, everywhere, and for everybody (Bekiaris et al. 

2004b). The SENSATION Consortium consists of 45 partners in academia and industry from 20 

countries in the European Union (EU), Australia, and China covering the following research 

areas: brain-sensing, neurosensing, computational neuroscience, signal processing, materials 

science, optoelectronics, microelectronics, intelligent user interfaces, telematics, data fusion, 

sleep research, human physiological measurements, and clinical/medical research. 

The stated specific goals of the SENSATION project are to design, develop, and extensively test 

two new nanosensors and 17 new miniaturized microsensors to monitor, predict, and detect 

human physiological states (Bekiaris et al. 2004b). A decision was made to apply this project to 

the issue of sleepy drivers. Transportation-related project objectives include the following: 

 Definition of sleep/wakefulness and their transition states, as well as stress, inattention, 

and emotional states, by a set of measurable criteria and their correlation with 

measurement methodologies and tools 

 Modeling of sleep stages and extraction of requirements and specifications for sensors to 

measure the required model parameters 

 Development of nano and microsensor technologies for unobtrusive monitoring of 

physiological state and activity 

 Development of camera-based microsensors for high-precision, cost-efficient, and highspeed measurement of EOG and motility parameters and estimation of the person’s 

vigilance and attention state 

 Development of posture and capacitive microsensors for measurement of physiological 

and activity-related parameters to estimate the person’s vigilance and attention state 

 Development of eyelid and skin micro and nanosensors for respiration and skin 

conductivity measurements (autonomic functions) to estimate the person’s sleep/ 

wakefulness state and analyze its characteristics (i.e., differentiating normal from 

abnormal sleep) 

 Development of innovative signal processing and computational intelligence algorithms 

for data fusion, data management, sensor integration, and power consumption 

minimization 

 Development of user-friendly, modular, and intuitive user interfaces for sensor output 

presentation as well as for real-time warnings to the user in case a problem is detected 
32 

 Development of a multi-purpose sensing platform to assess the physiological state of a 

user that will be: autonomous (will operate in real time, continuously monitoring the 

user); non-supervised (diagnosis will be displayed to the user without any on-line 

intervention from an expert); non-intrusive (no wires or cables so that the subject is not 

overly aware of the system); and robust (will function in an unpredictable real-world 

environment where conditions cannot be controlled) 

To summarize, the SENSATION project uses a multisensor platform approach to the problem of 

sleepiness and fatigue-related workplace accidents, with an intended outcome of improved 

worker health, safety, and quality of life. This will be accomplished through the novel use of 

micro and nanosensors and related technologies, at low cost and high efficiency, for 

physiological state monitoring (Bekiaris et al. 2004b). As this report was being written, the EU 

planned to start a research program in 2007 to foster innovation and competitiveness. This 

European technology platform will focus on research and development (R&D) issues for 

industry and government and will result in the establishment of an EU R&D agenda. The goal of 

the current SENSATION program is to detect sleepiness in an actual driving situation, sensitive 

to chronic deprivation and health-related issues as well as time of day, and to provide this 

information in a meaningful way to the driver. 

On May 29–30, 2006, an international conference on “Monitoring Sleep and Sleepiness—From 

Physiology to New Sensors” was held in Basel, Switzerland. This symposium was primarily a 

technical review of various projects under development for SENSATION, and it provided a 

forum for presenting recent advances in areas such as sensor technology, sleep physiology, and 

alertness prediction models and algorithms. Additional information, technical details, and 

research results of several promising emerging technologies and devices from this conference are 

presented in the following sections. 

4.1 FUZZY FUSION OF PHYSIOLOGICAL INDICATORS FOR 

HYPOVIGILANCE-RELATED ACCIDENT PREDICTION 

G. Damousis of the Centre for Research & Technology Hellas presented a fuzzy expert system 

for the detection of the physiological manifestations of extreme hypovigilance. The system fuses 

various eyelid-related features in order to provide a reliable system that both maximizes the 

accident prediction accuracy and generates a small number of false warnings. The system is 

trained to predict accidents that will occur in the next 5 minutes. For the development and testing 

of the system, driving simulator data from 37 subjects were used. The results of the preliminary 

tests show that fuzzy combination of physiological parameters presents great potential for sleep 

and accident prediction, while it is expected that the integration of other physiological 

modalities, such as EEG, will improve the performance of the system and make it suitable for 

commercial applications. 

4.2 INDIVIDUAL DIFFERENCES IN PREDICTED DRIVING PERFORMANCE 

FROM SENSORY DATA AND SUBJECTIVE RATINGS 

A study conducted by the Karolinska Institute was aimed at investigating individual 

differences in driving performance and sensory data. Data were collected from 10 subjects in a 
33 

:00 

n after they had worked 

the night shift. During the drive, eye-blink durations (BLINKD) were collected by means of 

g as 

ubjective sleepiness, as measured by KSS, was also 

used to predict the probability of accidents. With increased sleepiness, the probability of an 

ility of an 

 observed 

high-fidelity moving base dynamic car simulator. Subjects drove between 8:00 a.m. and 10

a.m. under two conditions: first, with a normal night of sleep, and the

electroocculogram, and driving performance was evaluated on the basis of lane driftin

measured by the standard deviation of lateral position of the vehicle (SDLAT). In addition, 

subjective ratings of sleepiness were collected in five-minute intervals using the Karolinska 

Sleepiness Scale (KSS). Accidents were scored when the vehicle went with two wheels off the 

right side of the road or with four wheels into the opposite lane. Data were analyzed using a 

generalized linear mixed model approach. 

Results of the study showed that there were statistically significant relationships between KSS 

and BLINKD and between KSS and SDLAT. With increased sleepiness, blink durations became 

longer and lane drifting larger. There were also large individual differences observed for both 

variables independent of sleepiness levels. S

accident occurring during the subsequent five-minute segment of driving increased to 

approximately 10% at KSS = 9 (very sleepy, fighting sleep, effort staying awake) for an average 

subject. There were large individual differences in accident propensity observed with 

probabilities of 3% to 90% at maximum KSS. Finally, both variables BLINKD and SDLAT were

found to predict accidents, and individual differences in accident propensity were larger for 

BLINKD than for SDLAT. The researchers concluded from their study that the probab

accident in a driving simulator was predicted by increased subjective sleepiness, longer eye-blink 

durations, and increased lane drifting. However, large individual differences were also

that must be accounted for if predictions are made for individual subjects. These individual 

differences were found to be largely independent of sleepiness levels and probably reflect 

differences in sleepiness propensity, basal physiological eye-blink processes, and driving ability. 

4.3 ADVISORY SYSTEM FOR TIRED DRIVERS 

Dr. Jim Horne of Loughborough University, UK gave an introductory lecture on the issues in 

detecting sleepiness in drivers. He then briefly discussed a new “intelligent” sleepiness warning 

system for drivers called the Advisory System for Tired Drivers (ASTiD). Dr. Horne stated that 

following fatigue-related road crashes, drivers usually deny knowledge of falling asleep at the 

wheel or feelings of sleepiness beforehand. This suggests they have no forewarning of sleepiness 

and that external aids to drowsiness detection are necessary. However, sleep does not normally 

occur spontaneously from an alert state. Drivers who fall asleep at the wheel have good “on-line” 

knowledge of sleepiness, but they underestimate the likelihood of actually falling asleep as well as 

the magnitude of sleepiness and its effect on impairment. Fatigued or sleepy drivers show different 

durations and sequences of the physiological events that precede sleep oUt. For example: 

 The EEG may show neither being awake nor asleep, but a protracted state of “quasisleep.” 

 Eye closure can be delayed so that drivers are asleep with their eyes open. In fact, one in 

five people do not exhibit slow eyelid closures; as a result, the effectiveness of eye 

closure detectors is debatable. 
34 

n be 

 by factors such as outside road lighting, sunlight, oncoming headlights, inat the 

 a randomly generated sound 

, and 

eat inclination can inhibit or 

y or simulator testing, 

 uses a 

ing 

e. 

nsor uses a 

pressure-sensitive sensor foil to assess body posture and movement. The concept is based on 

ovement can be used to establish a diagnostic 

ensor 

r 

 Eye blink rates are claimed to be good predictors of sleepiness, but driver blinking ca

affected

vehicle air temperature, and state of the ventilation system. 

 Reaction time is not a reliable measure of drowsiness. A study in the UK showed th

driver pushing a button on the steering wheel in respoU to

from within the car gives a poor indication of driver sleepiness. 

 Falling asleep can be accompanied by reduced body muscle tone (e.g., head rolling)

foot pressure on the accelerator pedal may relax. However, s

counteract head rolling, and any vehicle decelerating depends on the dynamics of the 

accelerator pedal return spring in counteracting foot pressure. 

Such difficulties in detecting drowsiness may not be evident in laborator

since fighting sleep and fear of crashing are absent. Thus, devices must also be tested under real 

road and driving conditions. A key to driver sleepiness is lane drifting with cessation of steering 

corrections, which is the basis of the ASTiD alertness monitoring device. ASTiD both measures 

the predicted fatigue level using mathematical fatigue models and fatigue-related steering 

behavior, and provides an audible and visual warning alarm to the driver. The system

sleep/fatigue software model to predict levels of fatigue based on time of day, circadian clock, 

and the amount and quality of sleep of the operator combined with tracking of steering wheel 

movements. The system can be set to require user input (quality of prior sleep) or can be set to 

assume varying levels of default sleep quality. ASTiD has been successfully field tested in both 

highway and mining vehicle applications, and it is being installed in all the trucks of a lead

European logistics company. Where installed, feedback from operators has been fairly positiv

In general, operators felt that the system was consistent with how they were feeling, although 

some drivers experienced alarms when they were not feeling sleepy at all. This was observed 

largely when operators were not inputting their personal sleep history information. 

4.4 SENSOR FOIL—CONCEPT FOR ASSESSING A DRIVER’S BODY POSTURE 

AND MOVEMENTS 

A new sensing concept referred to as Sensor Foil (SEFO) has been developed within the 

SENSATION project and was described by Serge Boverie of Siemens VDO. This se

research studies that have shown that the movements and attitudes of a human provide pertinent

information to estimate wakeful and sleepy states as well as the transitions between these states. 

Several indicators such as body posture and m

about a person’s state of wakefulness/sleepiness. The seat sensor foil design (SEFO) is a s

matrix consisting of 64 cells (elementary sensing units). The intent of this design is to be able to 

measure in real time the spatial distribution of the applied pressure variation on the seat. Body

movements can then be estimated with the on-line analysis algorithms. In addition, the sensor 

provides an estimation of body relaxation based on mechanical impedance. The system also uses 

an information processing and data extraction module to extract, filter, and classify the senso

output data in order to characterize the movements of the driver. 
35 

d in Australia, led by Dr. Murray Johns, has 

developed a new method called OptalertTM for monitoring the drowsiness of drivers based on the 

 to use, are 

ary 

eyeglasses. The total IR light reflected back from the eye and eyelid is measured by a 

ed from 

ments, as 

lated 

ess 

An experimental validation of the SEFO sensor was conducted to evaluate the functionality and 

performance of the sensor and algorithm. The sensor has been installed in an experimental 

vehicle, and both static and on-road driving tests have been performed. Preliminary results 

indicate generally good detection performance, although some misclassification problems have 

also been observed. 

4.5 OPTALERTTM—A SYSTEM FOR MONITORING EYE AND EYELID 

MOVEMENTS BY INFRARED REFLECTANCE OCULOGRAPHY 

A research team at Sleep Diagnostics Pty Lt

reflectance of infrared (IR) light. Brief pulses of IR light, which are invisible and safe

directed at 500 Hz up at the eye from an LED positioned in a frame worn like ordin

phototransistor beside the LED. The ambient light measured before each pulse is subtract

the output, which is then not affected by different environmental light levels. The output gives a 

measure of the position and movement of the eye and eyelid. The velocity of movement is 

calculated in the software as the change of position per 50 msec. For saccadic eye move

well as the closing and reopening of eyelids during blinks, their amplitude is very closely re

to their maximum velocity. Amplitude-velocity ratios (AVRs) increase with drowsiness, as does 

the duration of eyelid movements and closures, whether caused by sleep deprivation or alcohol 

impairment. A combination of such variables has been used to create a new scale of drowsin

that has been validated against objective performance measures. The device also emits an audible 

alarm to warn the user of approaching drowsiness. 

OptalertTM driver-drowsiness monitoring systems are currently being used by commercial 

vehicle operators in Australia. Despite the rather intrusive nature of the device, it reportedly has 

received generally positive feedback from users. 

4.6 NON-INVASIVE MULTI-PARAMETER ESTIMATION OF CIRCADIAN PHASE 

Alertness and sleepiness are significantly determined by an individual’s sleep history and the 

timing of his or her circadian rhythm. For individuals performing high-vigilance tasks, such as 

commercial vehicle operation, monitoring and predicting the timing of circadian-induced 

alertness low points can provide a tool for reducing accident risk. To enable predictive capability 

in operational settings, however, it is necessary to measure sleep times and circadian phase 

timing in a non-invasive and ambulatory manner. While ambulatory actigraphy sensors can 

produce reasonable estimates of sleep timing, existing techniques for measuring circadian phase 

rely on invasive core body temperature or saliva melatonin measurements, neither of which is 

amenable for use outside the laboratory. There are numerous physiological measurements that 

exhibit circadian variations; however, no non-invasive measurements have been shown to be 

suitable as a stand-alone determinant of circadian phase. 

Christopher Mott of the University of British Columbia presented a novel approach they are 

developing to circadian phase assessment using a statistical signal processing approach that 

combines the information from multiple non-invasive measurements into a single estimate. The 
36 

ay 

ray was used to simultaneously collect 

non-invasive cardiac, respiratory, thermoregulatory, and motion signals. Invasive measurements 

nided a 

an’s Visionary 1959 Christmas 

nanotechnology. Devreese’s paper focuses on the development and implementation of 

 the SENSATION effort. The author 

discusses four reasons why nanoparticles are unique tools as sensors. First, being similar in size 

particles 

nsors 

s 

r blood 

technique involves a multi-parameter estimation using a Kalman filtering framework. A four-d

laboratory study was performed in which a large sensor ar

of core body temperature and saliva melatonin were also collected for validation purposes. 

Analysis of the results confirmed that circadian components can be extracted from various no

invasive signal measurements, but that individually they are not sufficiently reliable markers. 

When analyzed in combination using the multi-parametric signal processing algorithm, an 

improved estimate of circadian phase in relation to the invasive validation measures was 

demonstrated. It was concluded that the multi-parametric signal processing technique makes an 

important step toward enabling ambulatory circadian rhythm assessment using currently 

available sensors and has the potential to enhance the capabilities of new ambulatory 

physiological sensors related to sleep and circadian rhythms. 

4.7 THE IMPORTANCE OF NANOSENSORS 

J. T. Devreese of the Universiteit Antwerpen (University of Antwerp) in Belgium prov

paper entitled “Sensing the Importance of Nanosensors: Feynm

Lecture.” The author cites a Richard Feynman lecture in 1959 as the starting point of 

nanotechnology, which underscores the primary goal of

to many proteins, nanoparticles can operate inside cells. Second, nanosensors possess unique 

physical characteristics. Their sensitivity can be orders of magnitude better than that of 

conventional devices, and they can have fast respoU times and portability. Third, nano

reveal unique physical properties that do not exist in bulk materials. And fourth, nanose

allow for building integrated devices, providing a basis for intelligent sensors with application

for data processing, storing, and analyzing power. The paper concludes by considering prospects

for the future of sensors with sizes at the nanoscale, including their usefulness as human 

vigilance monitoring devices. The author states that the calculated limits of sensitivity fo

pressure and pulse nanosensors, chemical and thermal nanosensors, as well as the estimated 

power budget for the data transfer in typical in vivo medical nanodevices, make them possible 

candidates to improve the quality of sensors for monitoring sleepiness and wakefulness. 

4.8 SLEEP CONTRACTS: A PROCEDURE FOR MANAGING DAY-TO-DAY 

FATIGUE RISK 

The Energy Institute in London, UK published a research report entitled “Viability of Using 

Sleep Contracts as a Control Measure in Fatigue Management” (The Energy Institute) and 

provided copies to attendees of the SENSATION conference. This document provides the 

foundation for understanding a “fatigue management system” and includes relevant tools, such 

as the contract itself. It is not a how-to prescription, however, and still requires expert 

consultation, either in-house or externally, to operationalize. This was the only discussion at the 

conference that centered on user and policy issues as opposed to mechanical/sensor technology. 

The concept of a sleep contract as an integral component in an overall fatigue management 
37 

system is summarized in the following paragraphs, which are excerpted from the Energy Institute 

research report. 

As a first step in determining the role of sleep contracts in fatigue risk management, a formal 

definition of the term “sleep contract” was developed. As defined by the authors, “a sleep 

contract is a negotiated and agreed framework for managing fatigue on a day-to-day basis that is 

integrated into an organization’s existing Safety Management System. The framework is 

formally documented and makes it clear that employees and management are jointly responsible 

for the management of fatigue risk and states the responsibilities and accountabilities of each 

party.” Thus, it is much more than simply a formal obligation on the part of the employee to 

obtain sufficient sleep prior to attending work. Nor is it merely a mechanism for management to, 

intentionally or unintentionally, step around fatigue risk management responsibilities. 

Since a sleep contract is a new concept, it is not yet possible to provide evidence-based guidance 

for how best to design, implement, or operate it. Nonetheless, experience with other fatigue risk 

management strategies strongly indicates that for a sleep contract to be effective, the content 

should be agreed via a process of consultation and negotiation among employees, management, 

and safety professionals. The success of a sleep contract depends to a large extent on a work 

environment in which mutual support between employee and management exists. In this type of 

supportive work environment, a sleep contract is more likely to be perceived as an operational 

tool that has been developed to jointly assist employees and managers, rather than a disciplinary 

tool or a mechanism for management to avoid their responsibility regarding fatigue. 

According to the Energy Institute researchers, “the theory behind a sleep contract is that, by 

making it a formal requirement to obtain sufficient sleep, an employee’s commitment to 

obtaining sleep is more likely to improve. Similarly, by making it a formal requirement to report 

fatigue, and by providing a formal procedure for how this should be responded to, employees 

should be more likely to report fatigue. When an individual reports fatigue to their immediate 

supervisor, a sleep contract would require that both parties respond to the case in a formal, 

structured manner and manage the immediate and long-term risk. In this way, a sleep contract 

enables an organization to know its actual fatigue risk and implement the necessary controls.” 

An additional benefit of a sleep contract is that the data collected on the incidence of fatigue can 

be used to track trends and to identify potential causes and countermeasures and the effectiveness 

of countermeasures. 

For a sleep contract to be successful, its impact on key performance indicators, safety outcomes, 

and the behavior of individuals (employees or managers) needs to be supported, measured, and 

reported. For a sleep contract to be supported by the necessary operational framework it seems 

reasonable to recommend that it be integrated into an organization’s existing Safety Management 

System (SMS). Within an SMS, the role of a sleep contract is twofold: 

 It is a tool for monitoring fatigue and the state of contributing factors. 

 It provides a negotiated framework for reporting and responding to fatigue. 

Considering the potential value of sleep contracts, the authors conclude by recommending that 

further research be conducted to develop guidelines for the process of negotiating and 

implementing such a contract. 
38 

5. CONCLUSIONS 

Driver drowsiness poses a major threat to highway safety, and the problem is particularly severe 

for commercial motor vehicle operators. Twenty-four-hour operations, high annual mileage, 

exposure to challenging environmental conditions, and demanding work schedules all contribute 

to this serious safety issue. Monitoring the driver’s state of drowsiness and vigilance and 

providing feedback on his or her condition so that he or she can take appropriate action is one 

crucial step in a series of preventive measures necessary to address this problem. 

This report attempts to present in a concise and summary fashion some of the scientific 

activities and technological approaches that are currently underway to address driver 

drowsiness as a critical safety issue. Several promising state-of-the-art devices and 

technologies were identified and evaluated against a set of proposed design guidelines. 

Technological advances in electronics, optics, sensory arrays, data acquisition systems, 

algorithm development, and machine vision have brought the goal of providing unobtrusive, 

real-time, affordable, 24-hour driver alertness monitoring capability much closer to reality. 

Considerable development effort is taking place to demonstrate the scientific validity and 

reliability of these technologies, but more work is required before they can be fully 

implemented as an integral component of an overall fatigue management program. 
39 

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