Development of Vehicle Driver Drowsiness DetectionSystem Using Electrooculogram (EOG)

Thurn Chia Chieh, Mohd. Marzuki Mustafa, AiniHussain, Seyed Farshad Hendi

Dept. of Electrical, Electronic & System EngineeringUniversiti Kebangsaan Malaysia

Bangi, Malaysia

Burhanuddin Yeop Majlis

Institute ofMicroengineering and NanoelectronicUniversiti Kebangsaan Malaysia

Bangi, Malaysia

Abstract- Driver drowsiness is one of the major causes of roadaccident. Various driver drowsiness detection systems have beendesigned to detect and warn the driver of impending drowsiness.Most available prototype and ongoing research have focused onvideo-based eye tracking system, which demands high computingpower due to real time video processing. In our research, the useof electrooculogram (EOG) as an alternative to video-basedsystems in detecting eye activities caused by drowsiness isevaluated. The EOG, which is the electrical signal generated byeye movements, is acquired by a mobile biosignal acquisitionmodule and are processed offline using personal computer. Digitalsignal differentiation and simple information fusion techniquesare used to detect signs of drowsiness in the EOG signal. EOGsignal is found to be a promising drowsiness detector, withdetection rate of more than 800/0. Based on the tested offlineprocessing techniques, an online fatigue monitoring systemprototype based on a Personal Digital Assistant (PDA) has beendesigned to detect driver dozing off through EOG signal.

I. INTRODUCTION

Driver drowsiness is a serious hazard in transportationsystems. It has been identified as a direct or contributing causeof road accident [1]. Drowsiness can seriously slow reactiontime, decrease awareness and impair a driver's judgment. It isconcluded that driving while drowsy is similar to driving underthe influence of alcohol or drugs [2]. In industrializedcountries, drowsiness has been estimated to be involved in 2%to 23% of all crashes [3].

The development of a driver monitoring system capable ofproducing warning to the driver upon detecting signs ofdrowsiness can prevent road accidents and thus save lives.Therefore, research on detection of drowsiness has sparkedmuch interest in many countries. However, most existingmethods of detecting drowsiness are in the prototypic,evaluation or early implementation stages and remainscientifically and practically unproven [4]. Complexdrowsiness detection systems such as the video-basedPERCLOS seem potentially very effective, but are not yetcommercially available [5]. Those complex systems usuallyrequire powerful computing devices to perform signalprocessing. This requirement hinders their practical use.

Figure 1. Electrode placement positions for EOG measurement

In this research, a less computational intensive alternativeof drowsiness detection method is explored and evaluated. Theevaluated methods is the electrooculogram (EOG) signal of thedriver

II. DATA COLLECTION PROCEDURES

EOG is electrical signal generated by polarization of theeye ball and can be measured on skin around the eyes. Itsmagnitude varies in accordance to the displacement of the eyeball from its resting location [6]. Rapid eye movements(REM), which occur when one is awake, and slow eyemovements (SEM), which occur when one is drowsy, can bedetected through EOG [7].

EOG signal is acquired by placing Ag/AgCIelectrophysiology electrodes around the eyes. Two channels ofbipolar EOG signal are acquired for analysis, which are thehorizontal channel and vertical channel. The horizontalchannel EOG reflects horizontal eyeball movements while thevertical channel EOG reflects vertical eyeball movements.Two disposable Ag/AgCI electrodes were placed above andbelow the right eye to measure vertical EOG while two othersuch electrodes were placed at the outer canthi to measurehorizontal EOG. A silver plated electrode was clipped on theleft earlobe, acting as the group point. The illustration ofelectrodes placements is shown in Fig. 1.

detection;Keywords-driving safety; drowsinesselelectrophysiological signal; electrooculogram.

1-4244-0011-2/05/$20.00 ©2005 IEEE.

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Figure 2. Instrument setup during data collection

The first derivative of any data point is obtained from thedifference between that point and another point severalsamples before it in order to reduce approximation errorcaused by noise in the signal. Two digital differentiators areimplemented according to equation 1 in order to process thehorizontal and vertical channels individually.

(5)

(4)

(3)

N

NREM(i) = LDREM(n)n=l

NREM is compared against another threshold value, Nth toproduce the decision output of this EOG analysis algorithm,indicating whether the experiment subject is awake or sleepy.The binary decision function of this algorithm, D(i) isindicated by equation 5.

. {O, NREM(i) ~ MhD(l) =

1,NREM(i) < N«

When the occurrence of REM activity equals or exceedsthe threshold value, the output of the algorithm equals zero,indicating the subject is not in fatigue state. Otherwise, thedetection algorithm produces an output of one to indicate thatthe subject is in fatigue state.

Information fusion technique is then employed to integratethe horizontal and vertical channels. The EOG signalrepresents eye movements but is separated into two channelsdue to limitation of the measurement technique. By integratingboth the horizontal channel, complete information of the eyemovement can be obtained. Besides that, both channels cancomplement each other in the process of fatigue detection. TheData In - Data Out (DAI - DAO) fusion which creates newdata from several sources of raw data as described by [8] isused to integrate the horizontal channel with the verticalchannel.

Since the EOG signal represents eyeball displacement fromthe central fixation point, the differentiated EOG signal isproportional to the velocity of the eyeball movement. Thedifferentiated horizontal channel signal represents eyeballvelocity component in the horizontal direction while thedifferentiated vertical channel signal represents eyeballvelocity component in the vertical direction. The velocity ofthe eyeball movement, v can be obtained from the vectoraddition of the horizontal velocity component, ds; and thevertical velocity component dsy as indicated by equation 2.

v = ds; + dsy (2)

The magnitude of the eyeball movement velocity iscompared with a threshold value in order to locate REMactivities. A hard limit function is used to detect REMactivities. The function DREM(i) produces 1 to indicate REMactivity has occurred when the velocity magnitude is greaterthan the threshold and otherwise 0 to indicate there is no REMactivity as indicated by equation 3.

{O,v(n) < Vth

DREM(n) =1, v(n) ~ Vth

In order to obtain an index of alertness, DREM is framed intoN samples using non-overlapping rectangular windows. Thesum of DREM values within a frame, NREM is computed. NREM

represents the duration of REM activity occurrence within aframe. This value is used as the index of alertness. Thecomputation ofNREM ofthe i-th frame is shown by equation 4.

(1)

o

PDA

DRS232 Link

Personal Computer

Test Subject

dsx(n ) =sx(n ) - sx(n - a ), a > 1

EOG Electrodes

Webcam

Data collection sessions involve having subjects sitting ona cushioned chair in an air conditioned room. The experimentprocedures were fully explained to the subjects and theirwritten consent were obtained. They were told to relaxthemselves and take a nap when feeling drowsy. EOG signalswere recorded for at least 35 minutes depending on thesubject's condition.

III. DATAANALYSIS

The EOG signal is proportional to the displacement of theeyeball from center fixation point. Thus, the first derivative ofthe EOG signal is proportional to the velocity of eyeballmovement and can be used to distinguish rapid eye movements(REM) which occur during wakeful period from slow eyemovements (SEM) that occur during sleep onset. The EOGsignal is differentiated to detect REM activities and theduration of REM occurrence is used as an index of alertness.

The first derivative of the EOG signal is approximated bythe difference between data points, as characterized byequation 1 where the first derivative of the horizontal channelis represented by ds, and the horizontal EOG signal by s..

The EOG signals were acquired by using a mobileelectrophysiological signal acquisition module with samplingfrequency of 256 Hz and resolution of 16 bit. The acquisitionmodule has AC coupled inputs, with frequency response from0.01 to 30 Hz. The recorded data were stored on a PersonalDigital Assistant (PDA) connected to the acquisition module.Upon completion of the recording, the stored data wastransferred to a PC for off-line processing. Subjects' behaviorsduring signal recording were video recorded using a webcamera to provide reference to the recorded data concerningthe subject's alertness. The instrument setup used during datacollection is shown in Fig. 2:

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The fatigue detection process through differentiation ofEOG signal is summarized in Fig. 3.

ecision (DrowsyNot Drowsy)

Figure 3. Summary ofthe EOG processing algorithm

IV. RESULTS AND DISCUSSION

Thirty-two sets of EOG data with duration of ten minutesrecorded from ten healthy subjects with ages ranging from 18to 26 years old were used to test the developed algorithm. Theproducts of different processing stages are shown in Fig. 4.The performance of the EOG fatigue detection algorithm interms of detection success rate in different channels is depictedin Table 1.

Figure 4. Products ofdifferent EOG signal processing stages

TABLE I.TABLE TYPE STYLES

Channel Detection Success Rate(%)

Horizontal 87.51

Vertical 86.82

Information Fusion 89.56

V. IMPLEMENTATION OF AN ONLINE PROTOTYPE

Based on the obtained results, a fatigue monitoring andprevention prototype has been implemented. The prototypecontinuously monitors the user's alertness level through onlineprocessing of his EOG signal. An audible alarm is activatedwhen the user closes his eyes for more than 2 seconds.

The developed prototype comprises of anelectrophysiological signal acquisition unit to condition anddigitize the EOG signal and a Personal Digital Assistant(PDA) to perform signal processing. The electrophysiologicalsignal acquisition unit also acts as an interface to activate abuzzer alarm through its digital output channels. The softwaremodule of the prototype is written by using the MicrosoftEmbedded Visual C++ 4.0 software development environmentand installed on the PDA to process data acquired by theelectrophysiological signal acquisition unit. The developedprototype is shown in Fig. 6.

Figure 6. The developed online fatigue monitoring andprevention prototype

VI. CONCLUSIONS

An alternative method to detect driver drowsiness, namelythe electrooculogram has been studied in this research. Theelectrooculogram produces encouraging results in detectingsigns of drowsiness. Simple information fusion technique hasalso been employed in this research and has successfullyimproved the success rate of drowsiness detection. Based onthe findings of this research, an online fatigue monitoring and

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prevention prototype has been implemented to evaluate theperformance of the developed fatigue detection techniques inreal life.

ACKNOWLEDGMENT

The authors would like to thank the Malaysia Ministry ofScience, Technology and Innovation for sponsoring this workunder project IRPA 03-02-02-0017-SR0003/07-03.

REFERENCES

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[4] D. F. Dinges & M. M. Mallis, "Managing fatigue by drowsinessdetection: Can technological promises be realised?" In ManagingFatigue in Transportation. Proceedings of the Third InternationalConference on Fatigue and Transportation, L. R. Hartley, Ed. Fremantle,Western Australia, 1998, pp. 209-229.

[5] T. Horberry, L. Hartley, G. Krueger & N. Mabbott, Review of FatigueDetection and Prediction Technologies. Melbourne: National RoadTransport Commission, 2000.

[6] J. Malmivuo, & R. Plonsey, Bioelectromangnetism, principles andapplications of bioelectric and biomagnetic fields. New York: OxfordUniversity Press, 1995.

[7] S. K. L. Lal, & A. Craig, "A critical review of the psychophysiology ofdriver fatigue", Biological Psychology vol. 55, pp. 173-194,2001.

[8] B. V. Dasarathy, "Sensor Fusion Potential Exploitation - InnovativeArchitectures and Illustrative Applications", Proceedings of the IEEE85(1), pp. 24-38, 1997.

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