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AN IMPROVED AND PORTABLE EYE-BLINK DURATIONDETECTION SYSTEM TO WARN OF DRIVER FATIGUE

Chin-Shun Hsieh and Cheng-Chi Tai

Department of Electrical Engineering, National Cheng Kung University,Taiwan, R.O.C.

& Driver fatigue is a major cause of vehicle accidents. The measurement of eye-blink durations isone of the main schemes to warn of driver fatigue. This article proposes a methodology to detecteye-blink duration, which is similar to electro-oculography (EOG), but only two electrode padsare employed in our detection system. Simple electrode pads were used as sensors to obtain the correcteyelid EOG signal and remove the artificial pseudo-signal (non EOG). A real-time microcontrollercalculates and shows the blink-durations and number of blinks. The measurement results are ana-lyzed to warn of driver fatigue. According to our measurements, the numbers of blinks can be mea-sured with high accuracy. The accuracy of blink duration determination was close to 95% via a1 kHz sampling rate that is equivalent to using a video camera.

Keywords eye-blink duration, fatigue, microcontroller, portable, regression

INTRODUCTION

Driver fatigue has been considered one of the main causes of traffic acci-dents and has become a critical issue on the road safety agenda. Most peoplethink fatigue is a factor resulting in drowsiness. In the 1990’s, the U.S.National Transportation and Safety Board focused on fatigue as one ofthe most important reasons for road traffic accidents (National Transpor-tation and Safety Board of US 1999). Boussuge[1] found fatigue and=ordrowsiness of the driver caused around 30% of accidents on French high-ways in the period 1979–1994, whereas about 40% of fatal accidents onU.S. highways are sleep related.[2] Horne et al.[3] stated that 10–20% of allaccidents are related to driver fatigue. Many traffic accidents are causedby drivers falling asleep at the wheel.[4] In 2007, fatigue, which impairsdrivers’ judgment and their ability to control vehicles, was involved in atleast 18% of fatal accidents and accounted for about 7% of all accidents.

Address correspondence to Chin-Shun Hsieh, Department of Electrical Engineering, NationalCheng Kung University, Tainan 701, Taiwan, R.O.C.. E-mail: [email protected]

Instrumentation Science and Technology, 41:429–444, 2013Copyright # Taylor & Francis Group, LLCISSN: 1073-9149 print/1525-6030 onlineDOI: 10.1080/10739149.2013.796560

Instrumentation Science and Technology, 41:429–444, 2013Copyright # Taylor & Francis Group, LLCISSN: 1073-9149 print/1525-6030 onlineDOI: 10.1080/10739149.2013.796560

Therefore, it is essential to develop a safety system for drowsiness-relatedroad accident prevention.

A method to detect drowsiness in drivers was developed by Thorslund.[5]

The drowsiness detection was based on eye blink measurements inEOG data. The method was based on the linear relationship between blinkamplitude and blink velocity, found by Hargutt and Kruger, and on theirsuggestion of how to define different stages of drowsiness.[6]

Drowsiness is the transition between the waking state and sleep duringwhich one’s abilities to observe and analyze are reduced. According to theUSA National Highway Traffic Safety Administration (NHTSA 2010), about100,000 crashes are the direct result of driver drowsiness each year. This isthe reason more and more studies are being conducted to build automaticdetectors of this dangerous state.

Driver fatigue detection techniques can be broadly classified into tech-niques that monitor the driver directly and the behavior of the vehicle asa result of the driver. Techniques to monitor the driver directly include mea-suring physiological conditions (such as brain waves, skin conductance,heart rate, pulse rate and production of the hormones adrenaline, pupilsize, noradrenaline, and cortisol[7,8] and monitoring eyelid movement,head movement, and facial conditions such as frequent yawning. Physiologi-cal measurements achieve the best accuracy in terms of driver fatigue detec-tion, but are intrusive as electrode attachment to the driver is required. Thisusually proves to be an annoyance to the driver and is in most situationsimpractical. However, physiological measurements serve as a good bench-mark when evaluating other techniques.

The EOG, which measures the eye electrical muscles activity, has beenwidely used in the literature to estimate drowsiness.[9,10] EOG is the mostreliable technique to detect and characterize blinks due to its high samplerate (from 250–500 Hz) and it is used as a reference by experts to evaluatedrowsiness.[4,11] Unfortunately, the EOG requires at least three electrodesto be placed on the driver’s skin, which is not pleasant. Therefore, for obvi-ous ergonomic reasons, the research community has focused on the use ofvideo to track the driver’s eyes and face and thus detect whether or not heor she is drowsy.

The spontaneous eye blink is considered a suitable indicator for fatiguediagnostics. To evaluate eye blink parameters as a drowsiness indicator, amethod for the measurement of spontaneous eye blinks was developed.[9]

Regarding oculomotoric parameters, blink duration, delay of lid reopen-ing, blink interval, and standardized lid closure speed were identified asthe best indicators of subjective as well as objective sleepiness.

Several characteristics can be extracted from the video to estimatedrowsiness such as blink duration, blink frequency, the PERcentage of eyeCLOSure (PERCLOS) as well as driver’s gaze or facial expressions.[12–14]

430 C.-S. Hsieh and C.-C. Tai

Most of these systems focus on too long blink durations to detectdrowsiness.[15] This article focuses on the visual signs of drowsiness: blinks.

An EEG-based algorithm for detecting driver fatigue was suggested byLal.[16] This algorithm considers alpha (a, 8–13 Hz), beta (b, 12–30 Hz),delta (d, 0–4 Hz) as well as theta (h, 4–7 Hz) waves to determine if a driveris fatigued. The algorithm was developed based on previously conductedstudies.[17–19] Beta waves are also associated with alertness and the activeconcentration of a person. Alpha waves can also be useful, but to a lesserextent. Therefore, significant increases in a driver’s delta and theta wavesand decreasing beta wave activity (increased in amplitude) are usually agood indicator of fatigue.

There are many drowsiness detection sensors, such as a charge-coupleddevice (CCD) camera.[20–22] However, these are greatly affected by sun-glasses, nighttime conditions, and other car light. Although the nighttimeillumination problem was resolved by using an invisible infrared lightsource, this increases power consumption and might present a potentialvision safety hazard. Other methods used many electrodes, but the instal-lation time was too long and expensive. Using PC-based (or ARMmicrocontroller-based) systems also increased power consumption andthe complex calculations took more time.

A small CCD camera mounted on the vehicle panel to capture images ofthe driver’s face while driving must surmount several hurdles in developingan effective method to measure blink duration. Therefore, the developmentof a secure system is necessary.

In addition, using electro-encephalogram (EEG) requires Fast FourierTransform (FFT) to be converted into the a, b, d, and h wave, and thenthe measurement is carried out. A CCD requires high frame rate videoand image processing technology are used.

MATERIALS AND METHODS

From the most advanced techniques, several indicators can be used toperform the diagnosis. The physiological measurements give the mostreliable information (i.e., electro-encephalogram EEG and EOG). Never-theless, it is not easy to measure them with non invasive systems. Accordingto the literature[8–11,23,24] both EOG and EEG are valid indicators of drowsi-ness. In the EOG, drowsiness is characterized by increased blink duration.In the EEG, drowsiness is characterized by a shift towards lower frequen-cies. Increased alpha activity and sometimes also theta activity are commonduring drowsiness. The problem with both measuring methods is therequirement of electrodes, making them unsuitable for automotives, asdrivers generally do not agree to being connected to cabling.

Detection System to Warn of Driver Fatigue 431

Electrocardiography (ECG), EEG, EOG, or electromyography (EMG)methods are characterized by measuring currents. Signal messages throughthis measurement are used to determine the physiological state. The electrodepads are used as the sensing element. Similar to EOG measurement, the padswere placed above the eyebrows, which is equivalent to 10–20 internationalstandard of electrode placement at the position of Fp1 or Fp2. The positionsof the electrode pads are shown in the upper right corner of Figure 1.

Measurement System Block and Circuit

The system block of our driver fatigue detector is shown in Figure 1.First, the employed sensors are prevailing non-invasive electrode pads and

FIGURE 1 (a) Block diagram and (b) photo of the measurement system. (color figure available online.)


the sensed signals are sent to an amplifier (or differential amplifier with ahigh common mode rejection ratio). Second, the weak signals are amplifiedby a suitable gain. Third, the filter circuit removes the artificial pseudo-signal (artificial signals of non EOG) to obtain the correct blink signal,and then converts the signal into an M-type waveform. M-type waveformsare converted by a waveform shaping circuit and their output levels mustbe microcontroller acceptable. Finally, the microcontroller calculates theblink duration and the number of blinks for the judgment of driver fatiguein real time.

The microcontroller also uses a watchdog function to avoid unexpectedinterferences that may result in system shutdown. If a system is being incor-rectly executed, the watchdog function can restart the system program toensure the safety of the system. Furthermore, the proposed detector is a sys-tem with low cost, low energy consumption, high stability, and high accu-racy. The employed electrode pads are very easily attached and can bereused 20–50 times.

The implemented circuits are shown in Figures 2 and 3 and include afront-end circuit and microcontroller circuit. The front-end circuit includesan instrument amplifier, an amplifier of suitable gain, a filter, and anM-type waveform shaping circuit. The microcontroller circuit includes amicrocontroller and a LCD display.

M-type Waveform Detection State and Interrupt Sub-Program

The blink of an eyelid message has been detected by a front-end circuitshown in channel 1 in Figure 4. Then, the M-type waveform is obtained by awaveform shaping circuit shown in channel 2 in Figure. 4.

The method to measure M-waveform durations can be clarified by aflow chart, as shown in Figure 5. As long as the states of 1, 2, and 3 areordered in sequence, their duration can be obtained when entering intostate 4. The interrupt sub-program is built and described as follows.

First, the microcontroller is interrupted every 1 ms to execute the inter-rupt sub-program. Second, when Pos¼ 1, it means the M-type head is aboutto begin, and T1 is increased by 1. If the voltage measured exceeds theboundary, the signal will be de-bounced and then transition to state 1.Third, when Pos¼ 0, T1 ends, the state will transition to state 2, and T2is increased by 1. The measured waveform will then be de-bounced. WhenNeg¼ 1, T2 ends, the state will transition to state 3, and T3 is increased by1. When Neg¼ 0, T3 ends, the state will transition to state 4, and the M-typesignal is obtained. The durations of T1, T2, and T3 are stored thereafter. T4is used to judge the necessity of de-bounce.

According to Hu,[25] the blink feature definition is shown in Figure 6.The eye-blink-time calculation is estimated from blink complex-start to


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blink complex-end, such as the Dx in Figure 4. Ten samples of T1, T2, T3,and true eye-blink time were taken and the results are shown in Table 1.After that, partial least squares (PLS) regression was used to obtain an equa-tion by Statistical Product and Service Solutions (SPSS) as follows:

Total machine testð Þ ¼ 1:673 T1 þ 0:092 T2 þ 1:096 T3 þ 24:476 msð Þ: ð1Þ

FIGURE 3 Microcontroller circuit. (color figure available online.)

FIGURE 4 Waveforms of a detected eyelid blink. (color figure available online.)


FIGURE 5 (a) Flow chart and (b) state diagram to detect M-type waveform.


TABLE 1 Ten Samples of T1, T2, and T3

True Eyeblink T1 (ms) T2 (ms) T3 (ms)

1 416 107 21 1832 384 92 20 2183 464 118 21 2064 368 85 19 1785 392 98 27 1996 384 77 25 1877 800 112 45 5288 720 141 60 4189 344 84 21 161

10 328 80 22 156

FIGURE 6 The blink feature definition. (color figure available online.)

TABLE 2 Regression using SPSS

Measured(ms) T1 (ms) T2 (ms) T3 (ms)

Machinetest

Error%(True-Machine)=True

328 80 22 156 331 1.01%344 84 18 167 350 1.66%576 138 64 288 577 0.15%600 125 64 309 578 �3.64%486 112 41 248 487 0.29%688 134 42 399 690 0.27%384 88 21 194 386 0.59%400 98 21 190 399 �0.35%

Note. Machine test¼ 1.673 T1þ 0.092 T2þ 1.096 T3þ 24.476.


Over 95% accuracy can be achieved, as shown in Table 2. The para-meter of R2 (coefficient of determination between 0 and 1) was 0.990and the adjusted R2 is 0.985.

Fatigue Detection Program

Figure 7 is a flow chart of the main program to detect driver fatigue thatcan be judged by the value of a calculated each minute:[26]

FIGURE 7 Flow chart of main program.


a ¼ ðnumber of long blinksÞ=ðtotal number of blinksÞ: ð2Þ

The driver is judged as drowsy when there is an increased pulse above aspecified threshold. Drowsiness stages based on blink behavior aredescribed in Table 3. This is dependent on a and the min-count value.

The flow chart of the main program is built and described as follows.First, the I=Os, Timer1, LCD, and Global enable interrupts were initia-

lized. Second, the watchdog command was cleared. Third, check if Flag¼ 4to ensure an eye-blink signal occurs, and the Mincount (an accumulator tonumber the eye blinks) in the flow chart is added by 1. At the same time,check if a long eye blink duration occurs. If it is true, the longcount inthe flow chart is added by 1. If longcount is over the normal duration ofa long-blink, it signifies the driver is very sleepy then the alarm is enabled.Fourth, check to see if the computation time equals 1 min (variable). If theresult is true and no blinks (Mincount¼ 0) are detected, it signifies the dri-ver has fallen asleep. Eye blinks occur before the driver falls asleep. Thus,the alarm needs to be invoked beforehand. However, if the driver isattacked by myocardial infarction or other acute diseases, the eye-blinkbehavior will stop immediately. Therefore, this facility is necessary. Fifth,check to see if a exceeds the threshold value. If the result is true, displaythe percentage of driver fatigue via a and invoke the alarm. Finally, the statewill transit to the second state to repeat the detection process.

RESULTS AND DISCUSSION

A common definition of blink duration is the time difference betweenthe beginning and the end of the blink, where the beginning and endpoints are measured at the point where half the amplitude is reached. How-ever, this definition will cause a problem when a vertical eye movement

TABLE 3 Drowsiness Stages Based on Blink Behaviour

Drowsiness stage Description Program Detection

Awake Long blink intervals and short blink durations. a value is small andmin-count is small

Low vigilance Short blink intervals and short blink durations. a value is mid andmin-count is mid

Drowsy Long blink durations. a value is largeSleepy Very long blink durations and=or single

sleep eventsa value is over or blink

duration is overSleep Eyes permanently closed (fallen asleep) Min-count is zero

Note. The value of a small than 0.1 is small, 0.1–0.2 is middle, 0.2–0.3 is big, and large than 0.3 is over.The value of min-count small than 6 is small, 7� 12 is middle.


FIGURE 8 Measurement of the blink duration (30 frames per second on video). (a) 11 frames for oneblink during 367 ms; (b) 372 ms as measured by detector (T1¼ 93 ms, T2¼ 25 ms, T3¼ 173 ms; display291 ms¼T1þT2þT3). (color figure available online.)


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occurs at the same time as the blink, since this causes a vertical shift in thesignal. The amplitude thus becomes difficult to define.

Eye-blink duration in this article was calculated from the blinkcomplex-start to the blink complex-end,[25] while other literatures calculatethe amplitude from 50% to 50% (from rise above 50% to fall down50%).[27] Any microcontroller with over 8 MHz operating frequencies canprovide relative good performance.

This eye-blink signal was accurately detected in testing of more than 30people. Verification of the eye-blink duration is shown in Figure 8 (30frames per second on video). As shown in Figure 8a, it can be seen thata blink is about 367 ms which is equal to 11 frames. In Figure 8b, the blinkduration was measured 372 ms by Equation (1) computed by our detector.

This article uses a secure, easy to install, and disposable electrode padsuitable for attachment to drivers. A microcontroller performs the maincontrol elements, and therefore the energy consumption is very low, beingless than 2 Watts (about 12 V at 150 mA). The regression scheme canquickly calculate the duration of the actual blink of an eye, and more than95% accuracy can be achieved. Since the proposed technique has a veryrapid response time to judge fatigue with one millisecond time resolution,it keeps the driver in a good driving condition.

One of the lane variability systems used a video camera. However, theaccuracy was severely affected by the clarity of the environment, road lines,and lights on the opposite lane. In addition, the steering wheel angle tech-nology needs to use complicated algorithms, such as artificial neuralnetworks, support vector machines and decision trees. The two schemesmentioned above are suitable for the general road. But when driving insecondary roads, the accuracy for drowsiness stage identification will becertainly affected.

As show in Table 4, our work is compared with recent technologies. Itcan be seen that our performances in responding speed, time resolution,and power consumptions outperform Camera-Based, EEG PC-based, lanevariability,[14] steering wheel angle,[31–34] and EOG PC-based detectors.

CONCLUSIONS

We used a simple method to solve a complex problem. Firstly, the pro-posed detector is camera-free; thus, the measurements are neither affectedby the intensity of sunlight, nor interfered with by shadows and the glassesof driver. Secondly, it is portable and can be easily and quickly installedwithout using complex electrodes. Thirdly, high time resolution with a1-kHz sampling rate to make an accurate determination can be achievedby employing a watchdog scheme to avoid shutdown and provide stability


and safety. Finally, the low cost and low power consumption featureoutperforms the existent methods. Therefore, the proposed detection sys-tem provides significant advantages.

REFERENCES

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2. Garder, P. Continuous shoulder rumble-strips: A safety evaluation. In CSRS Seminar. Linkoepin.1998, August.

3. Horne, J.; Reyner, L. Vehicle accidents related to sleep—A review. Occup. Environ. Med. 1999, 56,289–294.

4. Kecklund, G.; Akerstedt, T. Fatigue and road safety: An overview of the state of knowledge: Stock-holm: Swedish Institute for Psychosocial Medicine and the Karolinska Institute, 2000, p. 4.

5. Thorslund, B. Electrooculogram Analysis and Development of a System for Defining Stages of Drowsiness;Linkoping University: Linkoping, 2003.

6. Hargutt, V.; Kruger, H. P. Eyelid Movements and Their Predictive Value for Fatigue Stages.Psychologisches, Institute of the University of Wurzburg, 2000.

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10. Galley, N.; Schleicher, R.; Galley, L. Blink parameter as indicators of driver’s sleepiness – possibilitiesand limitations. Vis. Vehic. 2004, 10, 189–196.

11. Muzet, A.; Pbayle, T.; Langrognet, J.; Otmani, S. AWAKE Pilot Study no. 2: Testing Steering Grip SensorMeasures; Technical Report, IST-2000-28062, 2003.

12. Knipling, R. PERCLOS: A Valid Psychophysiological Measure of Alertness as Assessed by PsychomotorVigilance; Technical report. Federal Highway Administration (FHWA), MCRT-98 -006, 1998.

13. Ji, Q.; Yang, X. Real-Time eye gaze and face pose tracking for monitoring driver vigilance. Real-TimeImag. 2002, 8, 357–377.

14. Boverie, S.; Giralt, A.; Quellec, J. M. L. Diagnostic Fusion for in Vehicle Driver Vigilance Assessment.Proceedings of the 17th IFAC World Congress, Seoul, South Korea, 2008.

15. Horng, W. B.; Chen, C. Y.; Chang, Y.; Fan, C. H. Driver Fatigue Detection based on Eye Tracking andDynamic, Template Matching. Proceedings of the IEEE International Conference on Networking,Sensing and Control, Taipei, Taiwan, 2004.

16. Lal, S. K. L.; Craig, A.; Boord, P.; Kirkup, L.; Nguyen, H. Development of an algorithm for anEEG-based driver fatigue countermeasure. J. Safety Res. 2003, 34(3), 321–328.

17. Lal, S. K. L.; Craig, A. A critical review of the psychophysiology of driver fatigue. Biolog. Psychol. 2001,55, 173–194.

18. Lal, S. K. L.; Craig, A. Driver fatigue: Electroencephalography and psychological assessment. Psycho-physiology 2002, 39, 313–321.

19. Lal, S. K. L.; Craig, A. Electroencephalography activity associated with driver fatigue: Implicationsfor a fatigue countermeasure device. J. Psychophysiol. 2001, 15(3), 183–189.

20. Bergasa, L. M.; Nuevo, J.; Sotalo, M. A.; Vazquez, M. Real-time system for monitoring drivervigilance. IEEE Trans. Intell. Transport. Syst. 2006, 7(1).

21. Pavlidis, I.; Morellas, V.; Papanikolopoulos, N. A vehicle occupant counting system based onnear-infrared phenomenology and fuzzy neural classification. IEEE Trans. Intell. Transport. Syst.2000, 1, 72–85.

22. Wang, T.; Shi, P. Yawing Detection for Determining Driver Drowsiness. Proceedings of the IEEEInternational Workshop VLSI Design & Video Technology, Suzhou, China, 2005, May 28–30, pp. 373–376.


23. Renner, G.; Mehring, S. Lane Departure and Drowsiness - Two Major Accident Causes - One Safety System;No. TRL Report 220, Transport Research Laboratory, 1997.

24. Borbely, A.; Baumann, F.; Brandeis, D.; Strauch, I.; Lehmann, D. Sleep deprivation: Effect on sleepstages and eeg power density in man. Electroencephalogr. Clin. Neurophysiol. 1981, 51(5), 483–493.

25. Hu, S. Y.; Zheng, G. Driver drowsiness detection with eyelid related parameters by support vectormachine. Expert Syst. Applic. 2009, 36, 7651–7658.

26. Ogawa, K.; Shimotani, M. A drowsiness detection system. Mitsubishi Tech. Reports, 1997, 78, 13–16.27. Anund, A.; Peters, B.; Sehammar, H.; Lowden, A. Pilot 15 – Report: VTI; Swedish National Road and

Transport Research Institute: Linkoping, 2004.28. Lin, C. T.; Wu, R. C.; Liang, S. F.; Chao, W. H.; Chen, Y. J.; Jung, T. P. EEG-based drowsiness

estimation for safety driving using independent component analysis. IEEE Trans. Circuits Syst.-IReg. Pap. 2005, 52(12), 2726–2738.

29. Picot, A.; Caplier, A.; Charbonier, S. Comparison between EOG and High Frame Rate Camera forDrowsiness Detection. Proceedings of the IEEE Workshop on Applications of Computer Vision,Snowbird, 2009.

30. Jap, B. T.; Lal, S.; Fischer, P.; Bekiaris, E. Using EEG spectral components to assess algorithms fordetecting fatigue. Expert Syst. Applic. 2009, 36, 2352–2359.

31. Akerstedt, T.; Ingre, M.; Kecklund, G.; Anund, A.; Sandberg, D.; Wahde, M.; Philip, P.; Kronberg, P.Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in adriving simulator—The DROWSI project. J. Sleep Res. 2010, 19, 298–309.

32. Anund, A.; Kecklund, G.; Peters, B.; Forsman, A.; Lowden, A.; Akerstedt, T. Driver impairment atnight and its relation to physiological sleepiness. Scand. J. Work, Environ. Health 2008, 34(2), 142–150.

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