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International Journal of Neural Systems, Vol. 24, No. 3 (2014) 1450006 (15 pages)© World Scientific Publishing CompanyDOI: 10,1142/S0129065714500063

Worid Scientificwww.worldscientific.com

DETECTION OF DRIVING FATIGUE BY USINGNONCONTACT EMG AND ECG SIGNALS

MEASUREMENT SYSTEM

RONGRONG FU and HONG WANG*Laboratory of Bio-Mechatronic Engineering

School of Mechanical Engineering and AutomationNortheastern University, Shenyang 110189, P.R. China

*[email protected]. edu. en

Accepted 23 October 2013Published Onhne 11 December 2013

Driver fatigue can be detected by constructing a discriminant mode using some features obtained fromphysiological signals. There exist two major challenges of this kind of methods. One is how to collectphysiological signals from subjects while they are driving without any interruption. The other is to findfeatures of physiological signals that are of corresponding change with the loss of attention caused bydriver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) andelectrocardiograph (ECG) during the driving period. The noncontact data acquisition system was usedto collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fastindependent component analysis (FastICA) and digital filter were utilized to process the original signals.Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG(p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selectedas the combined characteristic to detect fatigue of drivers. The discrinainant criterion of fatigue wasobtained from the training samples by using Mahalanobis distance, and then the average classificationaccuracy was given by 10-fold cross-validation. The results showed that the method proposed in thispaper can give well performance in distinguishing the normal state and fatigue state. The noncontact,onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Keywords: Driver fatigue; sensor; EMG; ECG; Mahalanobis distance.

1. Introduction

Driver fatigue progressively impairs driver's aware-ness about external events,^ which may result insome abnormal driving behaviors. Although theremay exist some difficulties in estimating the serious-ness of driver fatigue, the fact that driver fatigue isone of the major hazards that contribute to trans-portation system^ is believed. Various studies haveshown that the driver drowsiness accounts for 16% ofall crashes and over 20% of the accidents in the high-ways,^^ Assessment for driver fatigue remains to bea big challenge to meet the demands of future intelli-gent transportation systems,® which can assist driver

to better cope with operation and reduce fatigue-related risks.

Researchers have developed a number of fatiguedetection technologies, according to the features thatused for fatigue recognition, these technologies canbe classified into four main categories:

• Gontextual features• Physiological features.• Drivers' performances and• Gombination of aforementioned features.

First, technologies-based contextual featuresalways rely on participants' self-report, these

* Corresponding author.

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R. Fu & H. Wang

features from different point of views include,

(i) driver-related: personality, sleep quality, circa-dian rhythm and physical condition.

(ii) vehicle-related: noise, seating comfort degreeand temperature;

(iii) road-related: monotony of road, density of vehi-cles and the number of lanes.

Questionnaire is always used for collecting suchcontextual features. Contextual features-based tech-nologies are perhaps the easiest method by whichthe extent of driver fatigue can be investigated fromthese features by some statistical methods.

Second, driver fatigue may be presented on someobservable physiological features, such as featuresreflect the brain activity from EEG (electroen-cephalogram), heart rate variability from ECG andskin conductance detected by EMG. Detecting driverfatigue from EEG based on the fact that EEG candirectly get abundant information on the human cog-nitive states. " ' Another popular feature is ECG, itcontains lots of fatigue relevant information. Rogadoet al. developed a driver fatigue detection systembased on HRV from ECG during driving period.EMG features were used by Hostens and Ramonunder high level monotonous driving condition. •

Third, performance-based technologies focus oninferring driver fatigue from monitoring the changesin driver's operational performances, such as reac-tion time, eye blinking frequency, eye-closure rate,eye tracking, steering angle, throttle/brake input,speed of movement, lane deviation,^'' head noddingand grasping position of driver's hand on steering

The previous three methods focus only on a cer-tain aspect, they can easily lead to inaccurate results.

(i) In the technologies based on contextual fea-tures, drivers can evaluate their efficiency decline,this results in poor performance during driving. Butself-feedback plays an important role in subjectivemeasurement and may be affected by subject's willand consciousness,^^ which cannot always reflect realobjectivity.

(ii) Studies of performance-based techniques can-not prove that these abnormal behaviors are relevantto driver's drowsiness state. Some other factors canalso result in those abnormal behaviors, like vehicletype, driver experience and driving conditions.^^

(iii) Some validation criteria used for fatiguerecognition were based on the image processing tech-niques, which is not always reliable because of thelimitation of current techniques for image process-ing. Although there is great value for fatigue detec-tion, many of these features were reported that theymay vary in different driving conditions.^° There aremoments when a driver still looks awake with wideopen eyes but does not process any information. -"Therefore, fusing as many features as possible is abetter way to get an accurate inference ' and makefatigue recognition more reliable. This is the last kindof method, which based on the combination of afore-mentioned features.

Physiological features, as mentioned above, con-tribute significantly to fatigue recognition because aperson usually has little control over them, whichmakes they could provide reliable source of infor-mation on a person's emotion.^'' Methods based onfused physiological features are perhaps the most log-ical method. ' ^ However, challenge still remains inthese methods. It is very difficult to acquire thesephysiological signals in a noncontact way becauseusually electrodes and wires need to be in contactwith driver directly. Then the driver will be dis-tracted by measuring electrode inevitably.

Our study does not use the change of driver-vehicle state or physical changes of subjects. Wefocus on detecting fatigue using fused physiologicalfeatures extracted from EMG and ECG. To over-come the weakness of physiological approach, record-ing electrodes were planted into car cushion to collectphysiological signals from biceps femoris of each sub-ject. Our data acquisition system consists of record-ing electrodes and amplifier. The design details areshown in Appendix A. We used noncontact measure-ment to avoid any interfering with driving opera-tions. Recorded signal contains both EMG and ECG.There are noise and artifact contained in recordedsignal induced by breath and movement of driverwhich are unavoidable.^^ Given the above, moni-toring some features of EMG and ECG may bea promising method for detecting drivers' fatigue.Meanwhile, considered components of the raw sig-nal recorded by the sensor from the biceps femorisof each subject, FastICA and digital filter were uti-lized to process the original signals in this paper.And with the help of them, not only the EMGand ECG could be separated, but also separated

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Detection of Driving Fatigue by Using Noncontact EMG and ECG Signals Measurement System

signals were free from noise and artifact contami-nation. Then some features of driving fatigue werestudied, based on the statistical analysis results givenby Kolmogorov-Smirnov Z test, peak factor of EMG[p < 0.001) and maximum of cross-relation curve ofEMG and EGG {p < 0.001) were selected as com-bined features to detect fatigue of drivers. Thus,Mahalanobis distance (MD) was used to obtain dis-criminant mode, performance of proposed methodwas validated on datasets with different states by 10-fold cross-validation. Based on these, a noncontactonboard vehicle drivers' fatigue detection system ispresented, which is capable of monitoring EMG andEGG, and detecting drivers' fatigue automatically.

2. Material and Methods

2.1. Subjects

Eight healthy volunteers (five males and threefemales, age: 24.75 ± 2.76) with no history of sleeprelated disease participated in these experiments. Inorder to make the subjects go into the fatigue statewithin the limited time, they all have driving expe-riences less than two years. This will be discussed inSec. 4. Alcohol, tea, coffee or any other drinks thatcan bring excitement to nerve were prohibited within24 h before the beginning of the experiment. Priorto the experiments, subjects familiarized themselveswith the operation of driving simulator. All theseexperiments were performed after we got informedconsent of the procedures of experiments from allparticipants.

2.2. Experimental design and protocol

The experiment time involved 2-2.5 h per subjectand was conducted almost between 12 pm to 3 pm.The experimental protocol (see Fig. 1) began with

Data recording

2h]—^

5minJ L

Data recording

lOmin 5minI

Driving period Post-dr ivingsitting

Idle Post -restsitting

Eig. 1. The procedure of experiment. It began withtwo-hour simulated driving, then was a post-driving sec-tion last 5 min. And next 10-min idle part without datarecording was given to subject for their rest. Another 5-min data post-rest part.

two-hour simulated driving, following that, there wasa post-driving section for 5 min. And next 10 minwere given to subjects for their rest, this part is calledas idle part. Another 5-min data was recorded afterthe idle part. During the idle period, subjects wereasked to take deep breaths and relax themselves asmuch as they can. No data recording occurred duringidle part.

Driving involves the participants with very fewroad stimuli, so the monotonous highway is adoptedas road scene here. Automatic control of vehiclewas selected with fine weather. All these designs arebecause of the fact that driver fatigue easily hap-pens under the monotonous conditions.^^ Monotonyof highway is classified as one exogenous factorsof fatigue. Automatic control of vehicle and fineweather are other two factors that provide driversthe highest monotonous task performance.

Data acquisition is realized by using the non-contact DAQ (data acquisition) system. The designdetail will be given in Appendix A. Recording elec-trodes were placed on the top surface of car seatcushion. Data were acquired from the biceps femoriswithout direct contact while the subjects were sittingon it.

2.3. Fatigue detection system

Fatigue detection is realized by two parts as shownin Fig. 2, classifier design and unknown status clas-sification decision. Glassifier design aims to get dis-criminant criterion of fatigue by using training setdata with known category of each record. In order togive accurate labels to data in training set, subjectswill raise their hands when they feel drowsy, whichis used to differentiate alertness and drowsiness ofsubject. Gonsidering the disturbance caused by thismovement, we do not analyze the data in five minutesbefore and after this movement. It can be seen as theboundary of alert and drowsy state. After data acqui-sition, raw signals will be analyzed by preprocess-ing, artifact removal and computation of features forfatigue estimation. In feature selection, some param-eters can be used as features of fatigue because theyhave consistent change among different subjects. Fol-lowing that, discriminant criterion will be obtainedby MD. Same processing has been applied to datawith unknown status, the classification result willbe given by the discriminant criterion obtained fromclassifier design. So Fig. 2 gives general idea of this

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Classifier design

Díiia intraining sel

Dala acquisition

Classification decision

Data analysis

!

Feature selection

1

1

Dala íuialysis Feature

of unknown status

DiscriminütHcnlcnon

1

\!/Classification

decision

\

ClassitT cationresults

Fig. 2. Fatigue detection system. Classifier design was finished in calibration part by training data. And classification ofsamples with unknown status was realized as lower part in Fig. 2. Program flow is represented by solid arrows. And thedashed arrows connect each two parts using similar process.

paper and the details will also be discussed in thefollowing sections.

2.3.1. Data pre-processing

The aim of data pre-processing is the extraction ofhomogeneous signal parts, which provides analysisand processing algorithm with the capacity of identi-fying EMG and EGG components clearly. Recordedsignal can be defined as the sum of different pro-cesses:

N

x{t) = A 'emgV (1)¿=1

where A is an unknown mixing matrix which repre-sents weights of EMG signal (semg) and EGG signal(secg), the term of Ni{t) includes various artifactsto be removed to get EMG and EGG, such as base-line noise induced by breath and body movementof driver and the 50 Hz electronic noise. Gonsideringvarious components of raw signal recorded by sensorfrom biceps femoris of each subject, FastlGA anddigital filter are utilized to process original signals toextract EMG and EGG in this paper.

The goal of FastlGA^^"' '' is to estimate theindependent components from observed mixture X,which is obtained from mixing model as X = AS,where A is an unknown mixing matrix, and S isunknown source signals. So FastlGA focuses on esti-mating de-mixing matrix W to get independentsource U form de-mixing model U = WZ, where

Z is whitened data of observed data X. Whenusing the FastlGA, the setup information shouldbe noticed first. There are two kinds of operationfor de-correlation in FastlGA: symmetric and defla-tion. For this paper, symmetric operation is pre-ferred for de-correlation approach. In most cases,these two approaches can give good performance forsource extraction generally, but when consideringthe constraint on the computational burden, sym-metric method should be preferred over the defla-tion method. And another important information forFastlGA setup is the adaptation of the nonlinear-ities. In the general case, it is necessary to makethe algorithm converge to the right solution, i.e. tomake the estimator consistent, and to guarantee localstability. Especially in the FastlGA algorithm, adap-tation of the nonlinearities will increase the statis-tical performance of the method. Hyvarinen^'' gavethe possible nonlinearity functions: gi = tanh(aiti),g2 = uexp{-a2u'^/2) and 53 = u^, where 1 < ai < 2,02 ~ 1 and 1 is the default value of ai and 02. Non-linearity functions used here was 'tanh', it is a goodgeneral-purpose nonlinearity function.

EMG and EGG signal can be separated by usingFastlGA, but the results still present little signalaliasing of each other. Baseline noises are still con-tained in them, which becomes the reason to usedigital filter to process signal further. And with thehelp of them, we can separate EMG and EGG, andmeanwhile separated signals are free from noise and

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Detection of Driving by Using Noncontact EMG and ECG Signals Measurement System

artifact contamination. Tbe result will be sbown inSec. 3.1.

2.3.2. Feature selection

Some features of driving fatigue based on EMG andECG were studied. Tbey all gave some cbanges astime went on, but unfortunately some of them couldnot give consistent cbange witb fatigue degree amongdifferent subjects. And tbe aiming of feature selec-tion is to get features to detect fatigue state witboutaffection of randomness of pbysiological signals.

Based on tbe statistical analysis results given byKolmogorov-Smirnov Z test, peak factor of EMG{p < 0.001) and maximum of cross-relation curveof EMG and ECG (p < 0.001) sbowed tbe signifi-cant difference of two sets of value, and tbey gaveconsistent trend among different subjects, so tbeywere selected as combining feature to detect fatigueof drivers.

More specifically, peak factor of EMG signal canbe defined as follow:

Fc = — , (2)

wbere A is amplitude of EMG and x^^s representstbe square root of EMG.

Metbod of obtaining maximum of cross-relation^ ' ^ curve of EMG and ECG can be roughlysbown in Fig. 3. hi and m¿ were obtained by dividingECG and EMG into tbe overlapping segments using4-s sfiding windows witb 50% overlap. ' Maximumvalue of cross-relation between hi and m¿ was namedas cor-max.;. All tbese maximum values were used asfeature named maximum of cross-relation curve.

2.4. Discriminant criterion

was used in tbis paper aiming to get discrim-inant criterion, wbicb is a distance measure basedon correlations between variables. It is often used tomeasure tbe degree of abnormality as compared tonormal conditions. Tbe calculation of MD between avector X and group G is given as follows.

- 1

d(x,G) = (3)

wbere /x and J2 ^ê tbe mean vector and tbe covari-ance matrix of G. Tbe decision rule can be statedvery simply: to classify a feature vector measure tbe

hn-l

cor_maxi

cor_max2

cor_max3

cor_maxn,

cor_maxn.i

cor_max„

mi

m3

in„.i

Fig. 3. Procedure for maximum of cross-relation curve.Epoch EMG and EGG into the overlapping segmentsusing sliding windows with 50% overlap to compute eachmaximum value from cross-relation curve. All these maxi-mum values are used as feature named maximum of cross-relation curve of EMG and EGG.

MD from vector x to eacb mean vector ^t¿, assignX to tbe category of tbe nearest mean. ^ can beestimated by tbe samples in training set as,

-Si = TF 7 X - Mz X - Hif ¿ - 1 , 2 , 4

- ^ -^1 + 77-^2, (5)

wbere iV¿ denotes tbe total number of data points ingroup Gi. Mniean represents tbe mean vector of fiând //2, discriminant function can be estimated as.

(6)

3. Results

In Sec. 2.3, we gave tbe principle of tbe metbodsinvolved in tbis paper. To see bow tbese work in tbissection, we sbow some results accordingly.

3.1. Data pre-processing

Recorded data from biceps femoris of eacb subjectincluded some noise and artifact as mentioned, wbicbwere inevitable. 15-s epocb of observed signals wereused as an example and independent components(ICs) can be obtained by FastICA, as sbown in Fig. 4.

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R. Fu & H. Wang

FastICA

I 2

I O-2

-4

h K

(a)

Fig. 4. Demonstration of signal separation by FastICA. (a) 15-s epoch of observed signals containing ECG, EMG andbeiseline noise, (b) Corresponding independent components (ICs) roughly for heart activity (top one) and muscle activity(lower one).

The stability indexes of IGA decomposition givenby repeating the estimates''^ using FastlGA for 100times are 0,9841 and 0,9840, respectively, which ver-ify the stability of IGs,

From Fig, 4(b), detail of IGs shows that EMG andEGG were roughly separated, but they still cannot beconsidered as final results because there existed smallextent aliasing in both of them and baseline noise

contamination was not filtered. So these signals needfurther processing to achieve satisfactory results.

According to previous studies, most of the infor-mation contained in EGG are found between 0.5 and50Hz.^° EMG should have the frequency range from10 to 300 Hz.'*! By using band-pass filter, EGG andEMG are shown in Fig, 5,

10 15

Fig, 5, The ECG and EMG, Comparing with signals' detail shown in Fig. 4(b), the baseline noise contamination andsmall extent aliasing are elinainated by using band-pass filter.

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3.2. Features

In the two-hour driving section, every 10 min, 30-slength of data were extracted. Other two 30-s lengthof data were also extracted from post-driving partand post-rest sitting part, respectively. So in total,we got 14 periods of 30-s time epochs. All theextracted data were pre-processed in the same wayas mentioned in Sec. 3.1.

As discussed in Sec. 2.3.2, peak factor and max-imum of cross-relation curve were selected as thecombined features to detect fatigue of drivers. Wegot similar trend among different subjects in thesetwo features, but the main problem is that dif-ferent amplitudes of alert and drowsiness tend tofluctuate significantly within different time periodof different subjects,"*^ so normalization is usedhere.

The upper sub-figure in Fig. 6 was plotted byusing all peak factor (Fc) after normalization, andmaximum of cross-relation curve (cor_max) after nor-malization was shown in the lower sub-figure inFig. 6. The horizontal axes represented the time sec-tion. These two features gave the consistent tendencyin detecting the state of fatigue.

3.3. Discriminant case

Detection of driver fatigue is similar to medical diag-nosis, which can often be understood as a supervisedlearning. ^"*^ To construct discriminant model, wechoose data of one subject as example. 130 samplesfrom the first experiment were employed as trainingset. Among them, there are 65 alert samples versus65 fatigue samples. And next day, at the same time,we have the driving section experiment on the samesubject. Then 30 samples was selected as test set. ^So we obtained a dataset with 160 samples contain-ing two states, subset Gi is alert state and subsetG2 is fatigue state. To measure the success of clas-sification process, we split these data into trainingand test set.'' *''® Considering two features Fc andcorjnax, which are mentioned in Sec. 3.2, discrimi-nant criterion could be obtained by MD (see Sec. 2.4)using data in training set as,

W = -2.5952Fc - 8.5541cor.max -f3.3985. (7)

Using the test features, confusion matrix could beseen as Table 1 below.

One of the most common techniques to provethe stability of algorithm is replication of training

6 7 8time sectioii

7 8 9time section

10 11 12 13 14

Fig. 6. The two normalized features used to estimating the driver fatigue. The upper one is peak factor {Fc), and thelower sub-figure is maximum of cross-relation curve (cor_max).

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Table 1. Confusion matrix.

Actual class

Alert Fatigue Precision

Predicated classAlert 15 0Fatigue 4 11

Recall 0.7895 1

1

0.7333

Accuracy = 0.8667

Table 2. Mean test results given by10-fold cross validation.

PrecisionAlertEatigue

RecallAlertEatigue

El scoreAlertEatigue

Accuracy

Mean of 10 folds

0.90400.8345

0.81620.9138

0.85720.8718

0.8650

by A"-fold cross-validation.4^'''i Then 10-fold cross-

validation was used for assessing the classification

accuracy,' ''"'' the mean values of test results are

shown in Table 2.

4. Discussion

First, about design of the experiment protocol, there

is a fact that simulator can cause higher sleepiness

levels than real driving.^^ xhis could be possibly

explained by the fact that in simulated driving condi-

tion with fewer stimuli there were lots of movements

that can be avoided, meanwhile, subjects driving in

simulator is much safer than real condition which

makes them feel relaxed, so they may go into fatigue

state easily.

Second, for selection of subjects, novice drivers

(driving experience less than 2 years) accept and

process almost all the information, which costs a lot

of energy and speed the onset of fatigue feeling in

short time.^ ' '

Third, in data acquisition, recorded data involved

EMG, EGG and artifacts. This depends on the

location of data collection, and heartbeat causes a

relatively high electric field in human body. This can

explain the larger amplitude of EGG than that of

EMG in Fig. 4(a). After obtaining EMG and EGG,

we still have some interests in lower frequency parts

of each IGs. As we know that low frequency compo-

nents represent the trend of signal, so in Fig. 7, we

plotted the low-frequency residual of IGl and IG2,

respectively.

There are some interesting facts in Fig. 7.

Under driving situation, resident of IGl give us

15

Eig. 7. The residual of ICs. The signal in upper sub-figure is residual of ICI, and the lower one is residual of IC2.

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0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Fccor-max

alert fatigue

Fig. 8. The bar graph with error of two features. Similar graphs can be useful for comparing significant differences offeatures between the subgroups.

approximately six ups and downs during the 15 s,which is corresponding to the fact that normal rangeof breaths is between 12 and 25, and the average rest-ing respiratory rate for adults is 12-20 breaths perminute. ^ And compared to IGs contained baselinenoise as shown in Fig. 4(b), the resident of IG2 gavethe trend of EMG. To make the recognition of thistrend easily, we selected a section of postural signalas example, which means that the change of base-line is not induced by muscle contraction, becausemuscle contraction can cause EMG amplitude growsuddenly. So these may explain the observation ofbaseline noise induced by respiration and body move-ment of subjects. But there may still exist some otherfactors which can lead to the change of baseline.These need further studies.

Fourth, we used FastlGA to realize the rough sep-aration of EMG and EGG, but for all IGA includingFastlGA, they have two intrinsic limitations, theseare (a) magnitude and scaling ambiguity: in IGAthere is no sufficient information to estimate the truevariance of independent components and (b) permu-tation ambiguity: IGA gives unspecified order of esti-mated independent components.''' And fortunately,these ambiguities are insignificant in application ofthis paper. On one hand, pre-whitening providesgreat solution for the first ambiguity. On the otherhand, EMG and EGG have inherent difference, they

can be recognized without influence of the permuta-tion ambiguity.

Fifth, significant difference of features was foundby Kolmogorov-Smirnov Z test, in order to show this

14

12drivingsection

Fc

A

1 1

0 1 2 3 5 6 7 8 9 10 11 12 13 14 15time section

02

0.15

I 01

005

dnvingsection

cor-max

^—i* * -

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15time section

Fig. 9. The trends of features which given by meanvalue with error bar of each data section, as we men-tioned above, the data used to analyze was composed 14data sections, the first 12 sections is extracted from driv-ing section, and the data section with '*' means that thisdata is from post-driving section, and the last data with'**' is the post-rest sitting data.

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clearly, tbe bar grapb witb error of tbese two featuresis sbown in Fig. 8.

And in order to see tbe trend of features, meanvalue witb error of data from 14 periods is illustratedin Fig. 9. Tbese two features present tbe similartrend, tbe upper sub-figure gives trend of peak factor(Fc) and trend of maximum of cross-relation curve(cor_max) is sbown in lower sub-figure of Fig. 9. Webave extracted 14 sections data to be processed, bor-izontal axes represent number of time section. Dur-ing tbe 2-b driving period tbey botb bave upwardtrend generally and downward trend occurs in botbpost-driving and post-rest pbase, wbicb means tbatdriver fatigue increased witb tbe longer driving timeand decreased wben driving pbase was over. Anddownward trend last to post-rest pbase after 10-minidle part, so post-rest pbase bas more lower indicestban post-driving pbase.

5. Conclusion

Viewed from an ergonomie aspect witb respect to tbeproduct development, a noncontact sensitive mea-surement of driver fatigue is investigated during tbecontinuous driving. And tbere are tbree main find-ings in tbe current study: (i) It provides a bardwaredesign detail of DAQ system tbat realize noncontactsensitive measurement of fatigue, (ii) Tbis currentstudy detects driver fatigue based on an objectiveevaluation of their driving abilities by using the fusedEMG and ECG features, (iii) It provides discrimi-nant criterion of fatigue by using MD witb tbe ulti-mate objective of reducing drivers' fatigue and savehuman lives. Meanwbile, 10-fold cross-validation wasused to validate performance of discriminant crite-rion. Tbe results sbowed tbat tbe metbod proposedin tbis paper can give good performance in distin-guisbing alert state and fatigue state. Noncontactmeasurement can be realized, wbicb provides a newmetbod to detect driver fatigue.

However, it should be noted tbat altbougb tbisstudy bas produced some promising results forfatigue recognition, tbere still exist some improve-ments tbat need to be aware of. First, tbe algo-rithm used for detecting drivers' fatigue in tbispaper is simple to be implemented in bardware. Itfocused on development of a simple, low-cost andonboard vebicle drivers' fatigue detection device.Tbe detection accuracy can be improved by using

more sophisticated features and classifiers. Somesenior autbors used information quantification,^^conventional and wavelet coberence^"' ' as fea-tures. Some algoritbms integrated tbe nonlinearscience-based cbaos tbeory, ^"^^ wavelets,^^" '' signalprocessing tecbnique neural networks^^"'' and pat-tern recognition tecbniques, such as cbaos-wavelet-neural network algoritbm, spiking neural networksalgoritbm, mechanism-based hierarcbical neural net-works,'' some enbanced probabilistic neural networkalgoritbm.^° Tbese algoritbms can be used in tbisfatigue detection system, but more additional workand more powerful computing capabilities are stillneeded. Second, modern tecbnologies, sucb as wire-less communication and some cell pbone devices,®^can be involved in tbis system. Tbe wireless com-munication can make signal measurement more eas-ier, like infra-red or Bluetooth tecbnologies. Wbenfatigue is detected, some alert text can be sent totbe cell pbone. Tbird, tbis system can be extendedto include otber features of ECG to monitor cardiacactivities and detect tbe relevant cardiac pathologies.

Acknowledgments

Tbis work was supported by Natural Science Founda-tion of Cbina (Project No. 61071057) and Fundamen-tal Research Funds for tbe Central Universities ofCbina (Project No. N100603003). Tbe autbor wouldfike to tbank Dr. Shahni Puwar, Dr. Wenbo Zbaoand Dr. Morteza Delgir for belpful discussions abouttbe manuscript. In addition, tbe autbors would liketo tbank tbe reviewers and tbe editor, wbose inputssignificantly improved tbis manuscript.

Appendix A: DAQ System

In application of tbis paper, two pieces of con-ductive knit fabric with tbe size of 12 cm x 22 cm

conductive fabric

cloth

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Fig. A.I. Noncontact electrode.

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Fig. A.3. Main circuit board of finished amplifier.

was sewn on top surface of car cushion paral-lely. Reference electrode located on the left anklebone.

This conductive knit fabric is silver-plated nylonthat is stretched in both directions. It is highly con-ductive with a surface resistivity of < 1 Q/sq. It cancollect data when the subject wears normal pantswith width less than 2 mm. Physiological signals withvariable electric potential can be seen as AC power.Skin and conductive knit fabric were regarded as thetwo plates of parallel plate capacitor, meanwhile, thepants were seen as insulation. Principle of capacitive-coupling is satisfied in this case. So the basic theoryof noncontract electrode can be described as Fig. A.I.These two pieces of conductive knit fabric were usedas electrodes to collect mixed signals of ECG andEMG data in noncontact way.

This noncontact electrode is very suitable forlong-term physiological signal acquisition. However,comparing with contact electrode, data collectedby using noncontact electrode is much weaker andsignal-to-noise-ratio is relatively lower. Then, thecrucial aspect was the design of amplifier.

Taking into account all these aspects, we designedan amplifier with the structure shown in Fig. A.2, themain circuit board of finished amplifier can be seenas Fig. A.3.

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4. S. G. Dastidar and H. Adeli, Wavelet-clustering-neural network model for freeway inciden detection,Comput.-Aided Civil Infrastmct Eng. 18(5) (2003)325-338.

5. A. Karim and H. Adeli, Incident detection algorithmusing wavelet energy representation of traffic pat-terns, J. Transport. Eng. 128(3) (2002) 232-242.

6. X. Jiang and H. Adeli, Wavelet packet-autocorrelation function method for traffic fiow pat-tern analysis, Comput.-Aided Civil Infrastmct. Eng.19(5) (2004) 324-337.

7. A. Karim and H. Adeh, Fast automatic incidentdetection on urban and rural freeways using thewavelet energy algorithm, J. Transport. Eng. 129(1)(2003) 57-68.

8. H. Cai and Y. Lin, A roadside ITS data bus pro-totype for future intelligent highway, IEEE Trans.Intell. Transport. Syst. 9 (2008) 344-348.

9. M. Ahmadlou, H. Adeli and A. Amir, Graph theoret-ical analysis of organization of functional brain net-works in ADHD, Clm. EEC Neurosci. 43(1) (2012)5-13.

10. M. Ahmadlou and H. Adeli, Euzzy synchro-nization likelihood with application to attention-deficit/hyperactivity disorder. Clin. EEG Neurosci.42(1) (2011) 6-13.

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35. M. Ahmadlou and H. Adeli, Visibility graph similar-ity: A new measure of generalized synchronization incoupled dynamic systems, Physica D 241(4) (2012)326-332.

36. M. Ahmadlou, H. Adeli and A. Adeli, Euzzy synchro-nization likelihood-wavelet methodology for diagno-sis of autism spectrum disorder, J. Neurosci. Meth.211 (2012) 203-209.

37. Z. Sankari, H. Adeli and A. Adeli, Intrahemi-spheric, interhemispheric and distal EEG coherencein Alzheimer's disease, J. Clin. Neurophysiol. 122(5)(2011) 897-906.

38. L. Xu, Best harmony, unified RPCL and automatedmodel selection for unsupervised and supervisedlearning on gaussian mixtures, three-layer nets and

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39. J. Himberg and H. A. Esposito, Validating the inde-pendent components of neuroimaging time series viaclustering and visualization. Neuroimage 22 (2004)1214-1222.

40. Z. Sankari and H. Adeli, HeartSaver: A mobilecardiac monitoring system for auto-detection ofatrial fibrillation, myocardial infraction and atrio-ventricular block, Comput. Biol. Med. 41(4) (2011)211-220.

41. V. V. Tscharner, B. Fskofier and P. Federolf,Removal of the electrocardiogram signal from surfaceEMG recordings using non-linearly scaled wavelets,J. Electromyograph. Kinesiol. 21 (2011) 683-688.

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44. S. Ghosh-Dastidar and H. Adeli, Spiking neural net-works. Int. J. Neural Syst. 19(4) (2009a) 295-308.

45. S. Chosh-Dastidar and H. Adeli, A new supervisedlearning algorithm for multiple spiking neural net-works with application in epilepsy and seizure detec-tion. Neural Netw. 22 (2009) 1419-1431.

46. M. Ahmadlou, H. Adeh and A. Adeli, Fractalityanalysis of frontal brain in major depressive disor-der. Int. J. Psychophysiol. 85 (2012) 206-211.

47. F. Garcia-Cuesta, I. M. Galvan and A. J. Castro,Recursive discriminant regression analysis to findhomogeneous structures, Int. J. Neural Syst. 21(1)(2010) 95-101.

48. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr,Mixed-band wavelet-chaos-neural network method-ology for epilepsy and epileptic seizure detection,IEEE Trans. Biomed. Eng. 54(9) (2007) 1545-1551.

49. B. Baruque, E. Corchado and H. Yin, The S2-ensemble fusion algorithm. Int. J. Neural Syst. 21(6)(2011) 505-525.

50. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Prin-cipal component analysis-enhanced cosine Radialbasis function neural network for robust epilepsy andseizure detection, IEEE Trans. Biomed. Eng. 55(2)(2008) 512-518.

51. M. Ahmadlou, H. Adeli and A. Adeli, New diagnos-tic EEG markers of the Alzheimer's disease usingvisibility graph, J. Neural Transmission 117 (2010)1099-1109.

52. S. Ghosh-Dastidar and H. Adeli, Improved spikingneural networks for EEG classification and epilepsyand seizure detection, Integr. Comput.-Aided Eng.14 (2007) 187-212.

53. M. Ahmadlou and H. Adeli, Functional communityanalysis of brain: A new approach for EEG-based

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54. P. Philip, P. Sagaspe, J. Taillard, C. Valtat, N.Moore, T. Akerstedt, A. Charles and B. Bioulac,Fatigue sleepiness and performance in simulatedversus real driving conditions. Sleep 28(12) (2005)1511-1516.

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59. Z. Mehboob and H. Yin, Information quantificationof empirical mode decomposition field potentials.Int. J. Neural Syst. 21(1) (2011) 49-63.

60. Z. Sankari, H. Adeli and A. Adeli, Wavelet coher-ence model for diagnosis of Alzheimer's disease. Clin.EEC Neurosci. 43(3) (2012) 268-278.

61. Z. Sankari and H. Adeli, Probabilistic neural net-works for EEG-based diagnosis of Alzheimer's dis-ease using conventional and wavelet coherence, J.Neurosci. Meth. 197(1) (2011) 85-92.

62. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, Awavelet-chaos methodology for analysis of EEGs andEEG sub-bands to detect seizure and epilepsy, IEEETrans. Biomed. Eng. 54(2) (2007) 205-211.

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64. M. Ahmadlou, H. Adeli and A. Adeli, Fractalityand a wavelet-chaos-neural network methodology forEEG-based diagnosis of autistic spectrum disorder,J. Clin. Neurophysiol. 27(5) (2010) 328-333.

65. H. Adeli, Z. Zhou and N. Dadmehr, Analysis of EEGrecords in an epileptic patient using wavelet trans-form, J. Neurosci. Meth. 123(1) (2001) 69-87.

66. X. Jiang, S. Mahadevan and H. Adeli, Bayesianwavelet packet denoising for structural systemidentification. Struct. Control Health Monit. 14(2)(2007) 333-356.

67. Z. Zhou and H. Adeli, Time-frequency signal analysisof earthquake records using Mexican hat wavelets,Comput.-Aided Civil Infrastruet. Eng. 18(5) (2003)379-389.

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68. H. Adeli and X. Jiang, Dynamic fuzzy wavelet neuralnetwork model for structural system identification,J. Struct. Eng. 132(1) (2006) 102-111.

69. A. B. Senouci and H. Adeli, Resource schedulingusing neural dynamics model of Adeli and Park, J.Construct Eng. Manag. 127(1) (2001) 28-34.

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75. H. Adeli and H. S. Park, A neural dynamics modelfor structural optimization theory, Comput Struct57(3) (1995) 383-390.

76. X. Jiang and H. Adeh, Pseudospectra, MUSIG,and dynamic wavelet neural network for damage

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77. A. Samant and H. Adeli, Enhancing neural net-work incident detection algorithms using wavelets,Comput.-Aided Civil Infrastruct Eng. 16(4) (2001)239-245.

78. D. Wu, K. Warwick, Z. Ma, M. N. Gasson, J. G.Burgess, S. Pan and T. Z. Aziz, Prediction of Parkin-son's disease tremor onset using radial basis functionneural network based on particle swarm optimiza-tion, Int J. Neural Syst 20(2) (2010) 109-116.

79. Q. Gong, W. Yu and T. Ghai, Gascade process mod-eling with mechanism-based hierarchical neural net-works, Int J. Neural Syst 20(1) (2010) 1-11.

80. M. Ahmadlou and H. Adeli, Enhanced probabilisticneural network with local decision circles: A robustclassifier, Integr. Comput.-Aided Eng. 17(3) (2010)197-210.

81. Z. Sankari and H. Adeli, HeartSaver: A mobilecardiac monitoring system for auto-detection ofatrial fibrillation, myocardial infraction, and atrio-ventricular block, Comput BioL Med. 41 (2011)211-220.

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