OPERATING EFFICIENCY STUDY OF AVIATION SECURITY SCREENERS USING THE EYE-TRACKING TECHNOLOGY AND FUZZY MODELS

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 05, May 2019, pp. 328-338, Article ID: IJCIET_10_05_034
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJCIET&VType=10&IType=5
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
OPERATING EFFICIENCY STUDY OF
AVIATION SECURITY SCREENERS USING THE
EYE-TRACKING TECHNOLOGY AND FUZZY
MODELS
A. A. Gladkikh
Ministry of Education and Science of the Russian Federation, Ulyanovsk State Technical
University, Severnyj venec, 32, 432027, Ulyanovsk, Russia
An. K. Volkov, Al. K. Volkov, V. M. Il'in, YU. V. Sulimov
The Federal Air Transport Agency, Ulyanovsk Civil Aviation Institute, Mozhaisky Street,
8/8, 432071, Ulyanovsk, Russia
N. A. Pchelin
Ministry of Education and Science of the Russian Federation, Ulyanovsk State Technical
University, Severnyj venec, 32, 432027, Ulyanovsk, Russia
ABSTRACT
This paper describes studies related to the influence of the fatigue factor on the
oculomotor activity of aviation security screeners. Analysis of possible instrumental
diagnostic methods of the functional status of aviation security screeners was
conducted and the Eye-tracking technology was selected as the one with the greatest
advantages. Overview of the experience in the application of the Eye-tracking
technology when diagnosing the functional status of aviation security screeners is
presented. In this paper for the first time as follows from experimental studies using
the Eye-tracking technology potential fatigue markers of aviation security screeners
with the most accurate changes were identified consistent with earlier studies pursued
by other authors. As the result of solving the problem of the statistical hypothesis
testing it is found that such oculomotor reactions as the blink frequency, the blink
duration average and the pupils diameter average can be used for diagnosing the
functional status of aviation security screeners whereas the saccade frequency did not
change for certain in the period of studies pursued and is not potentially useful for
monitoring. It was demonstrated that the Sugeno fuzzy model based on the subtractive
clustering and ANFIS-learning does better approximate the dependence between
oculomotor activity indicators of aviation security screeners and the prohibited items
detection efficiency in comparison with other models. Model fidelity by the Root-
Mean-Square Error criterion on the learning sample is 0,0348 and on the test sample
is 0,0858 as the case may be. Scientific and theoretical value of this paper involves
developing scientific knowledge related to the influence of the fatigue factor on the
oculomotor activity of aviation security screeners in the course of the working activity.

A. A. Gladkikh, An. K. Volkov, Al. K. Volkov, V. M. Il'in, YU. V. Sulimov, N. A. Pchelin
Key words: Aviation security, Aviation Security screener, Fuzzy Models, Subtractive
Clustering, Eye-Tracking Technology, Fatigue and Student’s t-test
Cite this Article: A. A. Gladkikh, An. K. Volkov, Al. K. Volkov, V. M. Il'in, YU. V.
Sulimov, N. A. Pchelin, Operating Efficiency Study of Aviation Security Screeners
Using the Eye-Tracking Technology and Fuzzy Models, International Journal of Civil
Engineering and Technology 10(5), 2019, pp. 328-338.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=5
1. INTRODUCTION
In the context of moving to the risk-oriented approach to the security provision, International
Civil Aviation Organization recommends operators to develop and to implement the fatigue
risk management system (FRMS). It is interesting to note that FRMS implementation
recommendations exist only for the air staff. However, under conditions of the expanding
passenger flow, the workload of aviation security screeners is increased. It means that the
fatigue risk is raised up. This can lead to the great number of mistakes (a free pass of
prohibited items and substances) and as the consequence committing of unlawful interference
acts. At the present time a traditional regulatory approach to the personnel fatigue control is
implemented with respect to aviation security service specialists. Approach involves
regulating the allowed working time and also rest periods between working cycles. At the
present time, systems diagnosing the functional status of aviation security screeners apply the
following methods:
1) Electroencephalography (Belyavin et al. 1987; Fabiani et al. 2000) [1, 2]. This method
is the reliable diagnostic facility of human cognitive processes and it is applied, for example,
for diagnosing of the driver fatigue (Berka et al. 2004) [3]. Despite all advantages, the
application of this method for diagnosing the functional status of aviation security screeners
in the working environment is not often possible because of the adjustment of facilities.
2) Cardiac activity analysis (Egelund 1982) [4]. Effects of the heart rate reduction and
their connection with the driver’s fatigue were demonstrated in the paper (Wierwille et al.
1981) [5]. This method does also have disadvantages: the low-resolving ability over time and
nuisance because of wires which are also sensitive to interferences.
3) Galvanic skin reaction (Dorokhov et al. 1998) [6]. This method, primarily,
characterizes the «vegetative tonus» of the human and it is used for diagnosing the emotional
tension. This method does also have disadvantages: the susceptibility to the influence of
temperature changes, humidity, drafts, and also, in some cases, the discrimination complexity
of various functional statuses of the aviation security screener (waves of the one and the same
form and sequence can be arisen by various reasons).
4) Analysis of speech characteristics (Heitmann et al. 2001) [7]. At the present time, the
development of this system together with the application of recognition mechanisms of
speech emotions for detecting of the human critical fatigue status is in progress (Batliner et al.
2006) [8]. Advantage of this approach is the absence of wiring channels for retrieving
information. This meets requirements for measuring fatigue indicators in day-to-day activities
of specialists. From the viewpoint of the aviation security screener, dialoging is an
unassuming but abstractive and subsidiary task.
5) Analysis of oculomotor patterns (Poole et al. 2006) [9]. Oculomotor activity of the
human-aviation security screener objectively reflects important psychic functions of their
activity. Modern Eye-tracking technologies are unassuming and require a simple video
recording for the registration of the large range of oculomotor activity parameters. The most
common information related to movements of eyes used for studying cognitive processes and

Operating Efficiency Study of Aviation Security Screeners Using the Eye-Tracking Technology
and Fuzzy Models
problems is provided by such characteristics as staring and saccades. Saccades are «fast
coordinated jumping movements of eyes between staring points» (Poole et al. 2006) [9].
As the result of the conducted analysis it may be concluded that the most perspective
fatigue diagnostic tool of aviation security screeners against the background of the
ergonomics, usability, registration quality of biological signals and information capacity of
diagnosed parameters is the Eye-tracking technology.
Eye-tracking technology became widely used in monitoring facilities of the functional
status of aviation security screeners in various fields of activity. Experimental studies
connected with diagnosing of functional statuses of drivers are actively pursued according to
evaluation results of the oculomotor activity (Sommer et al. 2008; Coetzer et al. 2011; Devi et
al. 2008) [10-12]. In papers (Di Stasi et al. 2012; Ceder 1977) [13, 14] authors found that the
reduction of the frequency, amplitude and speed of macro saccades could potentially be
markers reflecting the fatigue status of drivers. Results of studies are relevant to the paper
(Schleicher et al. 2008) [15] in which authors found that the reduction of the longstanding
fixation quantity with the simultaneous increase in the number of short fixations could be
used for estimating of drivers’ fatigue. In the paper (Wright et al. 2001) [16] it was made clear
that changing of the saccade amplitude is a good marker of the fatigue status of crew teams.
Similar results were obtained when studying the activity of air traffic control officers
(McGregor et al. 1996) [17]. Moreover, one of potential markers of the fatigue status of
aviation security screeners is the increase in the number of blinks (Van Orden et al. 2000)
[18]. Pupils’ diameter can also be the informative indicator of changes in the human
functional stress level. As the result of the conducted analysis it may be concluded that
various indicators of the oculomotor activity such as the number and the amplitude of
saccades, staring, blink frequency and pupils’ diameter can be used as potential markers of the
aviation security screeners’ fatigue. That is why the key objective is to study and to
substantiate physical and physiological reactions potentially suitable for being markers of
functional status changing of aviation security screeners.
With that knowledge in mind, the urgency of this paper is due to the availability of such
remaining challenge as the absence of decision-making support models by the operating
efficiency evaluation of aviation security screeners taking into consideration the influence of
the fatigue factor.
2. EXPERIMENTAL STUDY OF THE INFLUENCE OF THE FATIGUE
FACTOR ON THE OCULOMOTOR ACTIVITY OF AVIATION
SECURITY SCREENERS
Problem-solving of searching for oculomotor activity markers being in charge of the fatigue
functional status of aviation security screeners was pursued directly in the course of the
working activity. 22 aviation security screeners of the International Airport «Ulyanovsk
Vostochny» participated in the study. Sample consisted of men and women at the ages from
21 to 60. Oculomotor activity patterns of aviation security screeners were registered using the
mobile eye-tracker «Eye Tracking Glasses 2.0» of «SensoMotoric Instruments» and the
software «SMI BeGaze 3.7». Ocular movements were registered binocularly in other words
by tracking movements of both eyes of the persons under consideration using special high
precision infrared cameras. In addition to the above, for the objective estimation of aviation
security screeners’ fatigue we used the variation cardio-intervalometry (VCM) method which
gives an opportunity to determine the functional status of the vegetative nervous system by
heart function rhythm parameters. Method is included in the psychophysiological testing

facility UPFT-1/30 «Psychophysiologist» («Psychophysiologist»). Analyzed indicators are
presented in the Table 1.
Table 1 Registered parameters of the aviation security screener’s activity
Designation Parameter
Data of the Eye-tracking technology
SF, unit/sec saccade frequency
BF, unit/sec blink frequency
BDA, msec blink duration average
PDA, mm pupils diameter average
Cardiac activity data using the VCM method
MoRR, msec Mode of RR–intervals
SDNN, msec Root-Mean-Square deviation of RR–intervals
Heart rate, beats per
minute
heart rate
VSR, relative units Estimation of the functional status
LSR, points Level of the functional status
At first, the control study of the group of aviation security screeners was pursued. This
group of aviation security screeners corresponded to the control group characterizing the
operating efficiency status. At the beginning of the working shift after the 20-minute work
using the X-ray TV introscope the aviation security screener under consideration went to the
specially allotted room where he/she sat in front of the notebook with the installed simulator
for aviation security screeners. He/she put on eye-tracking glasses and after the calibration of
facilities he/she should interpret 20 text X-ray patterns. Simultaneously, information by VCM
is received. Thereupon the control series ended. Sequence of pursuing the test study
characterizing the fatigue status was completely analogous to the control study with the
exception that testing the group of aviation security screeners was carried out in the end of the
working shift (early in the morning after processing the flight) after the 20-minute work using
the X-ray TV introscope. This very period was selected based on the inquiry results of
aviation security screeners as the most fatigue for all of them.
Analysis of measurement results of the vegetative nervous system status in accordance
with the VCM method showed that results of the control series LSR fall within 4-5 pursuant to
the verbal interpretation means «near to optimal» and «optimal» as the case may be. Test
series is characterized with values LSR within 2-3 pursuant to the verbal interpretation means
«maximum allowable» and «allowable» as the case may be. As can be seen from the above,
the analysis in total all over the sample of persons under consideration gave an opportunity to
reach the conclusion that aviation security screeners in the control series of tests were in the
operating status. Aviation security screeners in the test series were in the fatigue status.
Results of the robust estimation of base data are presented in the Table 2. Estimation was
carried out using the program «Statistica».
Table 2 Results of the robust estimation of base data
Valid
N
Mean
Trimmed
mean,
5,000 %
Winsorized
mean,
5,000 %
Grubbs
Test
Statistic
p-
value
Std.
Dev.
SF 55 2.694 2.692 2.699 1.947 1.000 0.346
BF 55 0.274 0.249 0.268 2.822 0.190 0.257
BDA 55 322.859 322.664 320.629 2.281 1.000 92.197
PDA 55 2.137 2.139 2.142 2.613 0.393 0.509

and Fuzzy Models
In accordance with the Table 2 it is apparent that values of the mean, trimmed mean and
Winsorized mean are approximately equal. This suggests that there are no deviations in data.
Grubbs’ test for maximum values of indicators SF, BF, BDA and PDA has the significance 1;
0,19; 1; 0,3927 as the case may be which is more than the selected significance α=0,05.
Calculated Grubbs’ test does not exceed the critical point of 2,992 when α=0,05. As can be
seen from the above, maximum values are not runouts. To determine the accuracy rate for the
sample characteristic, it is necessary to find the ratio of the non-sampling error to the
arithmetic mean. Calculated values of the accuracy rate are %7467,1SFCs ,
%6827,12BFCs , %8505,3BDACs , %2123,3PDACs . Accuracy will be considered as
wholly satisfactory, if Cs does not exceed 3-5 %. Value of Cs for SF, BDA and PDA is below
specified criteria which indicate the reasonable accuracy of these characteristics. For the
indicator BF the value Cs has several times the 5 % which is obviously connected with the
small statistical sample. As can be seen from the above, the conducted analysis using the
«descriptive statistic» method showed the applicability of obtained statistic data for their
further analysis. In Tables 3 and 4 there are sample means and corrected sample variances of
experimental variables in the control group selx and and in the test group sely and as
the case may be.
Table 3 Results in the control group
Indicators SF BF BDA PDA
Sample mean, selx 2,7386 0,1964 320,2544 2,5215
Corrected sample variance, 0,1675 0,0422 4790,0913 0,2394
Table 4 Results in the test group
Indicators SF BF BDA PDA
Sample mean, sely 2,6909 0,4050 367,7850 2,1222
Corrected sample variance, 0,1040 0,0324 5692,1634 0,1637
Testing of the hypothesis for the equality of general dispersions in the first and the second
group using the F-test showed that the hypothesis is acceptable for all analyzed parameters. In
other words corrected sample variances differ insignificantly. This suggests the measurement
accuracy of oculomotor activity values.
Null hypothesis for the equality of theoretical mean values was tested by the Student’s t-
test. Observed value of the Student’s t-test ( obsT ) for parameters are:
.95,2)(;17,2)(;14,3)(;36,0)(  PDATBDATBFTSFT obsobsobsobs
Critical zone in this case is reversible. For the significance level and the number
of freedom degrees the critical point ( ..crbilatt ) is .02,2)42;05,0(.. crbilatt
For the parameter SF ..)( crbilatobs tSFT  the null hypothesis for the equality of means in
groups according to this indicator is acceptable. As can be seen from the above, the frequency
of saccades did not change for certain in the period of measurements taken. Pursuant thereto
this parameter is not suitable for diagnosing the fatigue of aviation security screeners.
For parameters BF, BDA, PDA ..),,( crbilatobs tPDABDABFT  , the null hypothesis for the
equality of means in groups according to this indicator is not acceptable. As can be seen from
the above, statistically significant changes of the blink frequency, the blink duration average
2
xs 2
ys
2
xs
2
ys
05,0
422  mnf

and the pupils’ diameter average give an opportunity to interpret indicator data as potential
fatigue markers of aviation security screeners. It is interesting to note that obtained results are
coherent with early studies within the framework of which it was found that the increase in
the number of blinks is the potential fatigue marker of aviation security screeners(Van Orden
et al. 2000) [18]. Moreover, in accordance with the PELCOS technology, the character of
blinks is analyzed by estimating the time percentage when eyelids of the person under
consideration are partially-obscured during one minute (> 80% – it means that it is the fatigue
criterion) (Dinges et al. 1998) [19]. Conclusively established changes of the pupils’ mean
diameter of aviation security screeners are coherent with results of modern studies of drivers’
fatigue problem and using pupilograph perspectives as the pupil measurement facility.
2. SYNTHESIS OF THE FUZZY MODEL
Principal component of decision-making support systems is the knowledge base. Taking into
consideration the fact that making decisions in the sphere of the aviation security support
often takes place in the context of basic data ambiguity and incompleteness, the application of
fuzzy models is justified. There are two main types of fuzzy models: Mamdani and Sugeno.
General approaches to the automatic synthesis of fuzzy models from experimental data are
presented in the paper of authors (Gladkikh et al. 2019) [20]. There is a learning sample of M
pairs of experimental data and the problem consists in the identification of fuzzy rules
connecting input data (x) with the output (y):
,,1),,( MryX rr  (1)
Where:
rX is the input data vector corresponding to r-line of the sample and - value of the
output variable.
Quality of the fuzzy model is estimated by the Root-Mean-Square Error criterion (RMSE):
,)),,((
1
.1
 
 Mr
rr XWPFy
M
RMSE (2)
Where:
M is the number of pairs of experimental data; P is the parameter vector of the
membership functions variables (x) and (y); W is the weight vector of rules from the
knowledge base; ),,( rXWPF is the calculation result of the fuzzy knowledge base.
To increase the quantity of the learning sample, experimental researches based on the
International Airport «Ulyanovsk Baratayevka» with the participation of 11 aviation security
screeners and the application of the mobile eye tracker «Eye Tracking Glasses 2.0» were
additionally carried out. As can be seen from the above, the total number of data pairs
describing the operating efficiency of aviation security screeners was 55. Indicators BF, BDA
and PDA are associated with input variables of the model (x). Output variable (y) was the
detection rate of prohibited items (DP). Original sample was divided into the learning (47
aviation security screeners) and the test (8 aviation security screeners) ones.
To synthesize the Sugeno model we selected the pack Fuzzy Logic Toolbox of the pack
MatLab. Synthesis procedure is implemented using the function genfis2. The following
subtractive clustering algorithm parameters are selected: the cluster radius is 0,7 (the value
from the range [0, 1]) is settled; the suppression coefficient is 1,25; the acceptance coefficient
is 0,5; the abruption coefficient is 0,15. As the result of the application of subtractive
clustering at the first stage, we received the knowledge base consisting of 4 rules
corresponding to four found clusters:
ry

and Fuzzy Models
IF BF=in1cluster1 AND BDA=in2cluster1 AND PDA=in3cluster1, THEN DP=out1cluster1;
IF BF=in1cluster4 AND BDA=in2cluster4 AND PDA=in3cluster4, THEN DP=out1cluster4.
Model bug on the learning sample by the criterion (2) is trnRMSE1=0,0436 and on the test
sample is chkRMSE1=0,0924. In the Figure 1а there are comparison results of the output
variable values from experimental data of the learning sample and results of fuzzy modeling
presented.
a) b)
Figure 1. Testing results of the model on the learning sample
a) after the subtractive clustering; b) after the ANFIS-learning
Figure 2. Dependence of modeling errors on
the iterations number of the ANFIS-
algorithm
Figure 3. Testing results of the Mamdani
model on the learning sample
To improve the accuracy, the model was learned using the ANFIS-algorithm. Number
learning of iterations was 200. On the basis of the graph in the Figure 1b it may be concluded
that the modeling quality was improved. Errors after the ANFIS-learning amounted
trnRMSE2=0,0348 and chkRMSE2=0,0858. In the Figure 2 the dependence of modeling errors
on the iterations number of the ANFIS-algorithms is illustrated. As the result of the ANFIS-

learning both parameters of variable membership functions and parameters in conclusions of
fuzzy model rules were specified. Example of the membership function type by fuzzy clusters
for the input variable PDA before learning using the ANFIS-algorithm and after learning is
presented in the Figure 4 (membership functions in rules are described using Gaussian
functions).
a) b)
Figure 4. Membership functions to fuzzy clusters by the variable PDA
a) before learning; b) after the ANFIS-learning
By way of illustration, in the Figure 5 the surface «inputs-output» for the obtained model
in coordinates BDA-PDA is illustrated.
a) b)
Figure 5. Surface «inputs-output» in coordinates BDA-PDA
a) before learning; b) after the ANFIS-learning
In the Table 6 comparing results of the synthetized Sugeno model with the fuzzy
Mamdani model and the linear regression. Mamdani model is synthetized using the function
genfis3. The following parameters of the fuzzy c-means algorithm are selected: the number of
clusters is 4; the exponential weight is 2; the target function improvement value during the
one iteration is 0,00001; the number of iterations is 100. As the result of experimental data,
the Mamdani model with 4 fuzzy rules was extracted. Comparison results of the output
variable value from experimental data of the learning sample and results of fuzzy modeling
using the Mamdani model are presented in the Figure 3. Quality of the identified linear

and Fuzzy Models
regression model in accordance with the corrected determination coefficient was 52,02
R .
Coefficients of this model and its characteristics are presented in the Table 5.
Table 5 Regression model
Variable Coefficient Standard error
Value of
Student’s
t-criterion
Test significance
(p-value)
Constant 1.051 0.039 26.291 3.971E-06
BF -0.109 0.032 -3.425 0.001
BDA -0.0002 9.396E-05 -2.734 0.009
PDA -0.033 0.0152 -2.156 0.064
Basic data partition quality into fuzzy clusters is implemented using the Xei-Beni (XB)
index (Gladkikh et al. 2019) [20]. Calculated value of the XB index was 0,5449. Optimal
partition into clusters corresponds to the criterion XB < 1. As the case may be, the good
partition into fuzzy clusters is received.
Table 6 Comparison results of obtained models
Model
RMSE on the
learning sample
RMSE on the test
sample
Sugeno model
(without the ANFIS-learning)
0,0436 0,0924
Sugeno model (with the ANFIS-learning) 0,0348 0,0858
Mamdani model 0,0613 0,0925
Linear regression model 1,4132 1,4193
In accordance with the Table 6 it is apparent that the fuzzy Sugeno model based on the
subtractive clustering and the ANFIS-learning does better approximate the dependence
between oculomotor activity indicators of aviation security screeners and the prohibited items
detection efficiency in comparison with other models.
3. CONCLUSION
In this paper for the first time as follows from experimental studies using the Eye-tracking
technology potential fatigue markers of aviation security screeners with the most accurate
changes were identified consistent with earlier studies pursued by other authors. Fuzzy
Sugeno model for the operating efficiency study of aviation security screeners based on the
subtractive clustering and the ANFIS-learning is developed. Model fidelity by the RMSE
criterion on the learning sample is 0,0348 and on the test sample is 0,0858 as the case may be.
Scientific and theoretical value of this paper involves developing scientific knowledge related
to the influence of the fatigue factor on the oculomotor activity of aviation security screeners
in the course of the working activity. Ambiguousness of obtained results against changing of
the saccade frequency under the influence of the fatigue factor unlike early studies which
established this indicator as the fatigue indicator of drivers and pilots raises the series of
questions requiring further studies. Provided that series of additional studies are pursued,
identified markers of the oculomotor activity can be used for the development of intellectual
systems for the fatigue control of aviation security screeners.
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OPERATING EFFICIENCY STUDY OF AVIATION SECURITY SCREENERS USING THE EYE-TRACKING TECHNOLOGY AND FUZZY MODELS

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