International Journal of Innovative
Computing, Information and Control
Volume 18, Number 2, April 2022c
ICIC International ⃝2022
ISSN 1349-4198
pp. 433–445

RELATIONSHIP BETWEEN THE ACCURACY OF MODELSFOR JUDGING CAR SICKNESS BASED ON LINE-OF-SIGHTFEATURES AND ROAD ATTRIBUTES
Shota Okuyama1 , Jun Toyotani2 and Yuto Omae2
1

Graduate School of Industrial Technology
2
College of Industrial Technology
Nihon University
1-2-1 Izumi, Narashino, Chiba 275-8575, Japan
shota.okuyama17@gmail.com; { toyotani.jun; oomae.yuuto }@nihon-u.ac.jp

Received September 2021; revised December 2021
Abstract. In previous research, we examined whether it was possible to determine if
people would suﬀer from car sickness based only on the movement of the line of sight.
The results of our work indicate that it is possible. However, in the previous research, thisﬁnding was only veriﬁed by the line-of-sight movement while driving on a straight road,so it is necessary to verify that this ﬁnding still holds for other types of less straight roads.
Therefore, in this research, we veriﬁed the ﬁndings using three types of roads, includingstraight roads. We added line-of-sight movement while driving in cities and residential
areas. As a result, the model constructed using the line-of-sight movement while drivingon a straight road achieved the highest accuracy, with a correct answer rate of 82.9%. In
contrast, the model constructed based on the line-of-sight movement while driving in thecity was the lowest with a correct answer rate of 58.1%. Therefore, we revealed that thenature of the road has a signiﬁcant inﬂuence on judgment accuracy when determining if
people will suﬀer from car sickness based only on the movement of the line of sight.
Keywords: Motion sickness, Line-of-sight trend, Random forest, Road attributes

1. Introduction. Technology in various ﬁelds continues to advance rapidly. One sucharea is the automobile, which has long been used as a means of transportation for people.
In recent years, the development of self-driving cars has attracted considerable attention.
Self-driving cars are expected to have various roles, such as reducing stress caused by driving, realizing a safe society by reducing traﬃc accidents, and maintaining infrastructure indepopulated and aging areas. In recent years, the focus on realizing a carbon-free society
has increased [1], due to the aggravation of environmental problems [2]; hybrid vehicles(that are fueled by both gasoline and electricity) and electric vehicles (EVs), that do notuse gasoline, have received increasing attention, and demand is expected to grow in the
future [3]. Research and development on car performance are actively being carried out.
In contrast, there are challenges when conducting research on riding comfort and comfortof the vehicle interior environment, as human subjectivity is greatly involved. Motion
sickness is one such condition. Dizio and Lackner [4], Webb and Griﬃn [5], Griﬃn andNewman [6], and Manning and Stewart [7] explained that motion sickness is caused byinconsistency between organs. The sensory organs depend on the individual, and somepeople are aﬀected, even with a slight vibration. In contrast, many people experiencemotion sickness only when they are riding without driving and not even when they are
driving by themselves. In addition, it is possible that the number of people aﬀected in thisway will increase in parallel with the realization of self-driving cars. Various factors can
DOI: 10.24507/ijicic.18.02.433
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be considered for this phenomenon; however, among them, the line-of-sight movement is
considered to be one of the main factors in the same in-vehicle environment. Thus, it may
be possible to reduce motion sickness by changing the line-of-sight movement. Therefore,
the goal of this research is to develop a system that presents line-of-sight trends to eliminate motion sickness in easy-to-understand language and to suit the individual. However,motion sickness by some causes cannot be eliminated because there are many causes of
motion sickness other than line of sight. However, it is expected to be useful in that it ispossible to eliminate car sickness from only the line of sight.
Three steps were required to realize this system: “1) Build a high-precision model that
automatically judges if people will suﬀer from car sickness based on the line of sight”,“2) Develop a system that presents line-of-sight trends in easy-to-understand sentences
to eliminate motion sickness”, and “3) Build a system that incorporates these steps”. In
previous research [8], using random forest method, we construct a model that automatically judges if people will get car sick based on the movement of the line of sight. In
addition, the performance evaluation of the model reﬂected an accuracy level of approximately 80% for the test data. In previous research [8], a model was created from theline-of-sight trends acquired by movies while riding, as this approach is both simple andsafe. Moreover, a video of a straight road was used to acquire the line-of-sight movement.
The line-of-sight movement is assumed to diﬀer depending on the attributes of the roadon which the vehicle is driving. Therefore, the results of previous research are extremely
limited and the model requires veriﬁcation if it is eﬀective on all roads. In this research,
we reconstruct models using three types of roads, with diﬀerent attributes, to conduct
a basic veriﬁcation. In addition, we performed an accuracy evaluation, using test data,for performance veriﬁcation. Finally, based on the results of the accuracy evaluation, we
reveal the types of roads that our car sickness prediction model can be applied to. In
addition, we consider the limits and possibilities of automatic car sickness judgment based
only on line-of-sight trends. In this research, a model was created using the line-of-sightmovements acquired by ﬁlming whilst riding in a car, owing to high safety levels and thesimplicity of the system, as conﬁrmed in previous research [8].
Finally, we discuss the diﬀerence between existing studies and this study. Several studies
have been conducted that attempt to determine some state from the line-of-sight movement (such as determining game proﬁciency [9], estimating consumer behavior patterns[10], and deriving keys to improve marine operations [11]). However, these studies do
not analyze the relationship between motion sickness and gaze trends. In addition, there
are many previous studies on car sickness (such as the relationship between car sickness
and gender [12], relationship between brain waves and car sickness [13], and relationshipbetween vibration and car sickness [14]). The mechanism of car sickness is the focus of
the majority of these studies. However, there are insuﬃcient studies mentioning the speciﬁc methods to improve car sickness. As mentioned above, although there are numerous
studies related to the line-of-sight movement and car sickness, studies that automatically
predict whether people will experience car sickness, as well as studies on the developmentof a system that provides feedback on how the line-of-sight movement can be improved
to prevent car sickness, are insuﬃcient. For these reasons, this study may be considered
as novel.
The remainder of the paper is as follows. In Section 2, we describe the overall system,
which is the ultimate goal of this research, and the position of the paper within the system.
Section 3 provides an overview of the experiment. In Section 4, the outline, results, anddiscussion of the analysis are explained. Section 5 summarizes our study and discusses
the future scope of research.

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2. Overview of the Final Conceptual System. Figure 1 shows the ﬁnal conceptual
system of this study. This system is premised on showing users, wearing an eye-markrecorder, a video footage that was taken during a car journey. It is more suitable, for the
purpose of this system, to actually measure the line-of-sight movement while riding in acar, but the video is used owing to high safety levels and the simplicity of the system.
The eye mark recorder was EMR-9 [15], developed by Nac Image Technology, Inc., Japan.
This is an oﬀ-the-shelf system that outputs XY coordinates of where the user is looking
at and there are numerous other cases where this technology has been used in previous
research (e.g., Kawaguchi et al. [9], Hori et al. [16], Takada and Miyao [17]). The origin
is at the upper left of the display, and the X coordinate value increases as it goes to theright, and the Y coordinate value increases as it goes down. As shown in the left of Figure
2, the subject’s point of view is output as XY coordinates. In this study, the subject’s
point of view was represented by 5400 XY coordinates because the sampling frequency ofeye mark recorder was 60 Hz and the measurement time was 90 s. The XY coordinate

Figure 1. The system proposed to improve car sickness

Figure 2. Acquisition method and structure of eye tracking data

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data measured in this manner as shown in the right of Figure 2 is a time-series signal. The
vertical axis indicates amplitude of XY coordinate, and the horizontal axis indicates unit
time (1/60 s). In addition, viewing point data, estimated from the XY coordinate valuesof the right eye and the left eye, were measured. By summarizing the data obtained, inthe shape of the signal, the linguistic information for eliminating car sickness is fed backto the user.
Procedure (i): The user, wearing the eye mark recorder, is shown the video taken
while riding the car, and the eye tracking data is measured.
Procedure (ii): Transfer the measured eye-tracking data to the analysis computer.
Procedure (iii): Judge if the individual will suﬀer from car sickness based on thetransferred eye-tracking data.
Procedure (iv): If people are judged to have car sickness, calculate the features ofline-of-sight that should be changed numerically to prevent the car sickness.
Procedure (v): Convert the line-of-sight trend that should be improved to an easyto-understand sentence.
Procedure (vi): The user who receives the feedback information changes the line-ofsight based on the feedback and repeats the procedure from (i).
By repeating the above procedure, we aim to improve car sickness step-by-step. In
addition, the main targets of this system are people who suﬀer from car sickness only when
they are not driving by themselves. Therefore, this system is built in an experimental
environment, not from the driver’s point of view, but assuming that the inside of the car
is driven by another person.
This is the ﬁnal concept. To this point, we have conducted study to judge if people will
suﬀer from car sickness based on the movement of the line of sight in previous research [8].
This corresponds to “procedure (iii)”. In a previous study [8], we constructed a random
forest model that automatically judges if people will get car sick using video footageof a straight road, corresponding to road B. In addition, we suggested the possibilityof determining if people will get car sick based only on the movement of the line-ofsight. The accuracy evaluation of the constructed model indicated an achieved accuracyof approximately 80% on the test data. However, the line-of-sight trend is assumed to
diﬀer depending on the attributes of the road.
Therefore, it is necessary to verify if car sickness can be automatically judged based
only on the line-of-sight trend, even on roads other than straight roads. Therefore, in thisresearch, we reconstruct the models using video footage taken whilst driving on three typesof roads with diﬀerent attributes, including straight roads. From the judgment accuracyof these models for the test data during performance evaluation, we reveal the types ofroads that can be assessed using our model and approach. However, it cannot conclusively
reveal the types of roads that it can be applied to because there are innumerable types ofroads.
Finally, we discuss the system’s usefulness in the context of reducing car sickness. Somestudies have been conducted to improve car sickness. There have been studies that focusedon various aspects such as postural activity during the ride as the cause of car sickness[18,19], clarifying the relationship between behavior during the ride (such as watching
videos) and car sickness [20], and quantifying motion sickness based on the acceleration
data of the head during the ride [21]. These studies are critical in reducing car sickness.
However, they do not refer to the line-of-sight movement in a running vehicle, which isthe focus of this study. In addition, there are previous studies on the relationship between
sickness and line-of-sight trends [22,23]. However, while these studies focused on video
sickness such as virtual reality, there are few studies focusing on car sickness. Therefore,
we consider it a novel system. In addition, we believe that the system is practical in terms

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of the physical burden on the user because this system only requires a single element of
data, that is, eye-tracking data.
3. Experiment. The subjects consisted of 37 healthy men and women (28 men, 9 women, aged 21.86 ± 1.21 years), and a questionnaire on car sickness was conducted, as shownin Table 1. In this questionnaire, those who corresponded to (1) to (3) were those deemed
to be prone to car sickness, and those who corresponded to (4) to (6) were deﬁned as
those who were not prone. As a result, 17 people were prone to car sickness (label: 1),
and 20 people were not prone (label: 0).
Table 1. Questionnaire on car sickness
Choices
Meaning(1)
I always get car sickness.
(2)
I get car sickness frequently.
(3)
I get car sickness sometimes.
(4)
I have been car sick a few times.
(5)
I have been car sick at least once.
(6)
I have never been car sick.
The implementation period of the experiment was 40 days from September 21 to October 30, 2020. Left of Figure 2 shows the state of the experiment. In this experiment, theline-of-sight movement was measured using the video footage taken while riding in a car
due to high levels of safety and the simplicity of the system. In the experiment, subjects
were seated in a chair and wore an eye mark recorder (EMR-9, Nac Image Technology[15]) with a sampling frequency of 60 Hz. The line-of-sight movement was measured while
watching the videos projected onto a liquid crystal display (55 inch) located approximately 140 to 145 cm away from the seated position. The height of the chair was adjusted
so that the position of the viewpoint was parallel to the center of the display. To prevent a decrease in the calibration accuracy of the eye mark recorder because of the head
movement during the test, a ﬁrm high-resilience neck pillow was attached.
Subjects in the above condition were asked to watch video footage taken while riding
a car. Figure 3 shows the video frames used in the experiment. Road A, which is a

Figure 3. Experimental videos A, B, C (All 90 s in duration)

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mostly straight road in a city, and features a gentle curve with many oncoming vehicles
and pedestrians. The video of Road A features two stops due to traﬃc lights; roads inthe city have heavy traﬃc and so signals are installed at regular intervals. Road B, which
is a straight road, runs in the center lane of a wide three-lane road, so overtaking vehicles
are shown multiple times, but pedestrians are not. In addition, Road B does not feature
oncoming vehicles because there is a median strip where trees have been planted. Road B
does not feature stops due to traﬃc light that are installed at greater intervals on this typeof road. Road C, which is a residential area, has many crossroads and T-junctions. Road
C features stops at points where right and left turns are made (ﬁve stops in total). Thesemovies were taken by installing a camera (HERO9 Black, GoPro [24]) on the dashboardof cars and ﬁlming the landscape of the actual driving scenery. From the landscape of theactual driving scenery, three patterns of movies (each 90 s in duration) were created with
diﬀerent scenes. The subjects were asked to watch a video twice in the order of roads A,
B, C. As the number of subjects was 37, the number of available data was 74.
The content of the questionnaire, conducted before the experiment, does not include aquestion on if an individual felt sick by watching the footage of roads A, B, and C, but
only asks car sickness in general. Therefore, caution must be taken, for the purpose ofthis research, to not analyze video sickness (i.e., if you get sick by watching video).
4. Automatic Judgment of Car Sickness Based on Line-of-Sight Information.
4.1. Features and judgment model used for analysis. The time-series signal consists of a large amount of numerical information. Therefore, it is desirable to convert it
into a value that expresses the characteristics of the signal before analysis. Such values
are called features, and statistics such as mean, variance, skewness, and kurtosis are often
used as features of time series signals (e.g., Omae et al. [25]). Therefore, these features
are adopted in this study. In addition, we considered that the line-of-sight movement
distance was important in the case of line-of-sight movement data, so we also adopted theaverage line-of-sight movement distance, which is the line-of-sight movement distance perunit time (1/60 s). Table 2 lists all the features that were ﬁnally adopted.
Table 2. Adopted features
Feature
f1
f2
f3
f4
f5
f6
f7
f8
f9

LabelsMeaning
Mean(X)
Average value of X signal
Mean(Y)
Average value of Y signal
Var(X)
Dispersion of X signal
Var(Y)
Dispersion of Y signal
Skew(X)
Skewness of X signal
Skew(Y)
Skewness of Y signal
Kurt(X)
Kurtosis of X signal
Kurt(Y)
Kurtosis of Y signal
Dist
Average viewpoint movement distance

Subsequently, we discuss analytical methods of this study. The purpose of this study is
to verify whether it is possible to automatically determine if people would suﬀer from car
sickness based solely on the movement of the line of sight, even if the road is not straight.
Therefore, in this study, we used the same random forest model as in the previous study[8].

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4.2. Data preprocessing. The error values that occurred when blinking or when looking
outside the display during the experiment were removed by substituting them with thetime-series signal average value of the subject. Subsequently, min-max normalization
was carried out for scale adjustment of each feature quantity, with the minimum andmaximum values of each feature adjusted to 0 and 1. In this experiment, data were
measured twice for one subject, and the ﬁrst and second data were used as teaching andtest data, respectively, because the number of subjects was small. However, when a new
third-party subject is tested using the random forest model constructed in this study, it ispossible that the generalization ability may decrease. This is because both data teaching
and testing are collected from the same subjects. Normally, test data should be collectedfrom an unknown third person, so we plan to acquire new third-person data and verify it
again as part of future work.
4.3. Chance level measurement. In this research, the chance level is measured byrandomly determining if an individual is car sick from the data of 37 cases corresponding
to the test data. However, we calculated the correct answer rate of 100 patterns using
the random number seed ranging from 0 to 99 and determined the chance level as theinterval given by the average value and standard deviation. By the value of the chancelevel depending on the random number, the result is not stable.
As a result of implementing this procedure, the average percentage of correct answers
at the chance level was 50.11%, and the standard deviation was 8.89%. Therefore, if thecorrect answer rate of the model construction in this research exceeds the average correct
answer rate of 50.11% of the chance level, it can be judged that the model has sometendency and possibly indicates some new ﬁndings.
4.4. Random forest condition setting. This section describes the setting of various
variables in a random forest. First, we set the minimum number of leaf node samples to
one to induce overtraining in individual decision trees. Next, we considered the numberof features, selected from all the features that are used when determining the branchat each decision tree. In general, the square root value of the number of all features isrecommended [26]. The total number of features in this study was nine, as shown in Table
2. Therefore, we selected three evenly spaced values including square roots such as 3, 6,
and 9. Finally, in this study, the number of decision trees included in one random forest
was set in a wide range of intervals, such as 10, 100, 500, and 1000, from the viewpointof both judgment accuracy and judgment time. In this study, we created 100 models bychanging the seeds of random numbers, as we did in previous studies [8].
In addition, we performed resampling using the bootstrap method when constructing
individual decision trees. Therefore, the same subject may be duplicated in the resampleddataset.
4.5. Results and discussion. We constructed a random forest based on the conditions
described in Section 4.4. Table 3 shows the percentage of correct answers when the test
data for performance veriﬁcation were input to the constructed model. These values are
the average values of the correct answer rate, standard deviation, maximum value, andminimum value, and are derived from the judgment results of all 100 random forests.
First, we describe the results for road A. The highest average accuracy was 58.1%,
and it was measured by adopting the random forest using the following parameters (thenumber of randomly selected features: 9, the number of decision trees: 1000). This result
was not considered to be highly accurate because it was not much diﬀerent from theaverage correct answer rate of the chance level (50.11%). Therefore, it is diﬃcult to judge
if people who will suﬀer from car sickness is correctly determined by the model based on

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Table 3. Correct answer rate of judgment model
Number of
Number ofMax
Min
Video features
Average Standard
decisiontype randomly
accuracy deviation accuracy accuracy
trees
selected
A
3
10
55.9%
5.2%
67.6%
43.2%
A
3
100
56.6%
2.4%
62.2%
51.4%
A
3
500
56.4%
1.4%
59.5%
54.1%
A
3
1000
56.4%
1.1%
59.5%
54.1%
A
10
6
56.0%
5.3%
70.3%
43.2%
A
6
100
56.8%
2.3%
62.2%
51.4%
A
6
500
57.3%
1.4%
59.5%
54.1%
A
6
1000
57.3%
1.2%
59.5%
54.1%
A
9
10
55.3%
5.2%
67.6%
45.9%
A
9
100
55.9%
2.6%
59.5%
48.6%
A
9
500
57.5%
1.8%
59.5%
54.1%
A
9
1000
58.1%
1.7%
59.5%
54.1%
B
3
10
65.4%
6.4%
78.4%
48.6%
B
3
100
75.7%
4.6%
91.9%
62.2%
B
3
500
79.9%
4.0%
89.2%
70.3%
B
3
1000
80.5%
3.2%
89.2%
73.0%
B
6
10
67.0%
6.1%
83.8%
51.4%
B
6
100
77.4%
4.2%
89.2%
67.6%
B
6
500
81.5%
3.6%
89.2%
73.0%
B
6
1000
82.9%
2.7%
89.2%
75.7%
B
9
10
67.1%
5.1%
78.4%
48.6%
B
9
100
78.4%
4.0%
89.2%
67.6%
B
9
500
80.7%
2.9%
89.2%
73.0%
B
9
1000
81.6%
1.8%
83.8%
75.7%
C
3
10
64.7%
5.4%
75.7%
48.6%
C
3
100
66.8%
3.5%
75.7%
56.8%
C
3
500
66.8%
2.9%
75.7%
59.5%
C
3
1000
66.5%
2.4%
73.0%
62.2%
C
6
10
65.4%
5.9%
86.5%
48.6%
C
6
100
65.2%
3.7%
75.7%
56.8%
C
6
500
64.3%
2.8%
70.3%
59.5%
C
6
1000
63.0%
2.6%
70.3%
56.8%
C
9
10
65.5%
5.6%
78.4%
51.4%
C
9
100
65.0%
3.8%
78.4%
56.8%
C
9
500
63.5%
2.5%
70.3%
56.8%
C
9
1000
63.3%
2.4%
70.3%
56.8%
the movement of the line-of-sight while driving on Road A assuming in the city. Second,
we explain the results for Road B. In the case of this road, high accuracy levels wereachieved. The highest average accuracy was 82.9%, and it was measured by the random
forest using the following parameters (the number of randomly selected features: 6, thenumber of decision trees: 1000). This result indicates that many models of the random
forests that adopted the described conditions can recognize car sickness labels with anaccuracy of approximately 80%. According to this result, it is possible to judge if people

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will suﬀer from car sickness based on the movement of the line-of-sight while driving on
Road B, (i.e., a straight road). Finally, we describe the results for Road C. The highest
average accuracy is 66.8%, which is 16.69% higher than the chance level of 50.11%, whichis measured by the random forest with the following parameters (the number of randomly
selected features: 3 and the number of decision trees to compose: 100 or 500). From
this result, although there is information for identifying car sickness in the line-of-sightmovement while driving on Road C (assuming a residential area), it may be diﬃcult tojudge with high accuracy if people will get car sick based on the movement of the line ofsight.
In this research, the accuracies of Roads A and C were not high. However, we consider
that making strong conclusions is diﬃcult because this result indicates the tendency of
the accuracies of the models proposed by this research, which is just a case study. The
judgment accuracy may improve if we adopt new line-of-sight features that are not adopted
in this research.
To obtain the overall judgment accuracy excluding the eﬀect of the variable settings ofthe random forest, we calculated the average of “Average accuracy” in Table 3 by each
video A, B and C shown in Figure 3. The results are shown in Figure 4. According
to the bar chart in Figure 4, Road B, which is assumed to be a straight road, has thehighest accuracy when compared to the other road attributes. Road B is followed by
Road C, assuming a residential area, and Road A, assuming in the city. In the mentioned
order, the accuracy of the judgment decreases by about 10% in each value. This result
indicates that judgment accuracy varies depending on the road attributes. Therefore, itis necessary to verify why the accuracy of the judgment changes depending on the roadattribute. Hence, we list the characteristics of the road attributes assumed in this study
and consider the cause of the eﬀect on the model’s decision accuracy.

Figure 4. Average accuracies of judgment models
First, we focus on the objects existing in the three types of roads used in this study. The
objects existing on Road B, which is a straight road and achieves the highest judgment
accuracy among all of the road types adopted in this research, are only overtaking vehicles.
Next, the objects existing on Road C, which is road in a residential area and has the second
highest judgment accuracy, are buildings (e.g., houses) and pedestrians. In addition to
overtaking vehicles, buildings, and pedestrians, oncoming vehicles also exist on Road A

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(which is road in a city based and has the lowest judgment accuracy). Therefore, whencomparing the judgment accuracies of the models for predicting car sickness on Roads A,
B, and C from the viewpoint of objects that exist on these roads, it is considered that theroad type with the fewest objects may lead to a diﬀerence in the line-of-sight movement
between people who would get car sickness or do not.
Next, we considered the relationship between the number of stops and judgment accuracy for road attributes. Road B does not feature any stops and is wide compared to otherroad attributes, and the signal installation interval is wide. In contrast, on Road A, there
are many pedestrians and pedestrian signals that exist at frequent intervals. Therefore,
waiting for a signal is observed multiple times. Road C also features stops when making
a right or left turn. Focusing on the stop, the model of the straight road, that does
not feature stops, has higher judgment accuracy than the other two road types, so it isthought that the line-of-sight movement when driving has an impact on car sickness morethan the line-of-sight movement when the vehicle is stopped. However, this result may
be observed only in the scenes used in this experiment. Therefore, it will be necessary toreexamine other roads that were not used in this research in the future.
Finally, we discuss the usefulness of results obtained in this study. Most of the existing studies that have been conducted to improve car sickness [19,20,21,27] focus on the
behavior of the subjects during the ride. Therefore, the road attributes used in the experiments are frequently of a single pattern. However, human behavior during the ride isassumed to diﬀer depending on the outside environment of the vehicle. Therefore, in thisstudy, we focused on the line-of-sight movement during the ride and investigated about
the road attributes that lead to a diﬀerence between the gaze movement of those whowill suﬀer from car sickness and those who will not. As a result, new ﬁndings on therelationship between car sickness and the environment outside the vehicle were obtained.
In this regard, the study is considered to be novel and useful.
As mentioned above, we revealed that road attributes have a signiﬁcant inﬂuence on the
judgment accuracy in judging if people will get car sick based only on the movement of theline of sight. However, this research is focused on simulated experiments because the lineof sight movement was measured by using the video while riding in a car. Although it isappropriate from the viewpoint of safety, it is insuﬃcient from the viewpoint of acquiringdata. Therefore, it is unclear if the results obtained conform, even when actually getting
the subject in a car and acquiring the line of sight movement. From this point of view, thisresearch has a strong meaning in basic research. In the future, we would like to consider
obtaining line-of-sight data by actually getting the subject in the car, while taking thesafety of the participants into account. In addition, the number of subjects and genderratio in this study are insuﬃcient; therefore, the results in this study show only one trend.
In future studies, we would like to verify again about the results of this study using moredata without any bias in the gender ratio.
5. Conclusions. In this research, we revealed the types of road attributes that can beused to judge if people would suﬀer from car sickness based only on the movement of
the line-of-sight. We measured the line-of-sight movements using three types of movies
with diﬀerent road attributes while riding in a car, due to safety and simplicity. Based on
previous research [8], we constructed an automatic car sickness judgment model for eachroad type from the measured line-of-sight movement and evaluated the accuracy of eachmodel. As a result, we observed a diﬀerence in the correct answer rate of approximately
25% between the straight road model, with the highest accuracy of 82.9%, and the modelfor the road in the city, with the lowest accuracy of 58.1%. From this result, it was revealed
that the road attribute has a big inﬂuence on the judgment accuracy in judging if people

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will get car sick based only on the movement of the line of sight. In addition, only one
type of road obtained a correct answer rate of over 80% in this study. Thus, the automatic
judgment of car sickness based only on the line of sight may be eﬀective only in limited
situations. However, the features used in this research are very biased because they all
are time domain features. Therefore, it is necessary to consider if an accuracy higher thanthat achieved in this research can be obtained by adopting line-of-sight features other than
the time domain. Speciﬁcally, we would like to consider the adoption of new features suchas time to follow the gaze object (building, oncoming vehicle, etc.) with the eyes, andfeatures such as power band, energy, and frequency domain entropy, which are featuresother than the time domain. In addition, we would like to examine if the accuracy can be
improved by reconstructing the model using the deep learning method, which was used inexisting studies that proposed a new analysis method based on image processing [28] andrealized the state judgment from the time-series signals [29]. In future, we aim to build asystem that provides feedback on how to improve the line-of-sight movement for people
who will get car sickness, complete the entire system shown in Section 2, and verify its
eﬀectiveness.
Acknowledgment. This work was supported in part by JSPS Grant-in-Aid for Young
Scientists (Grant No. 19K20062, Y. Omae).
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Author Biography
Shota Okuyama graduated from Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University, Japan in 2020. He
entered graduate school at the same university in 2020, currently in second year of
master’s program. He is interested in machine learning and artiﬁcial intelligence.

Jun Toyotani is a Professor at the Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University. He received Ph.D. fromthe Graduate School of Industrial Engineering, Nihon University. His areas of specialization are data science, digital marketing analysis, and research on streamlining
operations using AI. He is a member of the Japan Society for Engineering Education,
Information Processing Society of Japan, and Japan Society of Directories, among
others.

Yuto Omae is currently an Assistant Professor of Department of Industrial Engineering and Management, College of Industrial Technology, Nihon University, Japan
from April 2019. He received Doctor degree in Engineering from the Department
of Information Science and Control Engineering, Nagaoka University of Technology,
Japan in 2016. His research interests are artiﬁcial intelligence, machine learning,mathematical models, infectious disease simulation and so on.