This document analyzes eye movement patterns during motorcycle riding on mountains using an eye tracking system. The system recorded eye movements and front views of subjects riding on a mountain route. Analysis found that human factors like traffic, vehicles, and roadside objects influenced fixation points and eye tracking. Specifically, subjects' eyes tracked opposite lane vehicles more when climbing than descending, and fixated mainly at traffic signals, stopped vehicles, close objects, roadside buildings, and vehicles in front. The study aims to understand how human factors impact eye movements during motorcycle riding.

1. Human Factor Affects Eye Movement Pattern During
Riding Motorcycle on the Mountain
Haiwei Dong
Japan Society of the Promotion of Science
& Kobe University
1-8 Chiyoda-ku, Tokyo, JAPAN
haiwei@gold.kobe-u.ac.jp
Zhiwei Luo
Kobe University
1-1 Rokkodai-cho, Nada-ku, Kobe, JAPAN
luo@gold.kobe-u.ac.jp
ABSTRACT
Human’s eyes are of great importance in the process of
perception, cognition, movement, etc. as about 80% of
information about the surrounding world comes from
vision. Through analyzing the pattern of eye movement, we
can make it clear how human accomplish everyday life with
eyes. As human lives in the communities which are
artificial environments, various man-made signs, objects
and surrounding people have influence on human,
particularly on eye movement pattern. To fully understand
the eye movement pattern, we have to consider the human
factors. This paper focuses on clarifying eye movement
pattern during riding motorcycle on the mountain. We use a
mobile eye mark tracking system to record the eye motion
and the front view. By referring the recorded movie, eye
mark analysis and fixation point analysis verify the
influence from human factor. In addition, we provide
suggestions to promote safe riding.
Author Keywords
Human factor, moving platform, eye movement pattern.
ACM Classification Keywords
H5.m. Information interfaces and presentation (e.g., HCI):
Miscellaneous.
General Terms
Experimentation, Human Factors, Measurement
INTRODUCTION
Human is a large complex system. It obtains various kinds
of information from the surrounding environment by
sensors (e.g. eyes, ears, nose), processes the information by
nervous system (e.g. brain) and finally reacts the
environment by actuators (e.g. muscles). During the whole
process, eyes play particularly important role as about 80%
of information about the surrounding environment comes
from vision [1]. By eyes, human observes the world and
inversely from eyes, it is possible to obtain the
psychophysical state of human. Though analyzing the
pattern of eye movement, we can learn much on how
human accomplishes daily life activities with the aid of
eyes. Such analysis can help us to improve the medicine
science and also can give us many beneficial advices on
ergonomics.
The eye movement analysis has a long history. Although
the analysis of eye movement relies on much of the image
processing technologies, the pioneer research on eye
movement recording was done by Delabarre in 1898 [2].
After that, many researches analyzed the gaze direction in
the activity of looking at pictures [3], copy typing [4],
musical sight reading [5], etc. In these researches, the
subject’s head was required to keep still. The first device
for recording eye movement for unconstrained activity was
developed by Machworth and Thomas in 1962 [6]. Since
then, eye movement analysis gained great progress [7]. In
the last two decades, the researches on fixation strategies
and their relation with action have grown from sedentary
activities (including reading [8], musical sight reading [9],
typing [10], looking at pictures [11], drawing and sketching
[12]) to locomotion [13, 14], driving (including steering
[15], urban driving [16], racing driving [17]), ball sports
(including table tennis [18], cricket [19], base ball [20]),
etc. However, these researches just consider the eye
movement pattern in human’s particular task. As human
lives in the communities which are artificial environments,
various man-made signs, objects and surrounding people
have influence on human, particularly on eye movement
pattern. To fully understand the eye movement pattern, we
have to consider the above human factors.
This paper addresses the eye movement pattern during
riding motorcycle on the mountain. Here we take riding
bicycle as an example to research. The specific reasons are
demonstrated as the following three reasons. First, riding
motorcycle is specific case which has not been fully
considered in the previous eye movement researches.
Compared with the driving case, riding requires more eye
movements. Also, there are many unique actions, like
checking roadbed, are required to be done by rider. Second,
we consider the case of riding up and down the mountain.
The objective is to investigate whether there is difference
on eye movement between the two processes. Third, the
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. To copy otherwise,
or republish, to post on servers or to redistribute to lists, requires prior
specific permission and/or a fee.
PETMEI’11, September 18, 2011, Beijing, China.
Copyright 2011 ACM 978-1-4503-0930-1/11/09...$10.00.
27

2. traffic is left-driving in Japan. To verify whether this factor
affects riding is also of significance.
Specifically, we use a mobile eye mark tracking system to
record the binocular eye movement and the front view. By
referring the recorded movie, eye mark analysis and
fixation point analysis are done to clarify the relation of
human factor and eye movement pattern. The results show
that many artificial objects, such as license plate of the
vehicle, building beside the traffic light, tailstock of the
vehicle, etc., do affect eye movement pattern in ways.
Compared with previous researches, this paper analyzes the
eye movement pattern from a new viewpoint of human
factor. The experiment of riding motorcycle on the
mountain has also many unique features.
METHODS
We take three normal healthy persons (male) as subject to
do the experiment. All the subjects gave written informed
consent. The subjects are 27±2 years old male with 170±5
cm in height and 58.4±7 kg in weight. The vision
examination results are 1.3+ for the left eye and 1.2+ for the
right eye. The weather when doing the experiment was
cloudy. The temperature is from 8ºC to 15ºC, respectively.
The eye mark recorder is composed of a head unit and a
mobile controller. The head unit is a spectacle with three
cameras equipped: one is arranged forward to record the
environment view; the other two is arranged towards both
eyes to recognize eye movement. Thus, we can measure the
binocular information. The controller receives the
information from the cameras and computes the gaze
direction in real time. All the data is recorded to a compact
SD card.
The experiment includes three phases. First phase is
preparation. We instrument the subject with eye mark
recorder and calibrate the cameras. Second phase is
recording data. The subject rides a motorcycle to a
designated position on the mountain (Rokko Mountain,
Kobe, Japan) and go back to the starting point by a different
route, during which the eye mark recorder records all the
data relating with eye movement and surrounding
environment (Figure 1). The whole driving route is a circle,
including climbing up and climbing down the mountain.
The total driving time is 30 min and driving distance is
about 15 km. Third phase is analysis. By analyzing the data
recorded, we extract the eye movement pattern and obtain
the psychological state of the subject offline.
In the preparation phase, we set the average distance
between pupils as 63 mm after measuring the subjects. The
sampling rate of the system is 60Hz. To get a wide view,
the view lens of the front camera is set as 92 degree. We
adjust the binary image level of pupil and purukinje to
make sure them to be clear enough for recognition. We use
nine squares as marks to calibrate the gaze direction. In
detail, we point out the marks on a display which is 2000
mm away from the subject. When the subject sees the
pointed mark, he (or she) triggers a button and then the
calibration target moves to the next mark (Figure 2).
(a)
(b)
Figure 1. Experiment setting. (a) The subject
instrumented by eye mark recorder. (b) Vision from left
camera with eye mark tracking points.
Immediately after the calibration, the eye mark appears on
the view image. After that, we record the view image with
eye mark in MPEG format. It is noted that for the
synchronization problem, there is a time rag up to 0.1 sec
between the eye marks and the view image. Because the
scale of the video is 640×480, a SD card with 2 GB
memory can stand for about 30 min recording.
In the analysis phase, we read the visual movie recorded
and extracted the eye mark data from the visual image. As
the eye mark is presented as a particular image, it is not
complex to recognize it by image processing. In practice,
the visual field camera is not at the eyeball rotation
position. If viewing the subject closer or father from the
calibration distance, the eye marks will be displayed shifted
28

3. (a)
(b)
Figure 2. Calibration of gaze direction. (a) Left eye. (b)
Right eye.
from the actual view. Hence, we process parallax correction
where the visual field camera position is set as 43 mm
forfront distance and 24 mm for upside distance. Before
further analyzing, we use pulse cutting method to smooth
the original data. The parameter setting of pulse shape is
shown in Table 1.
Index
Start point to peak
point
Start point to end
point
XY
difference
9.2 deg 4.6 deg
P difference 1 mm 0.5 mm
Table 1. Parameter definition in pulse cutting method.
The analysis is processed by a software package MATLAB
(Mathworks Co. Ltd.). In this paper, we focus on the
relevance between human factor and eye movement pattern
when riding motorcycle. For the research of eye movement,
the usual analytical items include eye marks, fixation
points, angle of convergence, pupil diameter and blinking.
Different from the indoor experiment, we can not assume
an upper bound distance of the object eye gaze. In other
words, in the outdoor environment, the subject can gaze at
things from 100 mm to 10 km. Thus, the measurement error
of convergence angle is very large for outdoor
circumstances. Hence, we do not consider this index. In
addition, blinking in our experiment is considered as an
error because in that time, we can not obtain any
information of eyes. Thus, we do not use this index. To
conclude, in this paper, we focus on the variation of eye
mark and fixation point.
RESULTS
In the experiment, the system records the data in 60 Hz, i.e.
we obtain a group of data in every 0.017s. As the total
experiment lasts for 20 min, we obtain 65526 groups of
data. As the time of riding up the mountain and riding down
the mountain is approximately the same, we compare the
two situations in the following analysis. Here, we give eye
mark analysis and fixation point analysis to clarify the
influence of human factors on human’s eye movement
pattern.
Eye Mark Analysis
We plot a point cloud figure to illustrate the eye mark
movement of the left eye during the whole ride phase
(Figure 3). The measurement boundary is set as -45 deg ≤ X
≤ 45 deg, -35 deg ≤ Y ≤ 35 deg. When the eye mark
position extends the above boundary, we set it as a position
on the boundary. To be convenient for illustration, we
divided the figure into two parts from up to down and three
parts from left to right. Hence, the original figure is
portioned into six parts which are marked as ①, ②, ③, ④,
⑤, ⑥, respectively. Referring to the recorded video, the
left boundary line and right boundary line indicate the eye
gaze when checking the traffic situation at a traffic corner.
This traffic confirmation needs the subject to look at both
sides of the traffic. The eyes move quickly between left and
right extremities, which leads the eye movement to be
beyond the boundary [-45, 45] deg. Also this traffic check
action needs head to rotate for coordination. In the region
underneath (④, ⑤, ⑥), the point cloud indicates the case
when the subject check the road in front of it. Sometimes,
the subject looks some particular places (e.g. speedometer)
so close to itself that is beyond the boundary setting. Thus,
there is a boundary line at the bottom.
For the subjects in our research whose left eye is dominant
eye, the right eye cooperates the movement of the left eye,
i.e. when the left eye moves towards left, the right eye also
moves towards left. Thus, in the following analysis, we
focus on the movement of left eye. In Figure 3, from the
region ① and ②, we can see that for the most time, the eye
gaze in the horizontal direction. At this time, there is no car
coming from the opposite direction or a traffic corner which
needs to check. For most of the time, the subject is in this
riding status. There is a line-shape point cloud above the
horizontal direction in the region ②, which shows the case
29

4. when the subject gazes at the vehicles and pedestrians in
front of it. Besides, there is also a line-shape point cloud in
the region ③ which indicates the situation when the subject
is gazing at the vehicles moving from far to near on the
opposite lane. Comparing the first-half with the last half
phase, we find that the subject pays more attention on the
vehicles of the opposite lane when riding up the mountain
comparing with the case when riding down the mountain.
(a)
(b)
Figure 3. Eye mark tracking point cloud (left eye). (a)
First-half phase. (b) Last-half phase.
Fixation Point Analysis
From the previous literatures, we know that the pattern of
eye movement is referred as a ‘saccade and fixate’ strategy
[21]. Saccade is the first movement which directs the eye to
a new position. Fixation is the phase between saccade when
gaze is held almost stationary. Previous researches also
show that during fixation that information is taken in while
during saccades human are effectively blind [22]. From
analysis of fixation, we can make it clear what the human
really pays attention to during riding. Figure 4 shows
fixation frequency distribution. Compared with eye mark
tracking distribution (Figure 3), we find that there is not
direct correlation between the two. In other words, what the
eyes always glance at does not have direct relation with the
thing that eyes fixate on.
By referring to the video, we find the fixation occurs
mainly during the following six occasions:
a) waiting for traffic signals;
b) stopping because the vehicles in front stop;
c) observing things (e.g. roadbed) which are close to
the subject;
d) looking at the things (e.g. buildings) on the road
side;
e) noticing the vehicles from the opposite lane;
f) confirming the destination direction.
(a)
(b)
Figure 4. Fixation frequency distribution of the left eye.
(a) Isometric view. (b) Underside projection.
To verify the above summary, Table 2 gives the details
about the fixation longer than 1 sec (after rounding). In the
experiment, two pairs of fixation, (L207, R113) and (L322,
30

5. R236), approximately happen in the same time. The others
occur under different occasion in different time.
Index Fix.
No.
Start
(HMS)
Duration
(sec)
Fixation
place
Occasion
L207 00:02:31.403 1.201 building
beside the
traffic light
a)
L322 00:03:25.641 2.753 license
plate of the
vehicle in
front of it
b)
L905 00:07:55.363 0.968 trees beside
the road
d)
L1312 00:11:13.296 0.984 traffic line
in front of
it
d)
Left
eye
L1395 00:11.49.616 1.018 tailstock of
the vehicle
in front of
it
b)
Table 2. Classification of long fixations by occasions.
We process two statistics, i.e. moving velocity and direction
statistics, to analyze the dynamic process when changing
the fixation points (Table 3 and Table 4).
Left eyeMoving
velocity (deg/s)
Absolute frequency Relative frequency (%)
0.00 ~ 518 40.9
30.00 ~ 283 16.8
60.00 ~ 220 9.2
90.00 ~ 174 8.4
120.00 ~ 148 5.2
150.00 ~ 146 5.1
180.00 ~ 135 4.4
210.00 ~ 157 4.4
240.00 ~ 118 3.6
270.00 ~ 83 2.0
For detailed analysis, slow motion is the ordinary state
when changing fixation point, i.e. 26.1% for the left eye.
More specifically speaking, faster eye movement happens
infrequently than slower eye movement (Table 3). Besides,
considering the moving direction of eye, there are two
concentration intervals as 0 deg ≤ θ ≤ 45.0 deg and 180.0
deg ≤ θ ≤ 225.0 deg. The first interval indicates the case
when the eye keeps relatively still. Referring to the video,
the second interval shows the situation when the subject
checks the traffic condition (e.g. at the traffic corner),
which needs the eyes to move between left side to right side
very quickly. For the other intervals, eyes just move in the
region nearby (Table 4).
Left eyeMoving
direction
(deg) Absolute frequency Relative frequency (%)
0.0 ~ 688 30.1
45.0 ~ 188 8.2
90.0 ~ 118 5.2
135.0 ~ 132 5.8
180.0 ~ 695 30.5
225.0 ~ 202 8.9
270.0 ~ 113 5.0
315.0 ~ 146 6.4
Table 4. Moving direction frequency of the left eye when
transferring fixation points.
DISCUSSION
From the eye mark analysis and fixation point analysis, we
conclude that the eye movement pattern has a close relation
with human factors. From the old times to right now,
human changes the surrounding environment and
meanwhile the artificial environment, including the visible
structures and invisible rules, also influences the human
self. In this research, we gave the evidence how the human
factors affect the eye mark tracking trajectory and fixation
point movement. Furthermore, such kind of eye movement
pattern also provides many issues that we can discuss. For
example, for the most people, the left eye is the dominant
eye. In that case, when driving vehicle (or riding
motorcycle) in left-driving road, there comes out much
work to do for the left eye. In order to achieve safe riding,
we can choose the position of the traffic signs; equip
intelligent sensors to detect the roadbed position, etc. As the
suggestions to the drivers (or riders), we advise them to
look at the objects on the right side for relax when waiting
for traffic lights. By effectively reducing the movement of
eyes, the safety of drivers (or riders) should be promoted.
Besides, we feel that illumination intensity influences much
on the recognition of eyes. With strong illumination, like
the 12 am, there are many errors when following the eye
movement. In reverse, under the weak illumination, like 6
pm, the equipment can not recognize the pupil well. For this
Table 3. Moving velocity frequency of the left eye when
transferring fixation points.
31

6. problem, if we choose advanced cameras, the performance
of the eye mark tracking device is improved. However,
advanced cameras are always large and heavy, which
causes them not easy to carry. In our opinion, one good
solution is to seek more effective image-processing
technique.
CONCLUSION
This paper considers the eye movement pattern in the
situation of riding bicycle on the mountain. The results
show that the human factors do influence the eye movement
in eye mark trajectory and fixation pattern.
ACKNOWLEDGMENTS
This work was supported in part by the Japan Society for
Promotion of Science. The authors would like to thank Lu
Lv for great assistance in experiment.
REFERENCES
1. Whiteside, R., Face language. 1996: St. Petersburg.
2. Delabarre, E.B., A method for recording eye-movement.
American Journal of Psychology, 1898. 9: p. 572-574.
3. Buswell, G.T., How people look at pictures: a study of
psychology of perception in art. 1935: Chicago
University Press.
4. Butsch, R.L.C., Eye movements and the eye-hand span
in typewriting. Journal of Educational Psychology,
1932. 23: p. 104-121.
5. Weaver, H.E., A study of visual processes in reading
differently constructed musical selections.
Psychological Monographs, 1943. 55: p. 1-30.
6. Mackworth, N.H. and E.L. Thomas, Head-mounted eye-
marker camera. Journal of Optical Society of America,
1962. 52: p. 713-716.
7. Michael, F.L., Eye movements and the control of actions
in everyday life. Progress in Retinal and Eye Research,
2006. 25: p. 296-324.
8. Radach, R., A. Kennedy, and K. Rayner, Eye
movements and information processing during reading.
2004: Psychology Press.
9. Furneaux, S. and M.F. Land. The effects of skill on the
eye-hand span during musical sight-reading. in
Proceedings of the Royal Society of London, Series B:
Biological Sciences. 1999.
10. Inhoff, A.W. and J. Wang, Encoding of text, manual
movement planning, and eye-hand coordination during
copy-typing. Journal of Experimental Psychology:
Human Perception and Performance, 1992. 18: p. 437-
448.
11. Tatler, B.W., R.J. Baddeley, and I.D. Gilchrist, Visual
correlates of fixation selection: effects of scale and time.
Vision Research, 2005. 45: p. 643-659.
12. Tchalenko, J., et al., Eye movement and voluntary
control in portrait drawing, in The Mind's Eye:
Cognitive and Applied Eye Movement Research. 2003,
Elsevier Science. p. 705-727.
13. Patla, A.E. and J.N. Vickers, How far ahead do we look
when required to step on specific locations in the travel
path during locomotion. Experimental Brain Research,
2003. 148: p. 133-138.
14. Hollands, M.A., A.E. Patla, and J.N. Vickers, Look
where you're going: gaze behavior associated with
maintaining and changing the direction of locomotion.
Experimental Brain Research, 2002. 143: p. 221-231.
15. Land, M.F., The visual control of steering, in Vision and
Action. 1998, Cambridge University Press. p. 163-180.
16. Land, M.F., The coordination of rotations of eyes, head
and trunk in saccadic turns produced in natural
situation. Experimental Brain Research, 2004. 159: p.
151-160.
17. Land, M.F. and B.W. Tatler, Steering with the head: the
visual strategy of a racing driver. Current Biology,
2001. 11: p. 1215-1220.
18. Ripoll, H., P. Fleurance, and D. Caseneuve, Analysis of
visual patterns of table tennis players, in Eye
Movement: From Physiology to Cognition. 1987,
Elsevier. p. 616-617.
19. Land, M.F. and P. Mcleod, From eye movements to
action: how batsmen hit the ball. Nature Neuroscience,
2000. 3: p. 1340-1345.
20. Mcleod, P., Visual reaction time and high-speed ball
games. Perception, 1987. 16: p. 49-59.
21. Land, M.F., Motion and vision: why animals move their
eyes. Journal of Comparative Physiology A, 1999. 185:
p. 341-352.
22. Dodge, R., Visual perception during eye movement.
Psychological Review, 1900. 7: p. 454-465.
32