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

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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

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(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

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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,

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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 eye Moving 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 eye Moving 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.

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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.

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