Abstract
Research on drivers often uses the driving simulator (DS). Visually induced motion sickness (VIMS) has been pointed out as a problem in DS experiments. Although there are many methods to evaluate VIMS, reducing the burden on the experimental collaborators is an issue. As a method of measuring physiological indices that is less burdensome to the participants and has many applications, the use of noncontact eye-tracking system has been mentioned. This study developed a VIMS evaluation index using data collected with a noncontact eye-tracking system for DS experiments. The participants included eight elderly people with visual and balance functions that did not interfere with their daily life. The participants’ gaze data were measured at all DS trials and they answered the simulator sickness questionnaire (SSQ) before and after each trial. The participants were divided into two groups on the basis of their SSQ results. One group experienced VIMS during the DS trial (four people; average age, 79.0 years), whereas the other group did not experience it (four people; average age, 72.0 years). The results of the learning model’s validation showed a high rate of correct answers. The results suggested that the learning model obtained using machine learning was an effective evaluation index for VIMS during the DS trial.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ohmi, M., Ujike, H.: Eizou Jouhou ni yoru Jiko Teii to Eizou Yoi. Bio Med. Eng. 18(1), 32–39 (2004). (in Japanese)
Howard, I.: Human Visual Orientation. John Wiley & Sons Ltd., New Jersey (1982)
Marg, E.: Development of electro-oculography; standing potential of the eye in registration of eye movement. AMA Arch. Ophthalmol. 45, 69–185 (1951)
Golding, J.F.: Phasic skin conductance activity and motion sickness. Aviat. Space Environ. Med. 63(3), 165–171 (1992)
Wan, H., Hu, S., Wang, J.: Correlation of phasic and motion sickness-conductance responses with severity of motion sickness induced by viewing an optokinetic rotating drum. Perceptual Motor Skills 97(3), 1051–1057 (2003)
Kinoshita, F., Fujita, K., Miyanaga, K., Touyama, H., Takada, M., Takada, H.: Analysis of electrogastrograms during exercise loads. J. Sports Med. Doping Stud. 8(2), 285–294 (2018)
Shackel, B.: Eye movement recording by electro-oculography. In: Venables, P.H., Martion, I. (eds.) A Manual of Psychophysiological Methods, pp. 300–334. North-Holland Publishing Co., Amsterdam (1967)
Nakagawa, C., Ohsuga, M.: The present situation of the studies in VE-Sickness and its close field. Virt. Soc. Jpn. 3(2), 31–39 (1998). (in Japanese)
Hirayanagi, K.: A present state and perspective of studies on motion sickness. Jpn. J. Ergon. 42(3), 200–211 (2006). (in Japanese)
Fujikake, K., Miyao, M., Honda, R., Omori, M., Matuura, Y., Takada, H.: Evaluation of high quality LCDs displaying moving pictures, on the basis of the results obtained from statokinesigrams. Forma 22(2), 199–206 (2007)
Fujikake, K., Takada, H., Omori, M., Hasegawa, S., Honda, R., Miyao, M.: Evaluation of moving picture image quality on LCDs using Stabilometer. Jpn. J. Ergon. 44(4), 208–217 (2008). (in Japanese)
Kinoshita, F., Takada, H.: Numerical analysis of SDEs as a model for body sway while viewing 3D video clips. Mech. Syst. Control 47(2), 98–105 (2019)
Miyao, M., Takada, M., Takada, H.: Visual Issues on Augmented Reality Using Smart Glasses with 3D Stereoscopic Images. In: Antona, M., Stephanidis, C. (eds.) HCII 2019. LNCS, vol. 11572, pp. 578–589. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23560-4_42
Tanimura, T., Takada, H., Sugiura, A., Kinoshita, F., Takada, M.: Effects of the low-resolution 3D video clip on cerebrum blood flow dynamics. Adv Sci Tech Eng Syst J 4(2), 380–386 (2019)
U.S. Navy: OPNAVINST, 3710.7T (2004)
Young, L.R., Sheena, D.: Methods and designs; Survey of eye movement recording methods. Behav. Res. Methods Instrum. 7, 397–429 (1975)
Cornsweet, T.N., Crane, H.D.: Accurate two-dimensional eye tracker using first and fourth Purkinje images. J. Opt. Soc. Am. 63, 921–928 (1973)
Crane, H.D., Steele, C.M.: An accurate three-dimensional eye tracker. Appl. Opt. 17, 691–705 (1978)
Fujikake, K.: Measurements for visual function, including gaze, and electrooculography (EOG). In: Takada, H., Yokoyama, K. (eds.) Bio-information for Hygiene, pp. 45–56. Springer, Berlin (2021)
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 20K11905.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fujikake, K., Itadu, Y., Takada, H. (2022). Analyzing Gaze Data During Rest Time/Driving Simulator Operation Using Machine Learning. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies. HCII 2022. Lecture Notes in Computer Science, vol 13308. Springer, Cham. https://doi.org/10.1007/978-3-031-05028-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-05028-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05027-5
Online ISBN: 978-3-031-05028-2
eBook Packages: Computer ScienceComputer Science (R0)