Effects of Seated Postural Sway on Visually Induced Motion Sickness: A Multiple Regression and RUSBoost Classification Approach

Abstract

The purpose of this study was to propose a model for estimating individual susceptibility to motion sickness by correlating measures of seated postural sway before exposure to a roller coaster movie displayed in a head-mounted display (HMD) with subjective ratings of visually induced motion sickness (VIMS). The participants were required to watch the content for 15 min, and seated center of pressure (sCOP) was measured using a force platform for 5 min before viewing. We developed a VIMS estimating model from 16 subjects and verified it on 15 subjects. SSQ scores for VIMS were strongly correlated with the area of sCOP, the main-to-minor ratio of the sCOP ellipse, and the total length of sCOP, respectively (r = 0.706, r = 0.555, and r = 0.622). When verifying the estimation model, the observed SSQ scores differed by 32.628 ± 29.749 from the expected SSQ scores, and both score sets were positively correlated (r = 0.622). The classification results between nil and mild, moderate, and severe groups for VIMS showed an accuracy of 0.74 using random under-sampling boost (RUSBoost).

Keywords:

• Visually induced motion sickness
• head-mounted display
• postural sway
• balance board
• seated center of pressure

Authors’ contributions

Sangin Park: conceptualization, investigation, methodology, writing—original draft, visualization, software, and data curation. Sungchul Mun: conceptualization, investigation, analysis, and interpretation of results, writing—review and editing, and supervision.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data generated and analyzed in this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2022-0-00137, XR User Interaction Evaluation and Employing Its Technology).

Notes on contributors

Sangin Park

Sangin Park received his PhD degree in Emotion Engineering from Sangmyung University in 2016. He is currently a post-doctoral researcher at Hanyang University. His research area includes brain–computer interface, non-contact measurement, neuroscience, artificial intelligence, and emotion engineering.

Sungchul Mun

Sungchul Mun received his Ph.D. degree in HCI and Robotics from KIST in 2015. He was a research fellow at Seoul Institute of Technology in 2020. He is currently an assistant professor at Department of Industrial Engineering, Jeonju University. His research area includes machine learning, HCI, and data science.