Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents


  • Hak Gu Kim KAIST, Korea EPFL, Switzerland
  • Sangmin Lee KAIST, Korea
  • Seongyeop Kim KAIST, Korea
  • Heoun-taek Lim KAIST, Korea
  • Yong Man Ro KAIST, Korea



Affective Computing, Applications, Bio-inspired Learning


We address the black-box issue of VR sickness assessment (VRSA) by evaluating the level of physical symptoms of VR sickness. For the VR contents inducing the similar VR sickness level, the physical symptoms can vary depending on the characteristics of the contents. Most of existing VRSA methods focused on assessing the overall VR sickness score. To make better understanding of VR sickness, it is required to predict and provide the level of major symptoms of VR sickness rather than overall degree of VR sickness. In this paper, we predict the degrees of main physical symptoms affecting the overall degree of VR sickness, which are disorientation, nausea, and oculomotor. In addition, we introduce a new large-scale dataset for VRSA including 360 videos with various frame rates, physiological signals, and subjective scores. On VRSA benchmark and our newly collected dataset, our approach shows a potential to not only achieve the highest correlation with subjective scores, but also to better understand which symptoms are the main causes of VR sickness.




How to Cite

Kim, H. G., Lee, S., Kim, S., Lim, H.- taek, & Ro, Y. M. (2021). Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 836-844.



AAAI Technical Track on Cognitive Modeling and Cognitive Systems