Earticle

초록

영어

Cybersickness is one of the factors that deteriorates user experience in virtual reality (VR). To understand how cybersickness is presented through human reactions and responses, we conducted a user study with 13 participants and built a ResNet-BiLSTM-based model that learns visual factors, eye movement, head movement, and physiological signals. The study results show that the model using all modalities yielded a performance of 0.88 F1-score. In particular, the model using the data that can be collected by HMD (Head Mounted Display) showed 0.87 F1-score, comparable to the model using all modalities, which indicates that cybersickness can be sufficiently well predicted through basic VR equipment (HMD). Finally, we present the importance of individual characteristics in cybersickness modeling.

목차

Abstract
I. INTRODUCTION
II. STUDY PROCEDURE
A. VR 360 video selection
B. Data collection
C. Data pre-processing
D. Model development
III. RESULTS
A. Performance of models by modality
B. Performance of models by user
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Dayoung Jeong [ dept. Artificial Intelligence Ajou University ]
• Seungwon Paik [ dept. Artificial Intelligence Ajou University ]
• Kyungsik Han [ dept. Intelligence Computing Hanyang University ]

참고문헌