Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation. Wang, Y., Chardonnet, J., Merienne, F., & Ovtcharova, J. In 2021 IEEE Virtual Reality and 3D User Interfaces (VR), pages 373-381, 3, 2021. IEEE.
Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation [pdf]Paper  Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation [link]Website  doi  abstract   bibtex   5 downloads  
There have been many studies about how individual differences can affect users' susceptibility to cybersickness in a VR application. However, the lack of strategy to integrate the influence of each factor on cybersickness makes it difficult to utilize the results of existing research. Based on the fuzzy logic theory that can represent the effect of different factors as a single value containing integrated information, we developed two approaches including the knowledge-based Mamdani-type fuzzy inference system and the data-driven Adaptive neuro-fuzzy inference system (ANFIS) to involve three individual differences (Age, Gaming experience and Ethnicity) and we correlated the corresponding outputs with the scores obtained from the simulator sickness questionnaire (SSQ) in a simple navigation scenario. The correlation coefficients obtained through a 4-fold cross validation were found statistically significant with both fuzzy logic approaches, indicating their effectiveness to influence the occurrence and the level of cybersickness. Our work provides insights to establish customized experiences for VR navigation by involving individual differences.

Downloads: 5