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Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation

Abstract : Many studies have explored 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). We correlated the corresponding outputs with the simulator sickness questionnaire (SSQ) scores 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.
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Submitted on : Wednesday, March 31, 2021 - 6:39:34 PM
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Yuyang Wang, Jean-Rémy Chardonnet, Frédéric Merienne, Jivka Ovtcharova. Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation. 2021 IEEE Virtual Reality and 3D User Interfaces (VR), Mar 2021, Lisbonne, Portugal. pp.373-381, ⟨10.1109/VR50410.2021.00060⟩. ⟨hal-03187239⟩



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