Personalized Bayesian Networks for Cybersickness Prediction in Virtual Reality

Personal characteristics fundamentally shape virtual reality (VR) experiences, yet their integration into predictive models remains underexplored. This paper studies how to incorporate personal attributes (age, gender, prior VR experience) into Bayesian networks for cybersickness prediction via: (i) direct inclusion as root nodes, (ii) a two-stage model that learns a susceptibility score from personal attributes, and (iii) a stratified model. Using 26,040 samples from VR maze-navigation experiments, direct inclusion attains 82.53% accuracy (+14.02 percentage points over a 68.51% no-personal baseline). The two-stage approach reaches 77.32% while supporting cold-start prediction for unseen users, and stratified models achieve 73.62%. Using participant-level cross-validation to avoid subject leakage, we find that personalization consistently improves cybersickness prediction. These results argue that personal attributes should be treated as first-class signals in cybersickness models, with clear design trade-offs between maximal accuracy and deployability for unseen users, informing personalized VR systems and adaptive content delivery.