A Statistical Abstraction Framework for Integrating Heterogeneous VR Datasets in Ordinal Cybersickness Prediction

Despite the rapid growth of Virtual Reality (VR) technology across diverse applications, cybersickness remains a significant barrier to its widespread adoption. To move toward effective mitigation, current efforts have concentrated on predicting cybersickness, particularly through the integration of multimodal data sources. However, current cybersickness prediction models face several limitations that impede progress toward robust, generalizable systems: most studies are confined to single datasets due to substantial technical challenges in integrating heterogeneous VR sensor data from different hardware platforms, and existing approaches fail to appropriately model the ordinal nature of cybersickness severity scales, treating Fast Motion Sickness (FMS) scores as either continuous variables or arbitrary categorical classifications that discard valuable ordering information. We developed a comprehensive statistical feature abstraction framework combined with dual-head ordinal regression to enable cybersickness prediction across heterogeneous VR datasets. Our approach transforms diverse sensor modalities from VR.net (Meta Quest Pro proprietary systems), SIM21, and VRWalking (HTC VIVE/Tobii) platforms into standardized statistical descriptors that capture cybersickness-relevant behavioral patterns while remaining invariant to study design and data collection methodologies. The dual-head architecture combines ordinal-aware predictions with direct regression outputs through weighted averaging. Using the VR.net and VRWalking datasets, we achieved cross-dataset generalization with an RMSE of 0.8341 ± 0.0731 and MAE of 0.6254 ± 0.0389 through rigorous 10-fold participant-aware cross-validation, along with 97.7% ± 0.87% accuracy in severity classification despite heterogeneous hardware configurations and scale differences. Our statistical feature abstraction approach enables effective cross-platform cybersickness prediction by creating domain-agnostic representations that preserve essential physiological patterns, advancing the development of universal cybersickness monitoring systems capable of operating across diverse VR platforms.