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.

Enhancing user experience and performance, including task load in immersive environments, requires accurate prediction of user gaze point, reaction time, and mental and physical load uptake. Current gaze prediction approaches focus primarily on motion-based information, lacking physiological data, which leads to poor prediction accuracy in highly dynamic virtual reality (VR) environments. Traditional cognitive load measurements rely on post-task analysis without proper multimodal data integration and fail to capture the real-time dynamics of user states during interaction. Likewise, reaction time or attention load are often assessed only after the interaction, without using real-time immersive sensor data, which limits adaptive responsiveness. To tackle these limitations, we leveraged a comprehensive multimodal dataset - VRWalking, which recorded timestamped eye-tracking metrics, physiological signals (heart rate and galvanic skin response), and behavioral performance data during real-time engagement in a VR environment. We developed a unified multitask model based on the MultiPatchFormer architecture, which processes multimodal VR signals through dual patch projection branches for gaze and classification inputs. The model employs multiscale patch embeddings, cross-attention between gaze and classification pathways, channel attention, and transformer encoders to jointly predict continuous user gaze and classify reaction time, cognitive load (mental load and physical load). Our methodology achieved excellent predictive performance: 95.64% for reaction time, 98.01% for mental load, and 97.45% for physical load, with a MAPE (Mean Absolute Percentage Error) of 15.24% for gaze prediction. We applied Shapley Additive explanations (SHAP) analysis to interpret the model’s behavior across all features, including eye-tracking, head-tracking, and physiological signals. The analysis revealed which features most influenced the predictions of user gaze, reaction time, mental load, and physical load. Our methods, while based only on the VRWalking dataset, demonstrated strong performance across all tasks, suggesting promising potential for real-world VR applications such as interactive training systems that respond to user attention lapses, educational platforms that adapt to cognitive load, and performance assessments that consider physiological indicators.

Virtual Reality (VR) is quickly establishing itself in various industries, including training, education, medicine, and entertainment, in which users are frequently required to carry out multiple complex cognitive and physical activities. However, the relationship between cognitive activities, physical activities, and familiar feelings of cybersickness is not well understood and thus can be unpredictable for developers. Researchers have previously provided labeled datasets for predicting cybersickness while users are stationary, but there have been few labeled datasets on cybersickness while users are physically walking. Moreover, it is unclear how walking while cybersick will affect cognitive load, even though room-scale interaction is typical in many VR games. Thus, from 39 participants, we collected head orientation, head position, eye tracking, images, physiological readings from external sensors, and the self-reported cybersickness severity, physical load, and mental load in VR. Throughout the data collection, participants navigated mazes via real walking and performed tasks challenging their attention and working memory. To demonstrate the dataset’s utility, we conducted a case study of training classifiers in which we achieved 95% accuracy for cybersickness severity classification. The noteworthy performance of the straightforward classifiers makes this dataset ideal for future researchers to develop cybersickness detection and reduction models. To better understand the features that helped with classification, we performed SHAP(SHapley Additive exPlanations) analysis, highlighting the importance of eye tracking and physiological measures for cybersickness prediction while walking. This open dataset can allow future researchers to study the connection between cybersickness and cognitive loads and develop prediction models. This dataset will empower future VR developers to design efficient and effective Virtual Environments by improving cognitive load management and minimizing cybersickness.