Toward Accurate Cybersickness Prediction in Virtual Reality: A Multimodal Physiological Modeling Approach

Cybersickness poses a significant challenge to the widespread adoption of virtual reality (VR), as it impairs user experience and operational performance. This study proposes a physiological modeling approach to objectively assess cybersickness severity during VR experience. An interactive VR experiment was conducted, inducing varying levels of cybersickness through VR navigation tasks under different field-of-view and graphic quality settings. Physiological signals (i.e., electrodermal activity (EDA) and electrocardiogram (ECG)) were continuously recorded and extracted to build multiple machine learning regression models for cybersickness prediction. The results showed that EDA-based models consistently outperformed ECG-based models across all algorithms, with the Ensemble Learning model achieving the highest predictive accuracy (R² = 0.98). In contrast, ECG-based models yielded limited predictive capability (R² = 0.53). Combining ECG with EDA features showed little improvement in model accuracy, suggesting a limited complementary role of ECG features. SHAP-based feature importance analysis revealed that EDA features (e.g., mean, maximum, and variance of skin conductance) were the most effective features in cybersickness prediction, which captured both tonic arousal and phasic autonomic responses during the cybersickness process. ECG features such as SDNN and HRMAD contributed modestly, offering physiological interpretability despite being less effective in cybersickness prediction. The findings demonstrate the feasibility of using low-burden physiological signals for accurate and interpretable prediction of cybersickness severity. The proposed approach supports the development of lightweight, real-time monitoring systems for VR applications, offering practical advantages in terms of simplicity, adaptability, and deployment potential.