Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness

Simulator sickness not only degrades the quality of interaction within simulator environments but also poses challenges for user enjoyment and the long-term viability of simulation technologies. The early diagnosis of nausea, disorientation, and oculomotor issues related to simulator sickness is crucial for improving user experience and minimizing adverse side effects. In this paper, we aim to classify and predict the onset of these issues by establishing thresholds that indicate the presence of symptoms. A cohort of 20 participants was subjected to an 8-minute simulated flight video. During this period, both objective and subjective parameters are gathered using the Equivital sensor and questionnaire. Our research demonstrates promising results in predicting nausea symptoms, achieving high accuracy (91.68%), recall (87.50%), F1 score (86.81%), and AUC (0.905). Our findings reveal that while the inter-beat interval serves as a primary predictor of simulator sickness, the combination of other signals with distinct thresholds also plays a critical role in predicting each symptom, highlighting the complex interaction of multiple factors. In conclusion, these findings have significant practical implications for the development of early warning systems and the enhancement of immersive technologies across various domains. Designers and developers can use these insights to improve user experiences, reduce simulator sickness, and optimize the effectiveness of immersive simulations.