Distinguishing Startle from Surprise Events Based on Physiological Signals
This work addresses a safety-critical issue for pilots in aviation by enabling better differentiation of reactions that impair performance, though it is incremental as it applies existing machine learning methods to a new domain-specific dataset.
The paper tackled the problem of distinguishing startle from surprise events using physiological signals, achieving a highest mean accuracy of 85.7% for binary classification and 74.9% for ternary classification including a baseline state.
Unexpected events can impair attention and delay decision-making, posing serious safety risks in high-risk environments such as aviation. In particular, reactions like startle and surprise can impact pilot performance in different ways, yet are often hard to distinguish in practice. Existing research has largely studied these reactions separately, with limited focus on their combined effects or how to differentiate them using physiological data. In this work, we address this gap by distinguishing between startle and surprise events based on physiological signals using machine learning and multi-modal fusion strategies. Our results demonstrate that these events can be reliably predicted, achieving a highest mean accuracy of 85.7% with SVM and Late Fusion. To further validate the robustness of our model, we extended the evaluation to include a baseline condition, successfully differentiating between Startle, Surprise, and Baseline states with a highest mean accuracy of 74.9% with XGBoost and Late Fusion.