用生理信号区分飞行员的惊跳与惊讶反应，提升高风险环境安全预警能力。
Distinguishing Startle from Surprise Events Based on Physiological Signals
- 融合多模态生理数据，用机器学习区分惊跳与惊讶事件。
- 最高准确率达85.7%（SVM+Late Fusion），三状态分类达74.9%。
- 适用于航空等高风险场景中人因行为监测，适合安全系统开发者。
意外事件会损害注意力并延迟决策，在航空等高风险环境中可能带来严重安全隐患。惊跳与惊讶反应对飞行员表现的影响不同，但实践中难以区分。现有研究多将两者分开分析，缺乏对联合影响或基于生理数据识别的研究。本文通过机器学习与多模态融合策略，利用生理信号区分惊跳与惊讶事件。结果表明，该方法可可靠预测两类反应，最高平均准确率达85.7%（采用SVM与晚期融合）。为验证模型鲁棒性，进一步在基线条件下评估，成功区分惊跳、惊讶与基线状态，最高平均准确率为74.9%（采用XGBoost与晚期融合）。
原文摘要 · Abstract (English)
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.
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