基于SVM的飞行员急性压力的识别,使用客观指标
Hao Jiang1,2, Chenxu Zhang1,2, Xing Peng1,2
1College of Flight Technique, Civil Aviation Flight University of China, Sichuan, China.
Ergonomics
|June 22, 2025
概括
在飞行模拟期间经历急性压力的飞行员表现出显著的生理和生化变化. 一个机器学习模型准确地识别了这些压力状态,提供了一种监测飞行员压力的新方法.
科学领域:
- 航空心理学 航空心理学
- 人类因素工程 人类因素工程
- 航空航天医学 航空航天医学
背景情况:
- 突然发生的飞行事故会引发严重的压力,可能会危及航空安全.
- 对飞行员压力的客观监测对于加强飞行安全协议至关重要.
研究的目的:
- 研究飞行学员在模拟发动机故障期间对急性压力的生理和生化反应.
- 开发和验证用于客观飞行员压力检测的机器学习模型.
主要方法:
- 在37名飞行学员的C172-G1000模拟器上降落时模拟发动机故障.
- 收集的数据包括状态焦虑,心率,心率变化,唾液皮质醇,高度和方向.
- 使用特征选择,规范化和超参数调整开发了一个支持向量机器模型.
主要成果:
- 在压力和非压力条件之间的生理,生化和行为数据中观察到显著的差异.
- 支矢量机器模型在区分应力和非应力状态方面取得了86.49%的准确性.
- 确定了客观压力监测飞行员的关键指标.
结论:
- 该研究成功展示了一种在关键飞行阶段客观监测飞行员压力水平的方法.
- 这些发现支持将压力监测工具纳入航空培训和运营.
- 客观压力评估可以有助于提高飞行员绩效和飞行安全.
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