基于飞行关键事件的飞行员在飞行中的压力状态评估模型的研究
Lamei Shang1, Manting Lu2, Chunying Qian2
1Country Aviation Medical Engineering Research Center, Air Force Medical University, PLA, Beijing, China.
Frontiers in physiology
|March 6, 2026
概括
这项研究开发了一种机器学习模型,使用生理数据来评估飞行员在飞行期间的压力. 该模型准确预测压力水平,有助于提高培训和飞行安全.
科学领域:
- 航空航天医学 航空航天医学
- 生理监测 生理监测
- 机器学习应用 机器学习应用
背景情况:
- 在高压任务期间的飞行员压力会损害认知功能和任务有效性.
- 目前评估飞行员在飞行过程中的压力的方法有限.
- 制定准确的飞行压力评估对于优化性能和安全至关重要.
研究的目的:
- 建立一个评估模型,用于预测飞行员在飞行期间的压力状态.
- 用实时生理数据来评估压力水平.
- 加强飞行员压力管理和培训策略.
主要方法:
- 收集了32名飞行员 (年龄在22-30岁) 的飞行过程中的生理数据 (心电图,呼吸,加速).
- 确定了不同压力状态的敏感生理指标.
- 使用心电图和呼吸指数构建了一个机器学习模型.
主要成果:
- 确定了10个敏感的心电图指标 (例如,平均HR,SDNN,LF/HF) 和5个呼吸指标 (例如,平均RSP).
- 开发了一种集体学习模型,用于二进制压力状态分类.
- 在压力状态评估的五倍交叉验证中获得了95.56%的准确性.
结论:
- 使用生理特征有效评估飞行员压力状态.
- 为优化飞行员压力训练和应对机制提供基础.
- 为飞行任务性能和安全提供理论意义和工程价值.
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