为了确保航空安全:基于短期多式联运生理信号的飞行员疲劳检测
Kai Chen1, Jiming Liu2, Jiamei Zhu1
1East China Personnel Medical Appraisal Center, Civil Aviation Shanghai Hospital, Shanghai, China.
Frontiers in human neuroscience
|February 19, 2026
概括
这项研究引入了一个快速而准确的飞行员疲劳检测系统,使用脑电图 (EEG) 和心电图 (ECG) 信号. 这种新的方法显著降低了计算成本,同时保持了航空安全的高精度.
科学领域:
- 航空航天工程 航空航天工程
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 飞行员疲劳对航空安全构成重大风险.
- 目前的疲劳检测方法难以平衡精度和计算效率,特别是在短的生理信号段.
- 深度学习模型通常具有高的计算成本,限制了它们的实际应用.
研究的目的:
- 开发一个快速,准确和强大的飞行员疲劳检测框架.
- 将脑电图 (EEG) 和心电图 (ECG) 信号的特征融合在一起,以提高性能.
- 为了克服传统的心率变化 (HRV) 分析在短信号段的局限性.
主要方法:
- 开发了一个新的框架,融合了EEG和ECG特征.
- 统计ECG特征和EEG标记被整合在一起.
- 为了简化特征选择,使用了两阶段的ANOVA-SVM过程.
- 用一个XGBoost模型来对优化的功能集进行分类.
主要成果:
- 该框架在跨主题验证中实现了88.42%的准确性,证明了强大的概括性.
- 在标准交叉剪贴验证中观察到98.36%的更高准确度.
- 该模型的平均训练时间仅为39.3秒,大大降低了计算成本.
结论:
- 拟议的框架为客观的飞行员疲劳管理提供了一个有希望和可行的方法.
- 它有效地平衡了准确性,概括性和计算效率.
- 这种方法与以前仅使用EEG的模型相比,具有显著的进步.
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