通过多模式生理学区分飞行员的困境和压力:在智能驾驶中加强人与系统的整合
Yanzeng Zhao1,2, Keyong Zhu2, Wei Guo3
1International Innovation Institute, Beihang University, Hangzhou, China.
Ergonomics
|August 29, 2025
概括
这项研究引入了一个神经心脏框架,使用功能近红外光谱 (fNIRS) 和心电图 (ECG) 准确区分飞行员的压力和痛苦. 多式联接方式提高了智能驾驶的安全性.
科学领域:
- 神经科学
- 心脏病学
- 航空航天工程
- 人与计算机的交互
背景情况:
- 确保智能驾驶的飞行安全需要精确的飞行员应激评估,区分有益的应激和有害的应激.
- 现有的压力监测方法不足以在不同的飞行任务中区分这些状态.
- 下一代驾驶需要适应飞行员的认知和情绪状态的系统.
研究的目的:
- 开发和验证多式神经心脏框架,以区分飞行员在模拟飞行中的压力和痛苦.
- 从fNIRS和心电图数据中确定可靠地表明不同类型的压力.
- 评估在这些生物标志物上训练的机器学习模型的有效性,以进行试验压力分类.
主要方法:
- 在模拟飞行任务中收集了35名参与者的多式生理数据 (fNIRS和心电图).
- 从神经心脏数据中提取并分析了11个关键特征,
- 训练和评估机器学习分类模型,使用识别的特征来预测飞行员的压力水平.
主要成果:
- 11个不同的生理特征被确定为压力和痛苦之间的显著差异.
- 开发的机器学习模型在分类飞行员在不同飞行任务中的压力方面取得了83.04%的整体准确性.
- 在特定任务中,模型的准确性高达90.83%,在受控条件下显示出高精度.
结论:
- 基于fNIRS-ECG的多式模式框架提供了强大的准确方法来分类飞行员的压力状态.
- 从这种方法获得的客观生物标志物对于开发适应性智能驾驶系统至关重要.
- 这项研究有助于提高飞行安全和优化航空中的人机交互.
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