飞行员转向行为认知负载分析在模拟飞行中的模拟飞行
Wen-Gang Zhou1, Pan-Pan Yu1, Liang-Hai Wu1
1Flight Technology College, Civil Aviation Flight University of China, Guanghan, China.
Frontiers in neuroscience
|October 8, 2024
概括
飞行员在飞行转期间的认知负载可以使用心率变化 (HRV) 度量来确定. 一个先进的LSTM-Attention模型准确地识别了不同的认知负载,帮助飞行员培训和飞行安全.
科学领域:
- 航空心理学 航空心理学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 一个飞行实验模拟了飞行员"初步选"培训模块,以评估各种转任务期间的认知负载.
- 重建了真实世界的飞行场景,为数据收集提供了一个现实的实验环境.
研究的目的:
- 识别和量化与模拟飞行中不同转向机动相关的认知负载.
- 开发和验证一种机器学习模型,用于识别飞行员在飞行转时的认知负载.
主要方法:
- 采集心率变量 (HRV) 和飞行数据,使用飞行模拟器和心率传感器手.
- 分类转向行为到爬,下降,和水平飞行转.
- 开发了一种使用机器学习和深度学习算法来分析认知负载的识别模型.
主要成果:
- 特定的HRV指标 (例如,RMSSD,SD1) 与转向任务的认知负载有负相关性.
- LSTM-Attention模型在识别不同认知负载方面表现出高准确性,F1得分为0.9491.
结论:
- HRV特征对于分析不同飞行转向任务中的认知负载是有效的.
- LSTM-Attention模型为未来对飞行员认知负载的研究提供了有价值的工具,并可以为飞行员培训策略提供信息.
- 这些发现对加强飞行员培训和改善整体飞行安全有重大影响.
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