一项关于分类和预测消防员操作疲劳水平的研究
Mingwei Xu1, Shangxue Yang1, Ke Wang1
1School of Safety Engineering, Beijing Institute of Petrochemical Technology, Beijing, China.
PloS one
|May 15, 2025
概括
在高层大火中,消防员的疲劳是一个主要问题. 这项研究开发了一个准确的模型,使用心电图和反应时间来分类和预测疲劳,提高运营安全性和效率.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 生理学 生理学 生理学
- 医疗保健中的人工智能
背景情况:
- 在高层建筑物中的消防工作具有极端的身体需求,导致消防员显著的疲劳.
- 疲劳会损害在紧急情况下的运营效率和关键决策.
- 有效的疲劳监测对于消防员的安全和任务的成功至关重要.
研究的目的:
- 开发一个强大的模型来分类和预测消防员疲劳水平.
- 为优化培训和运营战略建立科学基础.
- 为了增强消防员疲劳管理系统.
主要方法:
- 收集了心电图 (ECG) 信号,主观疲劳评级 (Borg's RPE尺度),以及模拟消防场景参与者的反应时间数据.
- 采用K-平均分类来进行五级疲劳分类,并对综合疲劳指数 (F) 进行量分析.
- 采用BP神经网络,使用心率和心率变化 (HRV) 度量作为输入,以预测疲劳指数 (F).
主要成果:
- 建立了一个三级疲劳分类 (轻度,中度,重度).
- 英国石油公司的神经网络模型对消防员疲劳状态具有很高的预测准确性,R2值为93.24%.
- 该模型有效地预测了疲劳水平的动态变化.
结论:
- 开发的疲劳分类和预测模型准确地评估和预测消防员的疲劳.
- 这种方法为改善消防员培训和运营效率提供了科学基础.
- 这些发现支持消防员疲劳监测和管理策略的进步.
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