一个实用的深度学习模型用于预测高温环境中专业工人的核心温度
Xinge Han1, Jiansong Wu2, Zhuqiang Hu3
1School of Emergency Management & Safety Engineering, China University of Mining and Technology, Beijing, 10083, China.
Journal of thermal biology
|February 19, 2025
概括
一个新的模型使用皮肤温度和心率预测核心体温 (Tcr),提供了一种实用的方法来监测工作人员在炎热的环境中. 这种非侵入性方法有助于在危险操作期间预防与热有关的疾病.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 生物医学工程 生物医学工程
- 环境健康 环境健康
背景情况:
- 全球气温上升增加了危险操作期间热暴露的频率和严重性.
- 升高的核心体温 (Tcr) 损害生理功能,对工人造成重大健康风险.
- 实时Tcr监控在极端高温环境中至关重要,但具有挑战性.
研究的目的:
- 为准确,实时预测核心体温 (Tcr) 开发一种非侵入性模型.
- 为在高温工作环境中提供健康监测和人员保护的实用工具.
- 解决当前在危险条件下的TCR监测方法的局限性.
主要方法:
- 一个新的非侵入性预测模型,将卡尔曼波器与深度学习长期序列预测模型集成在一起.
- 使用监控的皮肤温度 (Tsk) 和心率 (HR) 作为个性化Tcr预测的输入特征.
- 在受控的高温条件下 (34-40°C) 与参与者进行室内实验验证实了该模型.
主要成果:
- 该模型实现了高精度,平均绝对误差 (MAE) 为0.07,根平均平方误差 (RMSE) 为0.09,R平方 (R2) 为0.93.
- 所有Tcr预测中的95%都在±0.17°C的误差范围内.
- 该模型使用七点Tsk与人力资源数据相结合,证明了有效性.
结论:
- 开发的非侵入性Tcr预测模型为极端高温下的健康监测提供了实用和准确的解决方案.
- 简单的输入要求和高预测准确度使其适合在危险,高温作业中保护工人.
- 这项技术可以显著提高职业安全,并减轻与热有关的健康风险.
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