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Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks

机译:利用来自生理数据的知识:利用传感器网络进行人体热应激风险预测

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摘要

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1 ± 2.9% and 94.4 ± 2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimise casualties.
机译:本文证明,可穿戴式传感器系统与实时人体处理和启动相结合,可以通过降低不可补偿的热应力(UHS)的风险来提高重型防护设备的佩戴者的安全性,这些佩戴者受到恶劣的热环境。该工作集中于爆炸物处置工作人员,并表明可以实时准确地进行UHS风险预测,以供实际使用。此外,示出了可以使用可穿戴的非侵入式传感器来获得用于这种算法的所需的感觉输入。提出了两种算法,一种是基于贝叶斯网络的算法,另一种是基于决策树的算法,将平均皮肤温度预测作为代理来确定热应激风险。使用经验数据训练算法,使用留一法则交叉验证进行测试时,算法的准确度分别为92.1±2.9%和94.4±2.1%。在诸如爆炸物处置操作监控之类的应用中,此类预测算法可以实现冷却系统和触觉警报的自主启动,以将人员伤亡降至最低。

著录项

  • 作者

    Gaura E.; Kemp J.; Brusey J.;

  • 作者单位
  • 年度 2013
  • 总页数
  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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