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

Elena Gaura, John Kemp, James Brusey

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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 minimize casualties.

Original languageEnglish
Article number6522188
Pages (from-to)861-870
Number of pages10
JournalIEEE Transactions on Biomedical Circuits and Systems
Issue number6
Publication statusPublished - Dec 2013


  • Biomedical informatics
  • body sensor networks
  • decision support systems
  • predictive models


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