Predicting uncompensable heat stress with embedded, wearable sensors

John Kemp*, Elena Gaura, James Brusey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

The use of heavy protective clothing (such as by EOD operatives) brings problems related to the build-up of heat within the clothing, potentially endangering the health of the wearer and their activities. This paper presents a method of autonomously predicting the onset of thermally dangerous conditions such as Uncompensable Heat Stress in EOD operatives. The method is based on a Dynamic Bayesian Network, trained using Gaussian Kernel Density Estimators based on experimental data. An accuracy of 88.5% was achieved on unseen data when predicting the occurrence of heat stress up to two minutes in the future. The method is intended to be generally applicable to wearers of protective clothing in thermally challenging environments.

Original languageEnglish
Title of host publicationProceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012
Pages475-478
Number of pages4
DOIs
Publication statusPublished - 2012
EventIEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering - Hong Kong and Shenzhen, China
Duration: 2 Jan 20127 Jan 2012

Other

OtherIEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering
CountryChina
CityHong Kong and Shenzhen
Period2/01/127/01/12

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