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 language | English |
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| Title of host publication | Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 |
| Pages | 475-478 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | IEEE-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 2012 → 7 Jan 2012 |
Other
| Other | IEEE-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 |
|---|---|
| Country/Territory | China |
| City | Hong Kong and Shenzhen |
| Period | 2/01/12 → 7/01/12 |