NDLI: Human thermoregulatory system state estimation using non-invasive physiological sensors

Content Provider	IEEE Xplore Digital Library
Author	Buller, M.J. Castellani, J. Roberts, W.S. Hoyt, R.W. Jenkins, O.C.
Copyright Year	2011
Description	Author affiliation: Defence Science and Technology Organization, Melbourne, Australia (Roberts, W.S.) \|\| Computer Science Department at Brown University Providence RI, and U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760 USA (Buller, M.J.) \|\| U.S. Army Research Institute of Environmental; Medicine, Natick, MA 01760 USA (Castellani, J.; Hoyt, R.W.) \|\| Computer Science Department at Brown University Providence RI, USA (Jenkins, O.C.)
Abstract	Small teams of emergency workers/military can often find themselves engaged in critical, high exertion work conducted under challenging environmental conditions. These types of conditions present thermal work strain challenges which unmitigated can lead to collapse (heat exhaustion) or even death from heat stroke. Physiological measurement of these teams provides a mechanism that could be an effective tool in preventing thermal injury. While indices of thermal work strain have been proposed they suffer from ignoring thermoregulatory context and rely on measuring internal temperature (IT). Measurement of IT in free ranging ambulatory environments is problematic. In this paper we propose a physiology based Dynamic Bayesian Network (DBN) model that estimates internal temperature, heat production and heat transfer from observations of heart rate, accelerometry, and skin heat flux. We learn the model's conditional probability distributions from seven volunteers engaged in a 48 hour military field training exercise. We demonstrate that sum of our minute to minute heat production estimates correlate well with total daily energy expenditure (TDEE) measured using the doubly labeled water technique $(r^{2}$ = 0.73). We also demonstrate that the DBN is able to infer IT in new datasets to within ±0.5 °C over 85% of the time. Importantly, the additional thermoregulatory context allows critical high IT temperature to be estimated better than previous approaches. We conclude that the DBN approach shows promise in enabling practical real time thermal work strain monitoring applications from physiological monitoring systems that exist today.
Starting Page	3290
Ending Page	3293
File Size	1026660
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090893
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heating Temperature measurement Heat transfer Data models Strain Mercury (metals) Production
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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