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Proceedings Paper

Hybrid approaches to physiologic modeling and prediction
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Paper Abstract

This paper explores how the accuracy of a first-principles physiological model can be enhanced by integrating data-driven, "black-box" models with the original model to form a "hybrid" model system. Both linear (autoregressive) and nonlinear (neural network) data-driven techniques are separately combined with a first-principles model to predict human body core temperature. Rectal core temperature data from nine volunteers, subject to four 30/10-minute cycles of moderate exercise/rest regimen in both CONTROL and HUMID environmental conditions, are used to develop and test the approach. The results show significant improvements in prediction accuracy, with average improvements of up to 30% for prediction horizons of 20 minutes. The models developed from one subject's data are also used in the prediction of another subject's core temperature. Initial results for this approach for a 20-minute horizon show no significant improvement over the first-principles model by itself.

Paper Details

Date Published: 23 May 2005
PDF: 11 pages
Proc. SPIE 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations, (23 May 2005); doi: 10.1117/12.605323
Show Author Affiliations
Nicholas O. Oleng', U.S. Army Medical Research and Materiel Command (United States)
Jaques Reifman, U.S. Army Medical Research and Materiel Command (United States)


Published in SPIE Proceedings Vol. 5797:
Biomonitoring for Physiological and Cognitive Performance during Military Operations
John A. Caldwell; Nancy Jo Wesensten, Editor(s)

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