Hybrid approaches to physiologic modeling and prediction

Nicholas O. Oleng'; Jaques Reifman

doi:10.1117/12.605323

23 May 2005 Hybrid approaches to physiologic modeling and prediction

Nicholas O. Oleng', Jaques Reifman

Proceedings Volume 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations; (2005) https://doi.org/10.1117/12.605323
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Nicholas O. Oleng' and Jaques Reifman "Hybrid approaches to physiologic modeling and prediction", Proc. SPIE 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations, (23 May 2005); https://doi.org/10.1117/12.605323

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available