Browsing by Author "Buller, Mark J"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- ItemIndividualized short-term core temperature prediction in humans using biomathematical models.(2008-04-28) Gribok, Andrei V; Buller, Mark J; Reifman, JaquesThis study compares and contrasts the ability of three different mathematical modeling techniques to predict individual specific body core temperature variations during physical activity The techniques include a first principles physiology based SCENARIO model a purely data driven model and a hybrid model that combines first principles and data driven components to provide an early short term 20 30 min ahead warning of an impending heat injury Their performance is investigated using two distinct datasets a Field study and a Laboratory study The results indicate that for up to a 30 min prediction horizon the purely data driven model is the most accurate technique followed by the hybrid For this prediction horizon the first principles SCENARIO model produces root mean square prediction errors that are twice as large as those obtained with the other two techniques Another important finding is that if properly regularized and developed with representative data data driven and hybrid models can be made portable from individual to individual and across studies thus significantly reducing the need for collecting developmental data and constructing and tuning individual specific models
- ItemProviding statistical measures of reliability for body core temperature predictions.(2007-11-16) Gribok, Andrei V; Buller, Mark J; Hoyt, Reed W; Reifman, JaquesThis paper describes the use of a data driven autoregressive integrated moving average model to predict body core temperature in humans during physical activity We also propose a bootstrap technique to provide a measure of reliability of such predictions in the form of prediction intervals We investigate the model s predictive capabilities and associated reliability using two distinct datasets both obtained in the field under different environmental conditions One dataset is used to develop the model and the other one containing an example of heat illness is used to test the model We demonstrate that accurate and reliable predictions of an extreme core temperature value of 39 5 degrees C can be made 20 minutes ahead of time even when the predictive model is developed on a different individual having core temperatures within healthy physiological limits This result suggests that data driven models can be made portable across different core temperature levels and across different individuals Also we show that the bootstrap prediction intervals cover the actual core temperature and that they exhibit intuitively expected behavior as a function of the prediction horizon and the core temperature variability