Browsing by Author "Hoyt, Reed W"
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- ItemLife sign decision support algorithms.(2004-09-13) Savell, C Thomas; Borsotto, Maurizio; Reifman, Jaques; Hoyt, Reed WThere is a pressing need in the military for a system that interprets data from a suite of wearable physiological sensors to infer a soldier s current clinical state on the battlefield The Warfighter Physiological Status Monitoring WPSM concept is envisioned by the US Army to address this need Life sign detection is a key component The future WPSM system will consist of a body worn network of biosensors with a central processing control unit containing firmware for assessing the soldier s physiological status In the present application the system will diagnose a Dead Alive or Unknown DAU physiological state and this information will be made available to field medics and others over separate communication channels This paper describes the various modules in the DAU determination algorithms and how they interact with each other as well as a simulator system built for parametric studies of the overall system
- 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