论文信息 - ALTERNATIVE APPROACHES TO IMPROVE PHYSIOLOGICAL PREDICTIONS

ALTERNATIVE APPROACHES TO IMPROVE PHYSIOLOGICAL PREDICTIONS

Abstract : Recent advancements in technology have resulted in new biosensors and information processing capabilities that permit on-line, real-time measurement of physiological variables. This has, in turn, given rise to the possibility of developing soldier-specific, data-driven predictive models for assessing physiological status in the battlefield. This paper explores how the accuracy of a predictive model based on first principles physiology can be enhanced by data-driven "black box" techniques of modeling and predicting human physiological variables. Such hybrid techniques are employed here in the prediction of core temperature. Preliminary results show that the mean square error of prediction can be reduced by up to fifty percent for prediction horizons of up to 30 minutes.

[1] Mark J. Buller,et al. Combat medical informatics: present and future , 2002, AMIA.

[2] K. Pandolf,et al. Heat strain models applicable for protective clothing systems: comparison of core temperature response. , 1997, Journal of applied physiology.

[3] J. Werner,et al. A dynamic model of the human/clothing/environment-system. , 1997, Applied human science : journal of physiological anthropology.

[4] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .

[5] K. Kraning,et al. A mechanistic computer simulation of human work in heat that accounts for physical and physiological effects of clothing, aerobic fitness, and progressive dehydration , 1997 .

[6] J. A. Wilson,et al. A generalised approach to process state estimation using hybrid artificial neural network/mechanistic models , 1997 .

[7] Jaques Reifman. Alternative methods for modeling fatigue and performance. , 2004, Aviation, space, and environmental medicine.

[8] S. D. Murphy,et al. Reactions to environmental agents , 1977 .

[9] Roger W. Johnson,et al. An Introduction to the Bootstrap , 2001 .

[10] T. Adams. The control of body temperature. , 1960, Technical report.; TR. Arctic Aeromedical Laboratory.

[11] Richard B. Vilim,et al. An identification scheme combining first principle knowledge, neural networks, and the likelihood function , 2001, IEEE Trans. Control. Syst. Technol..