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Noninvasive monitoring of human circadian phase using model-based particle filter estimation

University of British Columbia

This thesis presents a novel method for monitoring the state of an individual’s circadian physiology. While scientific understanding of circadian physiology has advanced rapidly in recent years, the difficulty of measuring an individual’s circadian phase in a non-invasive and portable manner remains a barrier to applications in workplace safety and medical fields. Existing approaches to circadian monitoring use measurements from a single physiological marker such as core body temperature. A new modelling framework is developed here that allows the integration of multiple physiological measurements and a predictive model of circadian physiology dynamics. On top of this framework a particle filter estimation algorithm is implemented that accurately captures the nonlinear dynamics of the circadian system and allows real-time incorporation of sensory input using Bayesian statistics. A human study in which subjects spent five days in an laboratory while continuously monitored with an array of sensors generated data from which the algorithm is demonstrated. Tuning parameters and the theoretical performance limits of the algorithm are also explored through a series of simulations. The novel conceptual aspects of the modelling framework will require further validation however results indicate that this system presents a set of capabilities leading towards application for circadian monitoring beyond the laboratory.

Text

2007-06-14

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http://hdl.handle.net/2429/18250

Electrical and Computer Engineering

University of British Columbia

UBCV

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