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Core body temperature measurement using an ingestible pill has been proven effective for field-based ambulatory applications. The ingestible pill overcomes many impracticalities related with traditional methods of assessing core body temperature, however, it suffers from two key issues: random gaps due to missing data and outliers due to electromagnetic intereference. In this paper, we propose a principled convex optimization based framework for preprocessing the raw core body temperature signal. The proposed framework assumes that the raw core body temperature signal consists of two components: a smooth low-frequency component and a transient component with sparse outliers. We derive a computationally efficient algorithm using the majorization-minimization procedure and show its performance on simulated data. Finally, we demonstrate utility of the proposed method for estimating the circadian rhythm of core body temperature in cognitively normal elderly.

0325 Nonlinear Smoothing of Data with Random Gaps and Outliers (DRAGO) improves estimation of Circadian Rhythm