Emerging wearable and environmental sensor technologies provide health professionals with unprecedented capacity to continuously collect human behavioral data for health monitoring and management. This enables new solutions to mitigate globally emerging health problems such as obesity. With such outburst of dynamic sensor data, it is critical that appropriate mathematical models and computational methods are developed to translate the collected data into accurate characterization of the underlying health dynamics, enabling more reliable personalized monitoring, prediction, and intervention of health status changes. In addition to addressing common analytic challenges in analyzing sensor behavioral data, such as missing values and outliers, we focus on modeling heterogeneous dynamics to better capture health status changes under different conditions, which may lead to more effective state-dependent intervention strategies. We implement switching-state dynamic system models with different complexity levels on real-world daily behavioral data. Evaluation experiments of these models are conducted to demonstrate the importance of modeling the dynamic heterogeneity, as well as simultaneously conducting missing value imputation and outlier detection in achieving interpretable health dynamic models with better prediction of health status changes.
Keywords: Daily behavioral data analysis; Longitudinal patient health modeling; Missing data and outlier treatment; Mobile health; Switching-state dynamic systems.
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