Supervised Fusion Learning of Physical Activity Features: Longitudinal Functional Accelerometer Data

Oregon Convention Center 

Accelerometry data collected by high-capacity sensors present a primary data type in smart mobile health. I holistically summarize an individual subject's activity profile using Occupation Time curves (OTCs). Being a functional predictor, OTCs describe the percentage of time spent at or above a continuum of activity count levels. The resulting functional curve is informative to capture time-course individual variability of physical activities both on the underlying functional variables of interest, as well as the specific health outcomes. I leverage the OTC curves to develop a longitudinal functional framework with repeated wearable data to understand the influence of serially measured functional accelerometer data on longitudinal health outcomes. I develop a new one-step method that can simultaneously conduct fusion via change-point detection and parameter estimation through a new L0 constraint formulation, invoking Quadratic Inference Functions (QIF), with an aim to detect physical activity intensity windows and assess their population-average effects on children health outcomes.

L0 regularization

changepoint detection

accelerometer

functional data analysis 

Biometrics Section