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

Abstract Number:

3554 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Margaret Banker (1), Peter Song (2)

Institutions:

(1) Northwestern Feinberg School of Medicine, N/A, (2) University of Michigan, N/A

Co-Author:

Peter Song  
University of Michigan

First Author:

Margaret Banker  
Northwestern Feinberg School of Medicine

Presenting Author:

Margaret Banker  
University of Michigan

Abstract Text:

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.

Keywords:

L0 regularization|changepoint detection|accelerometer|functional data analysis| |

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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