Amortized Bayesian inference for actigraph time sheet data from mobile devices

TL;DR

This paper introduces an amortized Bayesian inference method tailored for analyzing actigraph time sheet data from mobile devices, enabling uncertainty quantification and probabilistic imputation in health-related movement studies.

Contribution

It develops a hierarchical Bayesian framework for actigraph data that supports transfer learning and uncertainty propagation, advancing analysis capabilities for high-resolution mobility data.

Findings

Probabilistic imputation of actigraph time sheets achieved

Learned time-varying effects of explanatory variables on acceleration

Applied method to LA mobility study data

Abstract

Mobile data technologies use ``actigraphs'' to furnish information on health variables as a function of a subject's movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to conduct research on mobility patterns and health outcomes. Statistical methods for analyzing high-resolution actigraph data depend on the specific inferential context, but the advent of Artificial Intelligence (AI) frameworks require that the methods be congruent to transfer learning and amortization. This article devises amortized Bayesian inference for actigraph time sheets. We pursue a Bayesian approach to ensure full propagation of uncertainty and its quantification using a hierarchical dynamic linear model. We build our analysis around actigraph data from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study conducted by the Fielding School of Public Health in the University of California, Los Angeles. Apart from achieving probabilistic imputation of actigraph time sheets, we are also able to statistically learn about the time-varying impact of explanatory variables on the magnitude of acceleration (MAG) for a cohort of subjects.