Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health

TL;DR

This paper introduces an individualized dynamic latent factor model designed for multi-resolution, irregular time series data in mobile health, enabling better interpolation and integration of heterogeneous measurements across subjects.

Contribution

It presents a novel model that captures individual heterogeneity and multi-resolution data, with theoretical error bounds and superior empirical performance.

Findings

The model effectively interpolates unsampled measurements.

It outperforms existing methods in simulations.

Application to smartwatch data confirms practical utility.

Abstract

Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data which arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.