Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

by   Pierfrancesco Alaimo Di Loro, et al.

Rapid developments in streaming data technologies are continuing to generate increased interest in monitoring human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing a very large amount of data at a high-resolution that can be immediately downloaded and analyzed. While this kind of big data includes both spatial and temporal information, the variation in such data seems to be more appropriately modeled by considering stochastic evolution through time while accounting for spatial information separately. We propose a comprehensive Bayesian hierarchical modeling and inferential framework for actigraphy data reckoning with the massive sizes of such databases while attempting to offer full inference. Building upon recent developments in this field, we construct Nearest Neighbour Gaussian Processes (NNGPs) for actigraphy data to compute at large temporal scales. More specifically, we construct a temporal NNGP and we focus on the optimized implementation of the collapsed algorithm in this specific context. This approach permits improved model scaling while also offering full inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset concerning a health study conducted by the Fielding School of Public Health of the University of California, Los Angeles.



There are no comments yet.


page 4

page 5

page 13

page 19

page 21

page 25


Grid-Parametrize-Split (GriPS) for Improved Scalable Inference in Spatial Big Data Analysis

Rapid advancements in spatial technologies including Geographic Informat...

Fast Bayesian inference for large occupancy data sets, using the Polya-Gamma scheme

In recent years, the study of species' occurrence has benefited from the...

GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer

Group activity recognition is a crucial yet challenging problem, whose c...

Spatial Multivariate Trees for Big Data Bayesian Regression

High resolution geospatial data are challenging because standard geostat...

Parametric Gaussian Process Regression for Big Data

This work introduces the concept of parametric Gaussian processes (PGPs)...

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

Emerging wearable sensors have enabled the unprecedented ability to cont...

A flexible Bayesian framework for individualized inference via dynamic borrowing

The explosion in high-resolution data capture technologies in health has...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.