SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults.

ABSTRACT:

Introduction

Accelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for extracting intuitive, continuous measures of participants' health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep.

Methods

In this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation

SUBMITTER: Lin W 

PROVIDER: S-EPMC10715430 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature