Normalization and extraction of interpretable metrics from raw accelerometry data

Jiawei Bai, Bing He, Haochang Shou, Vadim Zipunnikov, Thomas A. Glass, Ciprian M. Crainiceanu

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

Original languageEnglish (US)
Pages (from-to)102-116
Number of pages15
JournalBiostatistics
Volume15
Issue number1
DOIs
StatePublished - Jan 2014

Keywords

  • Activity intensity
  • Movelets
  • Movement
  • Signal processing
  • Time active
  • Tri-axial accelerometer

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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