Literature DB >> 23999141

Normalization and extraction of interpretable metrics from raw accelerometry data.

Jiawei Bai1, Bing He, Haochang Shou, Vadim Zipunnikov, Thomas A Glass, Ciprian M Crainiceanu.   

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.

Entities:  

Keywords:  Activity intensity; Movelets; Movement; Signal processing; Time active; Tri-axial accelerometer

Mesh:

Year:  2013        PMID: 23999141      PMCID: PMC4072911          DOI: 10.1093/biostatistics/kxt029

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  17 in total

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Authors:  M L Blood; R L Sack; D C Percy; J C Pen
Journal:  Sleep       Date:  1997-06       Impact factor: 5.849

5.  Determination of sleep and wakefulness with the actigraph data analysis software (ADAS).

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6.  Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients.

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Review 9.  The role of actigraphy in the study of sleep and circadian rhythms.

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  19 in total

1.  Accelerometry data in health research: challenges and opportunities.

Authors:  Marta Karas; Jiawei Bai; Marcin Strączkiewicz; Jaroslaw Harezlak; Nancy W Glynn; Tamara Harris; Vadim Zipunnikov; Ciprian Crainiceanu; Jacek K Urbanek
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2.  Assessing the "physical cliff": detailed quantification of age-related differences in daily patterns of physical activity.

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3.  Classification of human physical activity based on raw accelerometry data via spherical coordinate transformation.

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6.  Generalized multilevel function-on-scalar regression and principal component analysis.

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7.  A semiparametric model for wearable sensor-based physical activity monitoring data with informative device wear.

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8.  Innovative modeling of naturalistic driving data: Inference and prediction.

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9.  An Evaluation of Accelerometer-derived Metrics to Assess Daily Behavioral Patterns.

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10.  Movement prediction using accelerometers in a human population.

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Journal:  Biometrics       Date:  2015-08-19       Impact factor: 2.571

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