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Accelerometer techniques for capturing human movement validated against direct observation: a scoping review.

Elyse Letts1, Josephine S Jakubowski1,2, Sara King-Dowling3

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This review maps accelerometer data analysis methods for physical activity, finding machine learning and cut-point approaches are common. Wear location influences analysis and outcomes, highlighting the need for standardized validation.

Keywords:
accelerometryanalysis techniquescalibrationmachine learningphysical activitysedentaryvalidation

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Area of Science:

  • Kinesiology
  • Biomedical Engineering
  • Public Health

Background:

  • Accelerometers are crucial for measuring physical activity and sedentary behavior.
  • Rapid advancements in accelerometer technology and analysis methods pose challenges for researchers.
  • Standardized best practices for accelerometer data processing and analysis are needed.

Purpose of the Study:

  • To conduct a scoping review of validated accelerometer data analysis methods for human movement.
  • To identify techniques validated against direct observation as the criterion measure.
  • To provide an overview of current practices in accelerometer data analysis.

Main Methods:

  • A comprehensive search of 14 academic and 5 grey literature databases was performed.
  • Two independent reviewers screened titles, abstracts, and full texts.
  • Data extraction and validation were conducted using Microsoft Excel by an independent reviewer.

Main Results:

  • The review included 115 papers from 1039 initial searches, utilizing 71 accelerometer models and 4217 participants.
  • Analysis techniques comprised machine learning (22%), existing cut-points (18%), ROC curves for cut-points (14%), and other algorithms (8%).
  • Direct observation for validation primarily used live observation (55%) or recordings (42%).

Conclusions:

  • Machine learning methods are increasingly used for activity identification, while cut-point methods remain prevalent.
  • Activity intensity is the most common outcome, but analysis and outcomes vary by accelerometer wear location.
  • This review offers a comprehensive resource on accelerometer analysis and validation techniques for researchers.