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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Exploring the ActiLife(®) filtration algorithm: converting raw acceleration data to counts.

D Peach1, J Van Hoomissen, H L Callender

  • 1Department of Mathematics, Bates College, Lewiston, ME 04240, USA.

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|November 13, 2014
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Summary

Portable accelerometers are common in health studies, but their accuracy is questioned. This study reveals biases in ActiLife software

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Area of Science:

  • Physiology and public health
  • Biomedical engineering
  • Wearable technology

Background:

  • Portable accelerometers are widely used in physiology and public health research to objectively measure physical activity.
  • The accuracy of these devices, particularly the software used to process their data, is crucial for reliable study outcomes.
  • ActiGraph accelerometers and ActiLife software are commonly employed, necessitating an examination of their data processing methods.

Purpose of the Study:

  • To identify and analyze biases in ActiLife software's processing of ActiGraph accelerometer data.
  • To investigate the impact of the proprietary filtration algorithm on activity count reporting.
  • To develop novel methods for analyzing software filter effects on raw acceleration data.

Main Methods:

  • Analysis of the two-stage proprietary filtration algorithm within ActiLife software.
  • Examination of data conversion from raw 30 Hz acceleration signals to compressed 1 Hz signals.
  • Development of new methods to analyze filter actions in the frequency domain.

Main Results:

  • Identification of specific biases introduced by the ActiLife software's filtration algorithm.
  • Quantification of how the proprietary filter affects the reporting of activity counts.
  • Demonstration of novel frequency-domain analysis techniques revealing software filter behavior.

Conclusions:

  • The ActiLife software introduces biases in physical activity data derived from ActiGraph accelerometers.
  • Understanding these biases is essential for accurate interpretation of research findings in physiology and public health.
  • Novel frequency-domain analysis methods provide new tools for evaluating accelerometer data processing algorithms.