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A multi-resolution investigation for postural transition detection and quantification using a single wearable.

Aodhán Hickey1, Brook Galna1, John C Mathers2

  • 1Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK; Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK.

Gait & Posture
|August 12, 2016
PubMed
Summary

This study recommends optimal wavelet and scale combinations for accurately detecting postural transitions (PTs) using wearables. While detection is accurate, wearable-based duration estimates for PTs require caution.

Keywords:
AccelerometerDiscrete wavelet transformPostural transitionWaveletWearables

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Signal Processing

Background:

  • Wavelet analysis is common for identifying postural transitions (PTs) from wearable sensor data.
  • Previous research lacks clear rationale for selecting wavelet and scale parameters in discrete wavelet transforms.

Purpose of the Study:

  • To examine various wavelet and scale combinations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) postural transitions.
  • To provide best practice recommendations for wavelet-based analysis of PTs.

Main Methods:

  • 39 young and 37 older adults performed SiSt and StSi transitions wearing a lower-back tri-axial accelerometer.
  • Wavelet and scale approximations were tested for transition detection and duration calculation.
  • A 2D video system served as the reference measure for comparison.

Main Results:

  • Excellent detection accuracy for SiSt (87-97%) and StSi (82-86%) transitions was achieved with specific wavelet and scale combinations.
  • Wearable-derived PT durations exhibited significant bias and poor agreement with video reference.
  • No significant differences were found between chair types or age groups.

Conclusions:

  • Optimizing wavelet and scale combinations can improve PT detection in clinical and free-living settings.
  • An upper threshold of 5th scale approximation is suggested for detecting multiple PT types.
  • Estimating the duration of PTs using wearables necessitates careful consideration due to observed biases.