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Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis.

Aodhán Hickey1, Silvia Del Din, Lynn Rochester

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

Physiological Measurement
|December 13, 2016
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Summary

A new gait segmentation algorithm accurately quantifies daily steps and walking bouts using a single wearable accelerometer. This wearable technology offers a valid approach for continuous gait analysis in real-world, uncontrolled environments.

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Gait Analysis

Background:

  • Wearable sensors are preferred over instrumented walkways for gait analysis in both clinical and free-living settings.
  • Existing research has limited validation of wearable algorithms for step and walking bout quantification in uncontrolled environments.
  • Variations in walking speed, terrain, and tasks can impact the accuracy of free-living gait data.

Purpose of the Study:

  • To develop and validate a gait segmentation algorithm for quantifying free-living step count and walking bouts.
  • To assess the accuracy of a wearable accelerometer (AX3, Axivity) for gait analysis in uncontrolled environments.
  • To compare gait data derived from a lower-back wearable accelerometer against a chest-mounted camera (gold standard).

Main Methods:

  • Ten healthy participants wore a lower-back accelerometer and a chest-mounted camera during 1-hour free-living activity sessions.
  • A novel gait segmentation algorithm was applied to accelerometer data to derive step counts and walking bouts.
  • Data from the wearable accelerometer were compared against the camera-based reference for accuracy and agreement.

Main Results:

  • The algorithm demonstrated excellent relative (rho ≥ 0.941) and absolute (ICC ≥ 0.975) agreement for step count, with no significant bias compared to the camera.
  • Walking bout identification showed excellent relative (rho ≥ 0.909) and absolute (ICC ≥ 0.941) agreement.
  • A significant bias was observed in walking bout identification, though overall agreement remained high.

Conclusions:

  • The developed gait segmentation algorithm is valid for quantifying step count and walking bouts using a single wearable accelerometer.
  • This approach enables pragmatic and continuous gait analysis in prolonged, uncontrolled free-living conditions.
  • Wearable accelerometers offer a reliable method for comprehensive gait outcome assessment outside of controlled laboratory settings.