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Related Experiment Video

Updated: May 14, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Real time gait pattern classification from chest worn accelerometry during a loaded road march.

Cynthia M Clements1, Mark J Buller, Alexander P Welles

  • 1U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760, USA. Cynthia.clements@us.army.mil

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study introduces a real-time gait analysis method using accelerometers to distinguish walking from running. The intuitive approach achieves high accuracy without individual calibration, suitable for wearable devices.

Area of Science:

  • Biomechanics
  • Wearable technology
  • Signal processing

Background:

  • Accelerometers in smartphones and wearables are increasingly used for activity recognition.
  • Current computationally intensive methods limit real-time gait analysis.
  • A need exists for intuitive, calibration-free gait analysis for ambulatory monitors.

Purpose of the Study:

  • To develop a real-time gait analysis approach using accelerometry.
  • To create an intuitive method requiring no individual calibration.
  • To enable adoption by ambulatory physiological monitors for real-time use.

Main Methods:

  • Collected chest-mounted tri-axial accelerometry data from 61 U.S. Army Ranger candidates during loaded road marches (walking and running).
  • Examined periodic movement patterns from vertical and longitudinal accelerometer axes over 4-second intervals.

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Related Experiment Videos

Last Updated: May 14, 2026

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

  • Applied Principal Component Analysis (PCA), inspired by "eigenfaces", to identify distinguishing features.

    • Main Results:

    • A single basis vector derived from 10% of the data distinguished walking and running patterns.
    • Achieved 95% classification accuracy for walking and 90% for running using the remaining data.
    • Movement features based on gridded frequency counts are suitable for body-worn microprocessors.

    Conclusions:

    • The developed PCA-based gait analysis is intuitive, requires no calibration, and is suitable for real-time application.
    • This method can be extended for complex gait analysis and integrated into ambulatory monitoring systems.
    • The approach is computationally efficient, making it ideal for wearable microprocessors.