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Related Experiment Video

Updated: Jul 12, 2026

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

Two simple methods for determining gait events during treadmill and overground walking using kinematic data.

J A Zeni1, J G Richards, J S Higginson

  • 1Department of Mechanical Engineering, Biomechanics and Movement Science Program, University of Delaware, Newark, DE, United States. jzenijr@udel.edu

Gait & Posture
|August 29, 2007
PubMed
Summary

Two new algorithms accurately detect gait events like heel strike and toe-off using only kinematic data. This aids researchers in analyzing walking patterns for both treadmill and overground conditions.

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Area of Science:

  • Biomechanics
  • Movement Science
  • Computational Kinematics

Background:

  • Gait event determination (heel strike, toe-off) is crucial for analyzing gait cycles.
  • Current methods may require specialized equipment or complex setups.
  • Accurate gait event detection is essential for clinical and research applications.

Purpose of the Study:

  • To present and validate two novel algorithms for automatic gait event detection.
  • To determine gait event times using solely kinematic data for treadmill and overground walking.
  • To compare the accuracy of kinematic-based algorithms against a gold standard (vertical ground reaction force).

Main Methods:

  • Collected kinematic data from healthy, multiple sclerosis, and stroke populations during treadmill walking.

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  • Collected kinematic data from healthy subjects during overground walking trials over force plates.
  • Developed and applied two computational algorithms to identify heel strike and toe-off events from kinematic data.
  • Validated algorithm performance by comparing detected events to those identified using vertical ground reaction force.

    • Main Results:

    • The algorithms achieved high accuracy, detecting 94% of treadmill events within one frame (0.0167s) for healthy subjects.
    • For impaired populations, 89% of treadmill gait events were detected within two frames (0.0334s).
    • Overground walking trials showed 98% of events detected within two frames compared to the gold standard.

    Conclusions:

    • Kinematic-based algorithms provide an accurate and accessible method for automatic gait event detection.
    • These algorithms can significantly aid researchers in analyzing both treadmill and overground walking data.
    • The developed techniques offer a valuable tool for gait analysis across diverse populations and conditions.