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Algorithms to determine event timing during normal walking using kinematic data.

A Hreljac1, R N Marshall

  • 1Department of Kinesiology and Health Science, California State University, Sacramento, 6000 J Street, Sacramento, CA 95819-6073, USA. ahreljac@hhs4.hhs.csus.edu

Journal of Biomechanics
|May 16, 2000
PubMed
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New algorithms accurately predict heelstrike and toeoff times during walking using only kinematic data. This method offers improved resolution over visual inspection for gait analysis.

Area of Science:

  • Biomechanics
  • Gait Analysis
  • Motion Capture

Background:

  • Accurate identification of gait events like heelstrike and toeoff is crucial for clinical and research applications.
  • Traditional methods often rely on force plates or visual inspection, which can be limiting in terms of accessibility and resolution.

Purpose of the Study:

  • To develop and validate algorithms for predicting heelstrike and toeoff times using solely kinematic data.
  • To assess the accuracy and reliability of these algorithms compared to force platform data.

Main Methods:

  • Kinematic data from two subjects walking at various speeds were collected using a 60Hz system.
  • Algorithms were developed to predict key gait event timings from the kinematic data.
  • The predicted timings were compared against synchronized force platform recordings.

Related Experiment Videos

Main Results:

  • The average absolute error for predicting heelstrike was 4.7ms.
  • The average absolute error for predicting toeoff was 5.6ms.
  • True average errors were +1.2ms for both events, indicating minimal systematic bias.

Conclusions:

  • The proposed algorithms provide an easy, reliable method for determining walking event times from kinematic data.
  • This approach significantly improves temporal resolution compared to visual inspection of video.
  • The algorithms are compatible with various 2-D and 3-D kinematic data collection systems.