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A simple computational method to estimate stance velocity in running.

Geoffrey T Burns1,2, Ronald F Zernicke1,2,3,4

  • 1School of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, MI 48109-2013, USA.

The Journal of Experimental Biology
|August 24, 2021
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Summary

A new method accurately estimates running stance velocity using contact and flight times, improving kinetic analyses. This approach enhances understanding of leg stiffness and impact angles in runners.

Keywords:
Horizontal forceImpact angleLocomotionSLIPSpring–massStiffness

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Area of Science:

  • Biomechanics
  • Sports Science
  • Human Movement Analysis

Background:

  • Traditional running analyses approximate stance velocity using average stride cycle velocity, leading to overestimations and biased results.
  • Accurate stance velocity is critical for kinetic spring-mass models, impacting calculations of impact angle and leg stiffness.
  • Existing methods lack precision, particularly when kinematic recordings are unavailable for precise stance velocity measurement.

Purpose of the Study:

  • To introduce and validate a novel method for approximating runner's stance velocity.
  • To assess the accuracy of this new method in estimating key biomechanical parameters like impact angle and leg stiffness.
  • To evaluate the method's robustness and applicability in both simulated and real-world running scenarios.

Main Methods:

  • Developed a new method to approximate stance velocity using contact time, flight time, average speed, leg length, and mass.
  • Validated the method with simulated spring-mass systems across various running speeds (3.5-5.5 m/s).
  • Tested the method's robustness on human runners at different speeds (2.5, 3.5, 4.5 m/s), comparing results with traditional estimates.

Main Results:

  • The new method accurately estimated stance velocity in simulations (r>0.99) and improved impact angle and leg stiffness estimations.
  • Human runner data showed steeper impact angles and higher leg stiffnesses compared to traditional methods, aligning with simulation findings.
  • Peak horizontal ground reaction force estimation was accurate (r=0.82) but showed speed-dependent bias; a corrective algorithm improved but did not eliminate variation.

Conclusions:

  • The novel method provides a more accurate approximation of stance velocity, enhancing kinetic spring-mass analyses of running.
  • This approach improves the estimation of crucial biomechanical variables like impact angle and leg stiffness.
  • The method offers a valuable tool for accurate field-based running analyses, especially when detailed kinematic data is limited.