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Updated: May 16, 2026

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
Published on: January 15, 2016
Development of a symmetry index using discrete variables
Sandro Nigg1, Jordyn Vienneau, Christian Maurer
1Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada. sandro@kin.ucalgary.ca
This study introduces a new method to measure lower extremity movement symmetry during running using stance phase data. The approach quantizes symmetry across different planes of motion, offering a comprehensive analysis.
Area of Science:
- Biomechanics
- Sports Science
- Human Movement Analysis
Background:
- Quantifying lower extremity movement symmetry is crucial for understanding running mechanics and identifying potential injury risks.
- Existing methods may not fully capture the dynamic nature of gait symmetry during the stance phase.
Purpose of the Study:
- To introduce and evaluate a novel methodology for quantifying lower extremity movement symmetry during over-ground running.
- To analyze symmetry across sagittal, transverse, and frontal planes, as well as a global index.
Main Methods:
- Seventeen subjects performed over-ground running trials over a force platform.
- Kinetic and kinematic data were collected, with 12 key variables selected for symmetry calculation.
- A formula was developed using the integral of the absolute difference between left and right leg stance phases.
Main Results:
- The new methodology provides distinct indices for sagittal, transverse, and frontal planes, alongside a global symmetry index.
- This allows for the identification of individuals with plane-specific symmetry or asymmetry.
- The method accounts for the entire stance phase and potential time lags between legs.
Conclusions:
- The developed methodology offers a comprehensive approach to quantifying lower extremity movement symmetry during running.
- It enables detailed analysis of gait patterns across multiple planes of motion.
- Future research should explore larger populations and incorporate flight phase analysis for a more complete understanding.
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