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Related Experiment Video

Updated: May 26, 2026

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

A kernel-regression framework for gait signal enhancement and inter-joint coordination: a single-subject

Dominik Krumm1, Dardi Shehu2, Dana Uhlig3

  • 1Sports Equipment and Technology, Chemnitz University of Technology, Chemnitz, Germany.

Computer Methods in Biomechanics and Biomedical Engineering
|May 25, 2026
PubMed
Summary

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This study introduces a kernel-regression framework to analyze lower-limb joint movement during walking, hiking, and running. The method enhances gait signal analysis and reveals predictable coordination patterns between joints.

Area of Science:

  • Biomechanics
  • Human Movement Science
  • Computational Neuroscience

Background:

  • Accurate characterization of lower-limb joint kinematics and inter-joint coordination is crucial for understanding human gait and its neuromechanical control.
  • Existing methods may lack the precision needed for detailed gait analysis and coordination network construction.

Purpose of the Study:

  • To introduce and evaluate a non-parametric kernel-regression framework for gait signal enhancement and coordination analysis.
  • To assess the performance of Nadaraya-Watson estimator and Kernel Ridge Regression for denoising, forecasting, and network analysis of lower-limb kinematics.

Main Methods:

  • Applied two kernel-based estimators (Nadaraya-Watson, Kernel Ridge Regression) to publicly available lower-limb kinematic data from a single healthy adult.
Keywords:
CoordinationKernel regressionforecastinggaitmethodsreconstruction

Related Experiment Videos

Last Updated: May 26, 2026

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running
06:35

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

Published on: September 14, 2017

  • Conducted validation through denoising, intra-cycle interpolation, long-range forecasting of gait cycles/amplitudes, and predictive mapping of inter-joint relationships.
  • Quantified performance using mean squared error and network-derived node strengths across different locomotor modes (walking, hiking, running).

    • Main Results:

    • Nadaraya-Watson estimator excelled in local fidelity and high-dimensional forecasting.
    • Kernel Ridge Regression yielded smoother reconstructions and more stable inter-joint predictability networks.
    • Analysis showed lower errors for contralateral joints and higher coordination strengths for proximal compared to distal joints, illustrating interpretable inter-joint predictive structures.

    Conclusions:

    • The proposed kernel-regression framework serves as an interpretable tool for enhancing gait signals and deriving data-driven coordination metrics.
    • The framework successfully characterized inter-joint predictive structure in a single-subject case study.
    • Further validation in multi-subject and clinical studies is necessary for broader biomechanical and clinical generalization.