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Related Experiment Video

Updated: May 10, 2026

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

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A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

Andre Andrade1, Marcelo Costa, Leopoldo Paolucci

  • 1a UFMG , Belo Horizonte, MG , Brazil.

Computer Methods in Biomechanics and Biomedical Engineering
|June 18, 2013
PubMed
Summary

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A new artificial neural network algorithm, multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO), effectively classifies dynamic gait patterns. This method identifies key features for gender-specific gait analysis, improving data mining with neural networks.

Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Dynamic gait pattern classification is crucial for understanding human movement.
  • Artificial neural networks offer potential for complex pattern recognition in biomechanics.
  • Existing methods may require extensive feature engineering for gait analysis.

Purpose of the Study:

  • To introduce a novel training algorithm, multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO), for dynamic gait pattern classification.
  • To apply MOBJ-LASSO to gender-specific gait classification using ground reaction force data.
  • To evaluate the performance and feature selection capabilities of MOBJ-LASSO.

Main Methods:

  • Utilized artificial neural networks for gait pattern classification.
Keywords:
gender classificationground reaction forcemulti-objectiveneural networks

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  • Employed 20 characteristics from three components of ground reaction force as input features.
  • Implemented the multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) algorithm.
  • Compared MOBJ-LASSO performance against a standard multi-objective algorithm (MOBJ).

    • Main Results:

    • MOBJ-LASSO achieved a classification performance of 97.4%, closely followed by MOBJ at 97.1%.
    • MOBJ-LASSO demonstrated superior results by automatically eliminating irrelevant inputs and selecting optimal neural network parameters.
    • The algorithm identified the first and second peaks of vertical force and the antero-posterior force peak as key gait classification variables.

    Conclusions:

    • MOBJ-LASSO is an effective tool for data mining using neural networks in biomechanical applications.
    • The algorithm's ability to perform automatic feature selection enhances its utility for dynamic gait analysis.
    • MOBJ-LASSO provides a robust method for gender-specific gait pattern classification.