Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.

Journal: Gait & posture
PMID: 39842155

Abstract

BACKGROUND: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems.

Authors

  • Arash Mohammadzadeh Gonabadi
    Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, 6160 University Drive South, Omaha, NE 68182, USA.
  • Farahnaz Fallahtafti
    Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA. Electronic address: ffallahtafti@unomaha.edu.
  • Iraklis I Pipinos
    Department of Surgery and Research Service, Veterans Affairs Nebraska-Western Iowa Medical Center, Omaha, NE 68105, USA.
  • Sara A Myers
    Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, 6160 University Drive South, Omaha, NE 68182, USA.

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