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

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.

Research question: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?

Methods: We employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets). We selected the EMG timeseries of 6 muscles (Biceps Femoris, Gluteus Maximus, Rectus Femoris, Medial Gastrocnemius, Soleus, and Tibialis Anterior) and joint moment timeseries in the lower limbs (ankle, knee, and hip).

Results: The ANN models demonstrated high predictive accuracy for joint moments (R-value: 0.97, p < 0.0001) and EMG signals (R-value: 0.95, p < 0.0001) across various gait activities, including walking and running. This underscores the potential of using GRF data to understand complex biomechanical interactions, offering significant insights into human locomotion.

Significance: The significance of this research extends broadly, touching upon the development of portable devices, assistive technologies, and personalized rehabilitation programs. Our findings have the potential to broaden the accessibility of advanced biomechanical analysis with implications spanning disciplines such as sports science, rehabilitation, and the advancement of innovative assistive devices and exoskeletons.