Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks

Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2–5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artifi...

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Bibliographic Details
Published in:Applied sciences 2024-06, Vol.14 (12), p.5210
Main Authors: Mohammadzadeh Gonabadi, Arash, Fallahtafti, Farahnaz, Antonellis, Prokopios, Pipinos, Iraklis I, Myers, Sara A
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
artificial neural networks
Biomechanics
Costs
Data analysis
Design
Efficiency
Fitness equipment
Forecasts and trends
Gait
ground reaction forces
Hypotheses
joint moments
Machine learning
metabolic cost
Metabolism
Motion capture
Neural networks
Performance evaluation
Sports sciences
Time series
Walking
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Summary:Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2–5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series. Data from 20 participants collected over 270 walking trials, including the GRF and joint moments, formed a detailed dataset. Two ANN models were crafted, net[sub.GRF] for the GRF and net[sub.Moment] for joint moments, and both underwent training, validation, and testing to validate their predictive accuracy for metabolic cost. Net[sub.GRF] (six hidden layers, two input delays) showed significant correlations: 0.963 (training), 0.927 (validation), 0.883 (testing), p < 0.001. Net[sub.Moment] (three hidden layers, one input delay) had correlations of 0.920 (training), 0.956 (validation), 0.874 (testing), p < 0.001. The models’ low mean squared errors reflect their precision. Using Partial Dependence Plots, we demonstrated how gait cycle phases affect metabolic cost predictions, pinpointing key phases. Our findings show that the GRF and joint moments data can accurately predict metabolic costs via ANN models, with net[sub.GRF] being notably consistent. This emphasizes ANNs’ role in biomechanics as a crucial method for estimating metabolic costs, impacting sports science, rehabilitation, assistive technology development, and fostering personalized advancements.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14125210