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Heel pad's hyperelastic properties and gait parameters reciprocal modelling by a Gaussian Mixture Model and Extreme Gradient Boosting framework

Title
Heel pad's hyperelastic properties and gait parameters reciprocal modelling by a Gaussian Mixture Model and Extreme Gradient Boosting framework
Authors
Quagliato L.; Kim S.; Hassan O.R.; Lee T.
Ewha Authors
이태용; Luca Quagliato
SCOPUS Author ID
이태용scopus; Luca Quagliatoscopus
Issue Date
2025
Journal Title
Biomedical Signal Processing and Control
ISSN
1746-8094JCR Link
Citation
Biomedical Signal Processing and Control vol. 107
Keywords
Extreme Gradient Boosting (xgb); Gait Analysis; Gaussian Mixture Model (gmm); Heel Pad Mechanical Properties; Indentation; Adaptive Boosting; Motion Capture; Rockwell Hardness; Gait Parameters; Gaussian Mixture Model; Gradient Boosting; Heel Pad Mechanical Property; Heel Pads; Hyper Elastic; Mechanical; Property; Gaussian Distribution; Adult; Article; Biomechanics; Extreme Gradient Boosting Framework; Female; Gait; Heel; Human; Machine Learning; Male; Soft Tissue; Target Variable; Walking Parameters
Publisher
Elsevier Ltd
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Gait analysis and heel pad mechanical properties have been largely studied by physicians and biomechanical engineers alike. However, only a few contributions deal with the intertwining relationship between these two essential aspects and no research seems to propose a modeling approach to quantitatively correlate them. To bridge this gap, indentation experiments on the heel pad and gait analysis through motion capture camera were carried out on a group composed of 40 male and female subjects in the 20′s to 50′s. To establish a robust correlation between these two sets of parameters, the Gaussian Mixture Model (GMM) features’ enhancement technique was employed and combined with the Extreme Gradient Boosting (XGB) regressor. The hyperelastic constants from models, together with the gait parameters, were employed as both features and target variables in the GMM-XGB architecture showing the ambivalence of the solution and deviations between 5% and 8% in most cases. The results show the strong reciprocal correlation between the individual's foot plantar soft tissue's mechanical response and the gait parameters and pave the way for further investigations in the field of biomechanics. © 2025 Elsevier B.V., All rights reserved.
DOI
10.1016/j.bspc.2025.107818
Appears in Collections:
ETC > ETC
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