TY - JOUR
T1 - Heel pad's hyperelastic properties and gait parameters reciprocal modelling by a Gaussian Mixture Model and Extreme Gradient Boosting framework
AU - Quagliato, Luca
AU - Kim, Sewon
AU - Hassan, Olamide Robiat
AU - Lee, Taeyong
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/9
Y1 - 2025/9
N2 - 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.
AB - 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.
KW - Extreme Gradient Boosting (XGB)
KW - Gait analysis
KW - Gaussian Mixture Model (GMM)
KW - Heel pad mechanical properties
KW - Indentation
UR - http://www.scopus.com/inward/record.url?scp=105001495855&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2025.107818
DO - 10.1016/j.bspc.2025.107818
M3 - Article
AN - SCOPUS:105001495855
SN - 1746-8094
VL - 107
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 107818
ER -