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

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Luca Quagliato , Sewon Kim , Olamide Robiat Hassan , Taeyong Lee
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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.
基于高斯混合模型和极端梯度增强框架的鞋垫超弹性特性及步态参数互反建模
步态分析和脚垫力学性能已经被医生和生物力学工程师大量研究。然而,只有少数贡献处理这两个基本方面之间的交织关系,似乎没有研究提出一个建模方法来定量地关联它们。为了弥补这一空白,我们对40名20 ~ 50岁的男性和女性受试者进行了足跟垫压痕实验和动作捕捉相机步态分析。为了建立这两组参数之间的鲁棒相关性,采用高斯混合模型(GMM)特征增强技术并结合极端梯度增强(XGB)回归器。在GMM-XGB结构中,模型的超弹性常数和步态参数同时作为特征和目标变量,显示了解决方案的矛盾性,大多数情况下偏差在5%到8%之间。结果表明,个体足跖软组织力学响应与步态参数之间存在较强的负相关关系,为生物力学领域的进一步研究奠定了基础。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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