Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, USA.
Ann and H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, USA.
J Biomech. 2021 Jun 9;122:110465. doi: 10.1016/j.jbiomech.2021.110465. Epub 2021 Apr 25.
A detailed understanding of foot morphology can enable the design of more comfortable and better fitting footwear. However, foot morphology varies widely within the population, and changes dynamically as the foot is loaded during stance. This study presents a parametric statistical shape model from 4D foot scans to capture both the inter- and intra-individual variability in foot morphology. Thirty subjects walked on a treadmill while 4D scans of their right foot were taken at 90 frames-per second during stance phase. Each subject's height, weight, foot length, foot width, arch length, and sex were also recorded. The 4D scans were all registered to a common high-quality foot scan, and a principal component analysis was done on all processed 4D scans. Elastic-net linear regression models were built to predict the principal component scores, which were then inverse transformed into 4D scans. The best performing model was selected with leave-one-out cross-validation. The chosen model predicts foot morphology across stance phase with a root-mean-square error of 5.2 ± 2.0 mm and a mean Hausdorff distance of 25.5 ± 13.4 mm. This study shows that statistical shape modeling can be used to predict dynamic changes in foot morphology across the population. The model can be used to investigate and improve foot-footwear interaction, allowing for better fitting and more comfortable footwear.
详细了解足部形态可以设计出更舒适、更合脚的鞋类。然而，足部形态在人群中差异很大，并且在站立时足部受力时会动态变化。本研究提出了一种基于 4D 足部扫描的参数化统计形状模型，以捕捉足部形态的个体内和个体间变异性。30 名受试者在跑步机上行走，同时在站立阶段以每秒 90 帧的速度对其右脚进行 4D 扫描。还记录了每位受试者的身高、体重、足长、足宽、足弓长和性别。将 4D 扫描全部注册到一个高质量的足部扫描上，并对所有处理后的 4D 扫描进行主成分分析。建立弹性网络线性回归模型来预测主成分得分，然后将其反变换回 4D 扫描。通过留一法交叉验证选择表现最佳的模型。所选模型可以预测整个站立阶段的足部形态，其均方根误差为 5.2 ± 2.0 毫米，平均 Hausdorff 距离为 25.5 ± 13.4 毫米。本研究表明，统计形状建模可用于预测人群中足部形态的动态变化。该模型可用于研究和改善足鞋相互作用，从而设计出更合脚、更舒适的鞋类。
