为了快速获得足部形态相关的参数信息并量化足部变形程度,提出一种能够准确定位足部特征点并自动计算足部形态特征参数的算法.首先,使用UPOD激光扫描仪获取93名受试者的足部三维模型.然后,采用随机采样一致性算法和主成分分析方法对模型坐标系进行对齐,结合足部形态特征识别并定位特征点,实现对足部长度、角度和围度形态参数的自动测量.为评估测量结果的准确性、重复性和一致性,使用平均绝对误差(MAE)、平均相对误差(MAPE)、组间相关系数(ICC)和Bland-Altman图进行分析.结果显示,足长和足宽的MAE小于2 mm,跖围、跗围和兜跟围的MAE小于4 mm,MAPE小于2%;3次重复测量的ICC均大于0.99,Bland-Altman图显示95%以上的散点位于一致性界限内.结果证明,该算法可以实现站立姿态下足部模型的坐标系自动对齐、特征点准确定位以及参数的精确测量;测量精度满足临床测量需求,具有较高的测量精度和可靠性;测量结果可为足型分类、智能辅具适配与个性化辅具设计提供数据支持,具有重要的临床应用价值.

In order to quickly obtain foot parameters and quantify the degree of foot deformation, an algorithm that can accurately locate foot feature points and automatically calculate foot parameters is proposed. First, a total of 93 patients participate and their foot models are obtained using the UPOD laser scanner. Then, the random sampling consensus algorithm and principal component analysis are used to align the foot coordinate system. The algorithm utilizes foot features to identify and locate feature points, enabling the parameter calculation of length, angle, and girth. The accuracy, repeatability, and consistency of the measurements are evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), interclass correlation coefficient (ICC), and Bland-Altman plots. The MAE of foot length and width is less than 2 mm, and for ball girth, instep girth, and heel girth, it is less than 4 mm. The MAPE is less than 2%, and the ICCs for the three replicates exceed 0.99. More than 95% of the scattered points in the Bland-Altman plots are within the consistency boundary. The results show that the proposed algorithm can automatically align the coordinate system, accurately locate feature points, and accurately measure foot parameters in the standing posture. The measurement accuracy meets clinical needs with high accuracy and reliability. The findings provide valuable data support for foot classification, intelligent assistive device adaptation, and personalized assistive device design, showing important clinical application potential.