The high prevalence of foot pain, and its relation to foot shape, indicates the need for an expert system to identify foot shape abnormalities. Yet, to date, no such expert system exists that examines the full 3D foot shape and produces an intuitive explanation of why a foot is abnormal. In this work, we present the first such expert system that satisfies those goals. The system is based on the concept of model-based outlier detection: a statistical shape model (SSM) is generated from 186 3D optical foot scans of healthy feet. This model acts as a knowledge base which is then parameterized by one’s demographic characteristics (e.g., age, weight, height, shoe size) through a multivariate regression. This regression introduces model flexibility as it allows the model to be fine tuned to a specific individual. This fine tuned model is then used as a baseline to which the individual’s 3D foot scan can be compared using standard statistical tests (e.g. t-tests). These statistical tests are performed at each vertex along the foot surface to identify the degree and location of shape outliers. Our expert system was validated on foot scans from patients with hallux valgus and abnormal foot arches. As expected, our results varied per patient, confirming that feet with the same clinical classification still show high shape variability. Additionally, the foot shape abnormalities identified by our system not only agreed with the expected location and severity of the tested foot deformities, but our analysis of the full 3D foot shape was able to completely characterize the extent of those abnormalities for the first time. These results show that the combination of statistical shape modelling, multivariate regression, and statistical testing is powerful enough to perform outlier detection for 3D foot shapes. The resulting insights provided by this system could prove useful in both shoe design and clinical diagnosis.

足部疼痛的高发率及其与足部形状的关系表明，需要一种专家系统来识别足部形状异常。但是，迄今为止，还没有这样的专家系统可以检查完整的3D足部形状并产生关于足部异常的直观解释。在这项工作中，我们提出了第一个满足这些目标的专家系统。该系统基于基于模型的离群值检测的概念：从186个健康脚的3D光学脚扫描中生成统计形状模型（SSM）。该模型充当知识库，然后通过多元回归由一个人的人口统计特征（例如，年龄，体重，身高，鞋码）进行参数化。这种回归引入了模型灵活性，因为它允许对特定个体微调模型。然后，将这种经过微调的模型用作基准，可以使用标准统计测试（例如t检验）将个人3D足部扫描与之进行比较。在沿脚表面的每个顶点执行这些统计测试，以识别形状异常值的程度和位置。我们的专家系统已在拇外翻和足弓异常患者的脚部扫描中得到验证。正如预期的那样，我们的结果因患者而异，证实具有相同临床分类的脚仍显示出较高的形状变异性。此外，我们的系统识别出的足部形状异常不仅与被测足部畸形的预期位置和严重程度相符，而且我们对完整3D足部形状的分析首次能够完全表征这些异常的程度。这些结果表明，统计形状建模，多元回归和统计测试的组合功能强大到足以执行3D足部形状的异常检测。该系统提供的最终见解可以证明对鞋的设计和临床诊断均有用。