A comprehensive methodological framework for 3D head anthropometric shape modeling of a small dataset
提出一个六步框架，用36名消防员的3D头部扫描数据，比较多种聚类和形状建模方法，发现表面映射、K-means聚类和三次样条最小二乘法能更准确预测头部形状，对设计贴合的头戴设备有帮助。
Efficient data analytics methods are essential to characterise occupation-specific anthropometric head shapes for developing well-fitted head-mounted devices. However, classifying and modelling 3D head shapes for small population groups remains challenging due to limited data and systematic approaches. This study proposes a streamlined six-step framework using 3D head scans from 36 firefighters (18 males, 18 females). We evaluated K-means and K-medoids clustering and four shape modelling methods—NURBS, NURBS least squares (LS), Cubic Spline, and Cubic Spline LS—and validated the predicted head shape against NIOSH, ANSUR II, CAESAR, and US Army databases. Results showed K-means outperformed K-medoids (28% lower distances). Surface mapping-based clustering was 35% more accurate than PCA-based clustering. Cubic Spline LS achieved the lowest mean squared error (0.70 cm2) and fastest computation (0.14 s), performing better than NURBS LS (7.19 cm2 and 1.87 s). Overall, surface mapping, K-means clustering, and Cubic Spline LS methods provided more accurate head shapes for our studypopulation groups.