A comprehensive methodological framework for 3D head anthropometric shape modeling of a small dataset

Ergonomics. 2025 Jun 18:1-16. doi: 10.1080/00140139.2025.2518306. Online ahead of print.

Abstract

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.

Keywords: NURBS; Small sample; clustering; cubic spline; head 3D shape modelling; surface mapping.

Plain language summary

The proposed surface mapping-based K-means clustering and Cubic Spline LS method were efficient in predicting head shapes for specialised occupational groups. These techniques can assist practitioners for characterising and establishing occupation-specific anthropometric head shapes in order to develop well-fitted head-mounted devices (e.g., helmets, virtual reality headsets).