Using Three-Dimensional (3D) Anthropometric Data in Design

Abstract

With the emergence of 3D scanning technology, it is now possible to generate a large database of 3D models of humans from different demographic backgrounds. Unfortunately, one major drawback is that there is no clear link between 3D anthropometric data and engineering design. Sizing is regarded as an effective way to design properly fitted products. This chapter aims to show the usefulness of 3D anthropometry for the development of product sizing systems, taking helmet design using 3D head data as a case study. Current 3D scanning technology, the representative large-scale 3D anthropometric surveys, the diverse applications of 3D anthropometry in ergonomic product design, and the historical development of military helmet sizing systems were reviewed. Emphasis was then put on the proposed sizing method for helmet design by combining a multi-resolution representation of 3D anthropometric data and k-means clustering on the decomposed data. By using a novel block-based distance, each 3D head was transformed into a multi-dimensional vector. Clustering validation was implemented by using two measures, i.e., size-weighted variances and Clustering Validity Indices (CVI). The results show that the proposed block distance-based descriptor is superior to traditional sizing dimensions. Comparative studies were conducted as well to investigate the robustness of the proposed method. The proposed method provides a systematic method for properly grouping 3D anthropometric samples into clusters according to their 3D shape.