Product sizing with 3D anthropometry and k-medoids clustering. (October 2017) - Lacko, Daniël; Huysmans, Toon; Vleugels, Jochen; De Bruyne, Guido; Van Hulle, Marc M.; Sijbers, Jan; Verwulgen, Stijn
Abstract: Aside from anthropometric data tables, 3D shape models of the human body are becoming increasingly common and call for new product sizing methods based on 3D anthropometry. Though some shape model-based methods exist, most of them focus on mathematical clustering and do not discuss the usability of the clustering results for product design. In this paper, a new shape-model based clustering method for product sizing is presented that takes into account both shape information and usability for designers. The new method, called constrained k -medoids clustering, is applied on a shape model of 100 human heads. It is compared to a partitioning around medoid (PAM) clustering of anthropometric measurements of the same 100 heads (i.e., feature-based), as well as to PAM clustering of the shape model (i.e., shape based). Results show that both shape-based and constrained clustering perform better than feature-based clustering, with an average size-weighted variance (SWV) of 62 × 1 0 3 ± 16 × 1 0 3 and 66 × 1 0 3 ± 26 × 1 0 3 as compared to 72 × 1 0 3 ± 12 × 1 0 3, respectively. The average point-to-point distances in shape-based and constrained k -medoids were found to be similar to those of feature-based k -medoids, indicating that using 3D-anthropometry for product sizing will not have a negative impact on designer workload and/or a higher cost to implement more sizes. The results suggest that for head-based products, which require accurate shape and size fit, sizing systems should be created using either shape-based or constrained k -medoids, with the latter being slightly less accurate but more intuitive for further design and verification. Highlights: A new clustering method for 3D shape models is presented. The method is compared to k -medoids clustering of 1D head measurements and of 3D head shapes. It was found to be better than one-dimensional clustering for all metrics. A workflow is presented to incorporate this method in head-product sizing. Using shape models for product sizing will result in a better fit for near-body products.