Analysis of segmented elderly women’s lower bodies using 3D-LOOK scan data and virtual representation

Abstract

To address the clothing needs of an aging society, this study developed a scale using three-dimensional (3D) scan data that determine elderly women’s lower body shapes to improve the way garments fit the elderly. Body type elements that play an important role in garment fit were identified and five body type elements were selected for use in this study. A stepwise discriminant analysis using 176 dimensions was performed to extract parameters reflecting body shape features, resulting in 37 parameters. A scale for determining body shapes was developed using the discriminant function equation. This study differs from existing studies on body shape classification in that we determined the diverse body shape features of individuals by extracting the lower body type elements related to garment fit. This study demonstrated an organic relationship among lower body types, where a greater posterior pelvic tilt was associated with a protruding lower abdomen, flat buttocks, and an o-type frontal leg shape. The significance of this study lies in the extraction of 3D parameters that reflect the body shape features of elderly women. Such 3D parameter data can be used to create personal virtual bodies in online shopping malls in the future.