A Parametric Modeling of Adult Body Shape in a Supported Seated Posture Including Effects of Age

Abstract

ABSTRACT Statistical body shape models (SBSM) provide compact, flexible representations of body shape that can be implemented in design software. However, few SBSMs have been created to represent adults in supported seated postures that are relevant for the design of seated environments, and none has incorporated the effects of age. This paper presents an SBSM based on surface laser-scan data from 155 U.S. adults. The data were processed to obtain homologous mesh structure and symmetric geometry, and the processed data were statistically analyzed using principal component analysis to obtain a compact representation of the data variance. Regression analysis was conducted to predict body size and shape from stature, body mass index, ratio of sitting height to stature, sex, and age. The resulting model allows rapid generation of realistic body models for applications, including product design, accommodation assessment, and safety system optimization. The model is publicly accessible at HumanShape.org. PRACTITIONER SUMMARY: This paper presents a statistical model that represents adult body shapes in a supported seated posture based on 3D anthropometric measurements. This model is the first whole-body parametric model known to incorporate age effects based on data extending beyond 65 years of age.