Our HumanShape™ online body shape models are based on accurate three-dimensional (3D) anthropometric measurements of people with a wide range of body characteristics, ages, and genders. Various state-of-the-art scanning systems, including VITUS XXL, 3D Systems Sens, were used to measure the 3D body contours and typical anthropometric dimensions. 3D digitizer, FaroArm, was also used to collect anatomical body landmarks, and these landmarks data were used to estimate kinematic joint locations. All the data were carefully collected and validated by our experienced research staff.

All the scan data were standardized by fitting a HumanShape™ template model. This template fitting process does not only facilitates analyzing the scan but also ensures anatomical homology across the scans. Our HumanShape™ model with 14k that is efficient to represent body shape was used as a template model. Vertex density in each body segment were optimized to express the details of segment geometry in the most efficient way. A two-level fitting method proposed in our previous study was used to fit the template (Park and Reed, 2015). Briefly, this method first morphs a template model based on the landmarks of the target scan using a radial basis function (RBF) technique. The initial morphing step ensures specific template vertices are located at corresponding landmark locations across all target scans. In a second step, an implicit surface fitting technique fits the morphed template to a target scan and captures geometric detail.

The statistcal body shape model presented here is broadly applicable and can be readily extended to a wide range of applications. We have applied the model to fitting low-resolution data from Microsoft Kinect, demonstrating the rapid generation of avatars from Kinect data along with accurate prediction of standard anthropometry (Park and Reed 2014, Park et al. 2015). We expect that the model will be useful for generating anthropometric specifications for human surrogates used for safety applications, such as crash test dummies and finite-element models used for crash simulation. For these applications, the desired body size is usually specified using only a few variables, typically stature, body weight, and erect sitting height. We expect the model to be integrated into commercial ergonomics software, such as the Jack human modeling software, in the same manner as we have demonstrated using adult SBSMs (Reed et al. 2014). We also anticipate that this or similar SBSMs may be useful for stochastic investigation of the performance of protective equipment, which currently is performed using a small number of body forms. Likewise, clothing fitting simulations might be enhanced by the availability of a well-validated parametric model.