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Developing parametric human models representing various vulnerable populations in motor vehicle crashes

In: Mimics Innovation Awards--Winners 2015; http://biomedical.materialise.com/mimics-innovation-awards-winners-2015

Authors: Jingwen Hu, Katelyn F. Klein, Zhigang Li, Jonathan D. Rupp, Matthew P. Reed

Children, small female, elderly, and obese occupants are vulnerable populations and may sustain increased risk of death and serious injury in motor-vehicle crashes compared with mid-size young male occupants. Unfortunately, current injury assessment tools do not account for immature and growing body structures for children, nor the body shape and composition changes that are thought make female/aging/obese adults more vulnerable. The greatest opportunity to broaden crash protection to encompass all vehicle occupants lies in improved, parametric human models that can represent a wide range of human attributes. In this study, a novel approach to develop such models is proposed. The method includes 1) developing statistical skeleton and human body surface contour models based on medical images and body scan data using Mimics and a series of statistical methods, and 2) linking the statistical geometry model to a baseline human finite element (FE) model through an automated mesh morphing algorithm using radial basis functions, so that the FE model can represent population variability. Examples of using this approach to develop parametric pediatric head model, adult thorax and lower extremity models, and whole-body human models representing various populations were represented. The method proposed in this study enables future safety design optimizations targeting at various vulnerable populations that cannot be considered with current injury assessment tools.

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