There are many hypotheses about the effects of human characteristics on injuries, and they can be assessed more accurately through the use of a parametric human finite element (FE) model. This model can be morphed automatically into other models to represent a diverse population. Dr. Jingwen Hu and his team at the University of Michigan Transportation Research Institute, USA have managed to develop this very same parametric model, and have given it the ability to predict injuries and represent different human anatomies. Dr. Hu won the first prize for the “Best Article Award: North & South America” of the Mimics Innovation Awards in 2015.
Limitations of standard crash dummies
Dr. Hu explains that most car safety features are tested using a dummy that comes in three sizes: large adult male, mid-sized adult male and small female. This selection is extremely limited, meaning that the current injury assessment tests do not represent an accurate result, as they fail to take into account the effect of the crash impact on different types of bodies with different bone structures. An elderly passenger will have a much more fragile morphological and physiological body structure, and obese passengers are at risk due to their increased mass and body shape, as the safety belt is not designed to fit them properly. Children are also at risk due to the immense anthropometric and biomechanical differences between their bodies and that of an adult. Yet the child-sized dummy used to test car safety is essentially a scaled-down version of an adult dummy, which doesn’t acknowledge their growing bone structure and different body shape.
Developing parametric models
The researchers started out by developing statistical skeleton and human body surface contour models. They used Materialise Mimics to segment the medical images to get the geometry out. Then, in order to make the finite element model represent population variability, it was necessary to link the statistical geometry model to a baseline human finite element model, using an automated mesh morphing algorithm with radial basis functions. Dr. Hu used his method to develop a parametric pediatric head model, adult thorax and lower extremity models, and whole-body human models representing various populations.
The study successfully demonstrated how human parameters affected the way the human body reacted to impacts, and that the development of the proposed parametric models could have a significant impact on the optimization of future car safety design. That optimized safety would in turn have the capacity to better protect the segments of the population which are more vulnerable to injury.
About the author
Dr. Jingwen Hu is an associate research scientist at the University of Michigan Transportation Research Institute. His research focuses on the injury biomechanics in motor-vehicle crashes in particular, and how the safety features in cars could be adapted to suit different segments of the population that are more vulnerable to these injuries. His approach encompasses children, elders, pedestrians, pregnant females and obese occupants, and he aims to improve upon the safety systems already present in cars by developing parametric computational human models that represent all these diverse members of the population. In 2015, Dr. Hu won a Mimics Innovation Award for his research on “Developing Parametric Human Models Representing Various Vulnerable Populations in Motor Vehicle Crashes”.