Effects of obesity on occupant responses in frontal crashes: a simulation analysis using human body models.

The objective of this study is to investigate the effects of obesity on occupant responses in frontal crashes using whole-body human finite element (FE) models representing occupants with different obesity levels. In this study, the geometry of THUMS 4 midsize male model was varied using mesh morphing techniques with target geometries defined by statistical models of external body contour and exterior ribcage geometry. Models with different body mass indices (BMIs) were calibrated against cadaver test data under high-speed abdomen loading and frontal crash conditions. A parametric analysis was performed to investigate the effects of BMI on occupant injuries in frontal crashes based on the Taguchi method while controlling for several vehicle design parameters. Simulations of obese occupants predicted significantly higher risks of injuries to the thorax and lower extremities in frontal crashes compared with non-obese occupants, which is consistent with previous field data analyses. These higher injury risks are mainly due to the increased body mass and relatively poor belt fit caused by soft tissues for obese occupants. This study demonstrated the feasibility of using a parametric human FE model to investigate the obesity effects on occupant responses in frontal crashes.

A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index.

In this study, we developed a statistical rib cage geometry model accounting for variations by age, sex, stature and body mass index (BMI). Thorax CT scans were obtained from 89 subjects approximately evenly distributed among 8 age groups and both sexes. Threshold-based CT image segmentation was performed to extract the rib geometries, and a total of 464 landmarks on the left side of each subject׳s ribcage were collected to describe the size and shape of the rib cage as well as the cross-sectional geometry of each rib. Principal component analysis and multivariate regression analysis were conducted to predict rib cage geometry as a function of age, sex, stature, and BMI, all of which showed strong effects on rib cage geometry. Except for BMI, all parameters also showed significant effects on rib cross-sectional area using a linear mixed model. This statistical rib cage geometry model can serve as a geometric basis for developing a parametric human thorax finite element model for quantifying effects from different human attributes on thoracic injury risks.