A First Step Toward a Family of Morphed Human Body Models Enabling Prediction of Population Injury Outcomes.

The injury risk in a vehicle crash can depend on occupant specific factors. Virtual crash testing using finite element human body models (HBMs) to represent occupant variability can enable the development of vehicles with improved safety for all occupants. In this study, it was investigated how many HBMs of different sizes that are needed to represent a population crash outcome through a metamodel. Rib fracture risk was used as an example occupant injury outcome. Morphed HBMs representing variability in sex, height, and weight within defined population ranges were used to calculate population variability in rib fracture risk in a frontal and a side crash. Two regression methods, regularized linear regression with second-order terms and Gaussian process regression (GPR), were used to metamodel rib fracture risk due to occupant variability. By studying metamodel predictive performance as a function of training data, it was found that constructing GPR metamodels using 25 individuals of each sex appears sufficient to model the population rib fracture risk outcome in a general crash scenario. Further, by utilizing the known outcomes in the two crashes, an optimization method selected individuals representative for population outcomes across both crash scenarios. The optimization results showed that 5-7 individuals of each sex were sufficient to create predictive GPR metamodels. The optimization method can be extended for more crashes and vehicles, which can be used to identify a family of HBMs that are generally representative of population injury outcomes in future work.

Influences of human thorax variability on population rib fracture risk prediction using human body models.

Rib fractures remain a common injury for vehicle occupants in crashes. The risk of a human sustaining rib fractures from thorax loading is highly variable, potentially due to a variability in individual factors such as material properties and geometry of the ribs and ribcage. Human body models (HBMs) with a detailed ribcage can be used as occupant substitutes to aid in the prediction of rib injury risk at the tissue level in crash analysis. To improve this capability, model parametrization can be used to represent human variability in simulation studies. The aim of this study was to identify the variations in the physical properties of the human thorax that have the most influence on rib fracture risk for the population of vehicle occupants. A total of 15 different geometrical and material factors, sourced from published literature, were varied in a parametrized SAFER HBM. Parametric sensitivity analyses were conducted for two crash configurations, frontal and near-side impacts. The results show that variability in rib cortical bone thickness, rib cortical bone material properties, and rib cross-sectional width had the greatest influence on the risk for an occupant to sustain two or more fractured ribs in both impacts. Therefore, it is recommended that these three parameters be included in rib fracture risk analysis with HBMs for the population of vehicle occupants.

Evaluation of a diverse population of morphed human body models for prediction of vehicle occupant crash kinematics.

Morphing can be used to alter human body models (HBMs) to represent a diverse population of occupants in car crashes. The mid-sized male SAFER HBM v9 was parametrically morphed to match 22 Post Mortem Human Subjects, loaded in different configurations. Kinetics and kinematics were compared for the morphed and baseline HBMs. In frontal impacts, the morphed HBMs correlated closer with the kinematics of obese subjects, but lower to small females. In lateral impacts HBM responses were too stiff. This study outlines a necessary evaluation of all HBMs that should be morphed to represent the diverse population in vehicle safety evaluations.

Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models.

To evaluate vehicle occupant injury risk, finite element human body models (HBMs) can be used in vehicle crash simulations. HBMs can predict tissue loading levels, and the risk for fracture can be estimated based on a tissue-based risk curve. A probabilistic framework utilizing an age-adjusted rib strain-based risk function was proposed in 2012. However, the risk function was based on tests from only twelve human subjects. Further, the age adjustment was based on previous literature postulating a 5.1% decrease in failure strain for femur bone material per decade of aging. The primary aim of this study was to develop a new strain-based rib fracture risk function using material test data spanning a wide range of ages. A second aim was to update the probabilistic framework with the new risk function and compare the probabilistic risk predictions from HBM simulations to both previous HBM probabilistic risk predictions and to approximate real-world rib fracture outcomes. Tensile test data of human rib cortical bone from 58 individuals spanning 17-99 years of ages was used. Survival analysis with accelerated failure time was used to model the failure strain and age-dependent decrease for the tissue-based risk function. Stochastic HBM simulations with varied impact conditions and restraint system settings were performed and probabilistic rib fracture risks were calculated. In the resulting fracture risk function, sex was not a significant covariate-but a stronger age-dependent decrease than previously assumed for human rib cortical bone was evident, corresponding to a 12% decrease in failure strain per decade of aging. The main effect of this difference is a lowered risk prediction for younger individuals than that predicted in previous risk functions. For the stochastic analysis, the previous risk curve overestimated the approximate real-world rib fracture risk for 30-year-old occupants; the new risk function reduces the overestimation. Moreover, the new function can be used as a direct replacement of the previous one within the 2012 probabilistic framework.