Validation of a parametric finite element human femur model.

OBJECTIVE: Finite element (FE) models with geometry and material properties that are parametric with subject descriptors, such as age and body shape/size, are being developed to incorporate population variability into crash simulations. However, the validation methods currently being used with these parametric models do not assess whether model predictions are reasonable in the space over which the model is intended to be used. This study presents a parametric model of the femur and applies a unique validation paradigm to this parametric femur model that characterizes whether model predictions reproduce experimentally observed trends. METHODS: FE models of male and female femurs with geometries that are parametric with age, femur length, and body mass index (BMI) were developed based on existing statistical models that predict femur geometry. These parametric FE femur models were validated by comparing responses from combined loading tests of femoral shafts to simulation results from FE models of the corresponding femoral shafts whose geometry was predicted using the associated age, femur length, and BMI. The effects of subject variables on model responses were also compared with trends in the experimental data set by fitting similarly parameterized statistical models to both the results of the experimental data and the corresponding FE model results and then comparing fitted model coefficients for the experimental and predicted data sets. RESULTS: The average error in impact force at experimental failure for the parametric models was 5%. The coefficients of a statistical model fit to simulation data were within one standard error of the coefficients of a similarly parameterized model of the experimental data except for the age parameter, likely because material properties used in simulations were not varied with specimen age. In simulations to explore the effects of femur length, BMI, and age on impact response, only BMI significantly affected response for both men and women, with increasing BMI producing higher impact forces. CONCLUSIONS: Impactor forces from simulations, on average, matched experimental values at the time of failure. In addition, the simulations were able to match the trends in the experimental data set.

Development, Evaluation, and Sensitivity Analysis of Parametric Finite Element Whole-Body Human Models in Side Impacts.

Occupant stature and body shape may have significant effects on injury risks in motor vehicle crashes, but the current finite element (FE) human body models (HBMs) only represent occupants with a few sizes and shapes. Our recent studies have demonstrated that, by using a mesh morphing method, parametric FE HBMs can be rapidly developed for representing a diverse population. However, the biofidelity of those models across a wide range of human attributes has not been established. Therefore, the objectives of this study are 1) to evaluate the accuracy of HBMs considering subject-specific geometry information, and 2) to apply the parametric HBMs in a sensitivity analysis for identifying the specific parameters affecting body responses in side impact conditions. Four side-impact tests with two male post-mortem human subjects (PMHSs) were selected to evaluate the accuracy of the geometry and impact responses of the morphed HBMs. For each PMHS test, three HBMs were simulated to compare with the test results: the original Total Human Model for Safety (THUMS) v4.01 (O-THUMS), a parametric THUMS (P-THUMS), and a subject-specific THUMS (S-THUMS). The P-THUMS geometry was predicted from only age, sex, stature, and BMI using our statistical geometry models of skeleton and body shape, while the S-THUMS geometry was based on each PMHS's CT data. The simulation results showed a preliminary trend that the correlations between the PTHUMS- predicted impact responses and the four PMHS tests (mean-CORA: 0.84, 0.78, 0.69, 0.70) were better than those between the O-THUMS and the normalized PMHS responses (mean-CORA: 0.74, 0.72, 0.55, 0.63), while they are similar to the correlations between S-THUMS and the PMHS tests (mean-CORA: 0.85, 0.85, 0.67, 0.72). The sensitivity analysis using the PTHUMS showed that, in side impact conditions, the HBM skeleton and body shape geometries as well as the body posture were more important in modeling the occupant impact responses than the bone and soft tissue material properties and the padding stiffness with the given parameter ranges. More investigations are needed to further support these findings.

Development and Validation of Statistical Models of Femur Geometry for Use with Parametric Finite Element Models.

Statistical models were developed that predict male and female femur geometry as functions of age, body mass index (BMI), and femur length as part of an effort to develop lower-extremity finite element models with geometries that are parametric with subject characteristics. The process for developing these models involved extracting femur geometry from clinical CT scans of 62 men and 36 women, fitting a template finite element femur mesh to the surface geometry of each patient, and then programmatically determining thickness at each nodal location. Principal component analysis was then performed on the thickness and geometry nodal coordinates, and linear regression models were developed to predict principal component scores as functions of age, BMI, and femur length. The average absolute errors in male and female external surface geometry model predictions were 4.57 and 4.23 mm, and the average absolute errors in male and female thickness model predictions were 1.67 and 1.74 mm. The average error in midshaft cortical bone areas between the predicted geometries and the patient geometries was 4.4%. The average error in cortical bone area between the predicted geometries and a validation set of cadaver femur geometries across 5 shaft locations was 2.9%.