Age targeted human body models indicate increased thoracic injury risk with aging.

OBJECTIVE: The objective of this study is to generate age targeted versions of the male and female Global Human Body Models Consortium (GHBMC) occupant human body models (HBMs), to validate each in frontal impacts, and to assess rib fracture probability of each. METHODS: Six age targeted models were developed based on the GHBMC average male and small female occupant models (M50-O v6.0 and F05-O v6.0, respectively). All age targeted models were modified to represent population means for height, weight, shape, and relevant material properties. The thin plate spline method was used to morph models, and material properties were modified using available literature. Validation focused on chest response. Models were evaluated in a rigid body frontal chest impact at 6.7 m/s. Furthermore, the male and female age targeted models were evaluated against published data from 40 km/hr and 30 km/hr frontal sled tests respectively. RESULTS: Chest deflections and landmark kinematics reasonably matched the respective corridors in the M50-O and F05-O aged models. Regional probability of rib fracture was assessed using probabilistic methods based on cortex strain. Increasing rib fracture with age was observed in both impacts for both sexes. For the rigid chest impact, the M50-O 70YO resulted in 10 ribs exceeding 50% probability of fracture whereas the younger ages reported 4 to 6 ribs exceeding the same probability. In the same simulation, the F05-O 70YO resulted in 8 regions exceeding 50% probability of rib fracture as opposed to 3 and 0 such regions at the youngest ages. Sled simulation demonstrated similar trends. The 70YO age adjusted models best aligned with the reported extent of fractures from the referenced PMHS studies, which tend to be composed of subjects of advanced age. CONCLUSIONS: Age targeted HBMs demonstrated increased fracture probability with age when subjected to equivalent impacts. Gross model kinematics approximate PMHS data but showed little difference between targeted age models. The findings indicate that while gross kinematics are unaffected by age-targeting models, such models can capture trends of increased thoracic injury risk observed in experimental and field studies, and further suggest their potential use to target interventions for vulnerable driving populations, such as older adults.

Hierarchical Validation Prevents Over-Fitting of the Neck Material Model for an Anthropomorphic Test Device Used in Underbody Blast Scenarios.

Injury due to underbody loading is increasingly relevant to the safety of the modern warfighter. To accurately evaluate injury risk in this loading modality, a biofidelic anthropomorphic test device (e.g., dummy) is required. Finite element model counterparts to the physical dummies are also useful tools in the evaluation of injury risk, but require validated constitutive material models used in the dummy. However, material model fitting can result in models that are over-fit: they match well with the data they were trained on, but do not extrapolate well to new loading scenarios. In this study, we used a hierarchical approach. Material models created from coupon-level tests were evaluated at the component level, and then verified using blinded component and whole body (WB) tests to establish a material model of the anthropomorphic test device (ATD) neck that was not over-fit. Additionally, a combined metric is introduced that incorporates the well-known correlation analysis (CORA) score with peak characteristics to holistically evaluate the material model performance. A Bergstrom Boyce material model fit to one loop of combined compression and tension experimental data performed the best within the training datasets. Its combined metric scores were 2.51 and 2.18 (max score of 3) in a constrained neck and head neck setup, respectively. In the blinded evaluation including flexed, extended, and WB simulations, similar combined scores were observed with 2.44, 2.26, and 2.60, respectively. The agreement between the combined scores in the training and validation dataset indicated that model was not over-fit and can be extrapolated into untested, but similar loading scenarios.