Geometrical Personalization of Pedestrian Finite Element Models Using Morphing Increases the Biofidelity of Their Impact Kinematics 2016-01-1506
Pedestrian finite element models (PFEM) are used to investigate and predict the injury outcomes from vehicle-pedestrian impact. As postmortem human surrogates (PMHS) differ in anthropometry across subjects, it is believed that the biofidelity of PFEM cannot be properly evaluated by comparing a generic anthropometry model against the specific PMHS test data. Global geometric personalization can scale the PFEM geometry to match the height and weight of a specific PMHS, while local geometric personalization via morphing can modify the PFEM geometry to match specific PMHS anatomy. The goal of the current study was to evaluate the benefit of morphed PFEM compared to globally-scaled and generic PFEM by comparing the kinematics against PMHS test results. The AM50 THUMS PFEM (v4.01) was used as a baseline for anthropometry, and personalized PFEM were created to the anthropometric specifications of two obese PMHS used in a previous pedestrian impact study using a mid-size sedan. Personalization was done using either global scaling or morphing, and the kinematics of each PFEM model were compared to the experiments using a correlation analysis (CORA). While the scaled models showed high correlation (CORA score = 0.92) with the PMHS compared to the baseline (0.85), morphing increases the biofidelity of PFEM impact kinematics (0.96). In addition, the morphed PFEM correlated the best for wraparound-distance, knee and pelvic impact locations and timing. Consequently, morphing is essential for evaluating the biofidelity of a human body model when making direct comparisons to specific pedestrian impact test cases.
Citation: Poulard, D., Chen, H., and Panzer, M., "Geometrical Personalization of Pedestrian Finite Element Models Using Morphing Increases the Biofidelity of Their Impact Kinematics," SAE Technical Paper 2016-01-1506, 2016, https://doi.org/10.4271/2016-01-1506. Download Citation
David Poulard, Huipeng Chen, Matthew Panzer