Which Pragmatic Finite Element Human Body Model Scaling TechniqueCan Most Accurately Predict Head Impact Conditions in Pedestrian-Car Crashes?

Pedestrian-to-vehicle crashes remain a world-wide health issue. Human body models (HBMs) are valuable tools for pedestrian safety-system development and evaluations. HBM biofidelity evaluation against full-scale post-mortem human subjects (PMHS) is crucial but challenging as common normalisation techniques enable limited adaptation for varying anthropometry, and morphing HBMs to experimental data is rarely feasible. This study evaluates the effectiveness of six pragmatic pedestrian HBM scaling techniques, focusing on head impact conditions and upper-body kinematics, using the Total Human Model for Safety (THUMS) 4.0. Upper-body 6 degrees-of-freedom kinematics prior to head-vehicle contact and head impact conditions were compared with five PMHS pedestrian experiments using a small sedan. The most accurate head impact conditions were achieved when THUMS was scaled with one z-factor adjusting its height, one x-y-factor adjusting its mass, and then translated in z-direction to adjust the pelvis height to the experimental measurements. THUMS generally reproduced head impact conditions and in-plane motions, and was numerically stable. Out-of-plane movements generally scored poorly but were small in the experiments. Accurate upper arm response was crucial for accurate head impact conditions. Possible THUMS improvements include softening the neck slightly in lateral bending and reducing resistance to upper-arm abduction, especially for large angles.