Frontal crash simulations using parametric human models representing a diverse population

Abstract

Objective: Analyses of crash data have shown that older, obese, and/or female occupants have a higher risk of injury in frontal crashes compared to the rest of the population. The objective of this study was to use parametric finite element (FE) human models to assess the increased injury risks and identify safety concerns for these vulnerable populations. Methods: We sampled 100 occupants based on age, sex, stature, and body mass index (BMI) to span a wide range of the U.S. adult population. The target anatomical geometry for each of the 100 models was predicted by the statistical geometry models for the rib cage, pelvis, femur, tibia, and external body surface developed previously. A regional landmark-based mesh morphing method was used to morph the Global Human Body Models Consortium (GHBMC) M50-OS model into the target geometries. The morphed human models were then positioned in a validated generic vehicle driver compartment model using a statistical driving posture model. Frontal crash simulations based on U.S. New Car Assessment Program (U.S. NCAP) were conducted. Body region injury risks were calculated based on the risk curves used in the US NCAP, except that scaling was used for the neck, chest, and knee-thigh-hip injury risk curves based on the sizes of the bony structures in the corresponding body regions. Age effects were also considered for predicting chest injury risk. Results: The simulations demonstrated that driver stature and body shape affect occupant interactions with the restraints and consequently affect occupant kinematics and injury risks in severe frontal crashes. U-shaped relations between occupant stature/weight and head injury risk were observed. Chest injury risk was strongly affected by age and sex, with older female occupants having the highest risk. A strong correlation was also observed between BMI and knee-thigh-hip injury risk, whereas none of the occupant parameters meaningfully affected neck injury risks.Conclusions: This study is the first to use a large set of diverse FE human models to investigate the combined effects of age, sex, stature, and BMI on injury risks in frontal crashes. The study demonstrated that parametric human models can effectively predict the injury trends for the population and may now be used to optimize restraint systems for people who are not similar in size and shape to the available anthropomorphic test devices (ATDs). New restraints that adapt to occupant age, sex, stature, and body shape may improve crash safety for all occupants. ARTICLE HISTORY

A detailed finite element model of a mid-sized male for the investigation of traffic pedestrian accidents

“…Future work will be done to develop an appropriate model morphing technique to match the M50-P and PMHS internal anatomies, which is necessary to improve model subject specific predictions. 65,66 Lastly, the friction coefficient between the test vehicle and body suit that housed the PMHS’s was not reported, leading to us using coefficients found in literature.…”

Adapting load limiter deployment for frontal crash diversity

“…However, the utility of the model is not simply a matter of biofidelity or limited sizes but also the representation of different injury mechanisms and kinematics, which may have substantial impact on occupant protection. For example, recent studies have shown that occupants with different sizes, shapes, ages, and genders sustained very different kinematics and injury concerns in frontal crashes (Hu et al 2019). The utility of the model is also a matter of the model's sensitivity to changes in restraint design, and this type of sensitivity is rarely assessed for dummies or human body models.…”

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

“…An exception is probabilistic rib fracture risk prediction, which can produce multiple age-adjusted risk predictions from the same, fixed, HBM rib strain predictions. In recent years, morphing (re-shaping) the geometry of HBMs based on statistical human shape models has been used to create several HBMs that geometrically represent male and female occupants of varying age, height, and weight ( Hu et al, 2019 ; von Kleeck et al, 2022 ; Larsson et al, 2022b ). However, these HBMs still represent geometrically average individuals, with an average ribcage shape, for the subpopulation described by each choice of sex, age, height, and weight.…”