Impact Response Comparison Between Parametric Human Models and Postmortem Human Subjects with a Wide Range of Obesity Levels

A First Step Toward a Family of Morphed Human Body Models Enabling Prediction of Population Injury Outcomes

The injury risk in a vehicle crash can depend on occupant specific factors. Virtual crash testing using finite element human body models (HBMs) to represent occupant variability can enable the development of vehicles with improved safety for all occupants. In this study, it was investigated how many HBMs of different sizes that are needed to represent a population crash outcome through a metamodel. Rib fracture risk was used as an example occupant injury outcome. Morphed HBMs representing variability in sex, height, and weight within defined population ranges were used to calculate population variability in rib fracture risk in a frontal and a side crash. Two regression methods, regularized linear regression with second-order terms and Gaussian process regression (GPR), were used to metamodel rib fracture risk due to occupant variability. By studying metamodel predictive performance as a function of training data, it was found that constructing GPR metamodels using 25 individuals of each sex appears sufficient to model the population rib fracture risk outcome in a general crash scenario. Further, by utilizing the known outcomes in the two crashes, an optimization method selected individuals representative for population outcomes across both crash scenarios. The optimization results showed that 5-7 individuals of each sex were sufficient to create predictive GPR metamodels. The optimization method can be extended for more crashes and vehicles, which can be used to identify a family of HBMs that are generally representative of population injury outcomes in future work.

Size and age of fatal drivers by crash type, vehicle type and gender

Objective: The objective of this study was to determine the physical characteristics of fatal drivers in motor vehicle crashes with focus on rear impacts.Methods: 1998 to 2020 FARS data was analyzed for height, weight, and age of fatal drivers. The data was queried by gender, crash type and vehicle type.Results: The average fatal driver weighed 80.4 kg, was 173.4 cm tall, and was 43 years old. Females were 16.0 kg lighter and 14.2 cm shorter than males on average. The height was 151.2 cm for the 5^th percentile female, 177.0 cm for the 50^th male and 188.9 cm for the 95^th male. The weight of fatal drivers increased linearly with calendar year. The increase rate was greater in females than in males. About 3% of fatal drivers were involved in rear crashes, 39.9% in frontal crashes and 36.8% in rollovers. The average fatal driver was 172.5 cm tall and weighed 81.0 kg in rear impacts. They were similar in height and weight to the overall sample. The average fatal driver in rear impacts was 46 years old, 3 years older than the overall average. Pickup truck drivers weighed 85.4 kg and were 176.8 cm tall on average. They were heavier and taller than passenger car drivers on average, which were 78.0 kg and 172.2 cm. Fatally injured minivan drivers were 10 years older than fatally injured passenger car drivers on average. The findings are compared with ATDs (anthropometric test devices) used in sled and crash testing.Conclusion: The average weight of fatal drivers increased with calendar year. The average size of fatal drivers was similar by crash types. Fatal drivers were older in rear impacts.

Evaluation of a diverse population of morphed human body models for prediction of vehicle occupant crash kinematics

Morphing can be used to alter human body models (HBMs) to represent a diverse population of occupants in car crashes. The mid-sized male SAFER HBM v9 was parametrically morphed to match 22 Post Mortem Human Subjects, loaded in different configurations. Kinetics and kinematics were compared for the morphed and baseline HBMs. In frontal impacts, the morphed HBMs correlated closer with the kinematics of obese subjects, but lower to small females. In lateral impacts HBM responses were too stiff. This study outlines a necessary evaluation of all HBMs that should be morphed to represent the diverse population in vehicle safety evaluations.

Development and Application of Digital Human Models in the Field of Vehicle Collisions: A Review

In the human-vehicle-road system of collisions, the human is the most important factor, and digital human models (DHMs) are developed with the aim of preventing or at least reducing human injury. Because most of the relevant literature is focused mainly on collisions in traffic accidents (TAs), only some of the literature reviewed in this paper involves research results on other aspects of collisions. In this review, based on the background of DHMs and the application of DHMs regarding human injury biomechanics in collisions field, research results regarding the development of DHMs are described, the methods for verifying such models are introduced, and the application of the research results is discussed based on the aspect of human injury biomechanics. From the research literature, the development and validation of DHMs and their application in human injury biomechanics are summarized, and future research trends are proposed and discussed.

Restraint systems considering occupant diversity and pre-crash posture

OBJECTIVE

Use volunteer data and parametric finite element (FE) human body models to investigate how restraint systems can be designed to adapt to a diverse population and pre-crash posture changes induced by active safety features.

METHODS

Four FE human models were generated by morphing the midsize male GHBMC simplified model into geometries representing a midsize male, midsize female, short obese female (BMI 40 kg/m²), and large obese male (BMI 40 kg/m²) based on statistical skeleton and body shape geometry models. Each human model was positioned in a generic vehicle driver environment using two occupant pre-crash postures based on volunteer test results including one resulting from 1-g abrupt braking events. Improved restraint designs were manually developed for each occupant model in a 56 km/h frontal crash condition by adding a knee airbag, adjusting the shoulder belt load limit, steering column force, and driver airbag properties (tethers, inflation, and vent size). The improved designs were then tested at both pre-crash postures. Injury risks for the head, neck, chest, and lower extremities were analyzed.

RESULTS

Human size and shape dominated the occupant injury measures, while the pre-crash-braking induced posture had minimal effects. Some of the safety concerns observed for large occupants include head strike-through the airbag and a conflict between head and chest injuries, which were mitigated by a stiffer restraint system with properly-tuned driver airbag. Chest injuries were a prominent safety concern for female occupants, mitigated by a softer seatbelt and smaller airbag size near the chest. Obese occupants exhibited a higher likelihood of lower extremity injuries indicating a need for a knee airbag. A diverse set of improved restraint designs were effective in lowering injury risks, indicating that restraint adaptability is necessary for accounting for occupant diversity.

CONCLUSIONS

This study investigated the effects of occupant size and shape variability, posture, and restraint design on injury risk for high-speed frontal crashes. More forward initial postures due to active safety features may decrease head, neck, and lower extremity injury risk, but may also increase chest injury risk. Safety concerns observed for large occupants include head strike-through and a conflict between head and chest injuries. Obese occupants had higher knee-thigh-hip injury risk. New restraints that adapt to occupant size and body shape may improve crash safety for all occupants. Further investigation is needed to confirm and extend the findings of this study.

The relationship between weight indices and injuries and mortalities caused by the motor vehicle accidents: a systematic review and meta-analysis

BACKGROUND

The relationship between weight indices and injuries and mortality in motor vehicle accidents is unknown. Systematic review studies addressing the collection and analysis of the relationship in investigations are very limited. The purpose of this systematic review is to determine the relationship between BMI, obesity and overweight with mortality and injuries and their severity and vulnerable organs after the motor vehicle accident.

METHODS

The databases (MEDLINE/PUBMED, EMBASE, Web of Science, etc) were searched for relevant abstracts using certain keywords. Of all the articles, similar ones were removed considering different filters. The collected data were entered into the STATA SE v 13.1. The heterogeneity of the data was analyzed using i2 statistics. In addition, the estimates of the study were done based on the age group (children and adults) and the impact of obesity on different regions of the body.

RESULTS

A direct relationship was observed between the overall BMI and the degrees of injuries (CI=0.503-1.139), and mortality due to motor vehicle accident (CI=1.267-1.471). A positive relationship was found between obesity and AIS+2 (CI=0.653-1.426), and AIS+3 (CI=1.184-1.741), and ISS (CI=1.086-1.589). Also, a negative relationship between overweight and injuries rates, and a direct relationship between overweight and mortality (CI=0.979-1.167), and injuries with index of AIS+2 (CI=1.178-0.768) and AIS+3 (CI=0.48-2.186) were found.

CONCLUSIONS

The prediction of injury, mortality and severity of injuries in the motor vehicle accident by the variable of obesity and overweight determines the need to design prevention programs for this vulnerable group at all levels.

Validating diverse human body models against side impact tests with post-mortem human subjects

This study aimed at evaluating the ability of morphed finite element (FE) human body models (HBMs) to reproduce the impact responses of post-mortem human subjects (PMHS) with various stature and shape. Ten side impact tests previously performed using seven PMHS under 3 m/s and 8 m/s impact velocities were selected for model evaluation. With weight, stature, sex, and age of PMHS, seven FE HBMs were developed by morphing the midsize male THUMS model into the target geometries predicted by the statistical skeleton and external body shape models. The model-predicted force histories, accelerations along the spine, and deflections in the chest and abdomen were compared to the test data. For comparison, simulations in all testing conditions were also conducted with the original midsize male THUMS, and the results from the THUMS simulations were scaled to the weight and stature from each PMHS. The CORrelation and Analysis (CORA) was used to evaluate the model accuracy, with CORA scores close to one indicating excellent agreement. Ten simulations using the morphed models exhibited 0.80 ± 0.01, 0.80 ± 0.01, 0.78 ± 0.02, and 0.78 ± 0.02 CORA scores for the impact forces to the thorax, abdomen, iliac-wings, and greater-trochanter, respectively; the corresponding CORA scores with the original THUMS were markedly lower at 0.60 ± 0.06, 0.69 ± 0.05, 0.71 ± 0.05, and 0.69 ± 0.04; while those for the scaled THUMS were 0.65 ± 0.05, 0.71 ± 0.05, 0.73 ± 0.05, and 0.72 ± 0.02, also lower than the morphed models. Across all simulations, the morphed HBMs demonstrated significantly higher accuracy than the THUMS with or without scaling. These results suggested the necessity of accounting for size and shape effects on predicting human responses in side impacts.

Development and validation of a semi-automatic landmark extraction method for mesh morphing

Currently, landmark-based mesh morphing technology is widely used to rapidly obtain meshes with specific geometry, which is suitable to develop parametric human finite element (FE) models. However it takes too much time for landmark extraction to obtain high geometric accuracy. The purpose of this study is to develop and validate a semi-automatic landmark extraction method to reduce the time of manual selection of landmarks without sacrificing the accuracy of identifying landmarks in the process of mesh morphing. A few contour edge landmarks were extracted manually. Mathematical landmarks and pseudo-landmarks were extracted automatically by user-defined algorithm. The radial basis function (RBF) was used to morph the baseline FE model into the target geometry based on these landmarks. The cervical vertebra (C5), rib (R7) and femur were selected as the target geometries to verify the effectiveness of the method. The maximum mean geometric error of the three types of target geometries was less than 1 mm. The mesh quality of the morphed FE model was similar to that of the baseline FE model. Compared to the traditional manual method, 2/3 to 3/4 of the time for landmark extraction was saved by the semi-automatic method.

Frontal crash simulations using parametric human models representing a diverse population

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.

Combining specimen-specific finite-element models and optimization in cortical-bone material characterization improves prediction accuracy in three-point bending tests

Although the beam theory is widely used for calculating material parameters in three-point bending test, it cannot accurately describe the biomechanical properties of specimens after the yield. Hence, we propose a finite element (FE) based optimization method to obtain accurate bone material parameters from three-point bending test. We tested 80 machined bovine cortical bone specimens at both longitudinal and transverse directions using three-point bending. We then adopted the beam theory and the FE-based optimization method combined with specimen-specific FE models to derive the material parameters of cortical bone. We compared data obtained using these two methods and further evaluated two groups of parameters with three-point bending simulations. Our data indicated that the FE models with material properties from the FE-based optimization method showed best agreements with experimental data for the entire force-displacement responses, including the post-yield region. Using the beam theory, the yield stresses derived from 0.0058% strain offset for the longitudinal specimen and 0.0052% strain offset for the transverse specimen are closer to those derived from the FE-based optimization method, compared to yield stresses calculated without strain offset. In brief, we conclude that the optimization FE method is more appropriate than the traditional beam theory in identifying the material parameters of cortical bone for improving prediction accuracy in three-point bending mode. Given that the beam theory remains as a popular method because of its efficiency, we further provided correction functions to adjust parameters calculated from the beam theory for accurate FE simulation.