Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models

Obesity as a Risk Factor for Adverse Outcomes After Pedestrian Trauma Accidents in the Pediatric Population

OBJECTIVE

The aim of the study is to determine whether overweight or obese children are at an increased risk for injury and adverse outcomes following pedestrian motor vehicle accidents.

METHODS

We performed a retrospective study of patients between the ages of 2 and 17 who were pedestrians injured in a collision with a motorized vehicle, presenting to a level 1 trauma center, between January 1, 2010, to December 31, 2021. Patients with admission weights falling above the 90th percentile of the Centers for Disease Control and Prevention's sex-specific growth charts were identified as overweight/obese, those below the cutoff were categorized as nonobese. Groups were compared regarding demographics, mechanism of injury, Injury Severity Score, and Abbreviated Injury Scale by body region of injury. Outcome measures included hospital admission, length of hospital stay, intensive care unit (ICU) admission, ICU length of stay, and mortality.

RESULTS

Of the 306 patients included, 72 (23.5%) were overweight/obese and 234 (76.5%) were nonobese. The mean Injury Severity Score scores were higher among overweight/obese patients (5.37 vs 8.74, P = 0.008). Overweight/obese children were more likely to sustain severe abdominal injuries (Abbreviated Injury Scale ≥ 3) (12.2% vs 5.1%; odds ratio [OR], 2.64; 95% CI, 1.07-6.56; P = 0.030), be admitted to the hospital (94.5% vs 74.3%; OR, 12.07; 95% CI, 2.87-50.72; P < 0.001), require ICU admission (31.0% vs 20.0%, OR, 1.87; 95% CI, 1.03-3.36; P = 0.036), and require a longer ICU stay (0.9 vs 0.4 days, P = 0.014) compared with nonobese patients.

CONCLUSIONS

Obese and overweight children are at increased risk for higher injury severity scores, severe abdominal injuries, and ICU admission after pedestrian motor vehicle accidents.

A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

Personalization of human body models and beyond via image registration

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.

Can we reposition finite element human body model like dummies?

Rapidly repositioning finite element human body models (FE-HBMs) with high biofidelity is an important but notorious problem in vehicle safety and injury biomechanics. We propose to reposition the FE-HBM in a dummy-like manner, i.e., through pose parameters prescribing joint configurations. Skeletons are reconfigured along the trajectories inferred from model-specific bone geometries. We leverage differential geometry to steer equidistant moves along the congruent articulated bone surfaces. Soft tissues are subsequently adapted to reconfigured skeletons through a series of operations. The morph-contact algorithm allows the joint capsule to slide and wrap around the repositioned skeletons. Nodes on the deformed capsule are redistributed following an optimization-based approach to enhance element regularity. The soft tissues are transformed accordingly via thin plate spline. The proposed toolbox can reposition the Total Human Body Model for Safety (THUMS) in a few minutes on a whole-body level. The repositioned models are simulation-ready, with mesh quality maintained on a comparable level to the baseline. Simulations of car-to-pedestrian impact with repositioned models exhibiting active collision-avoidance maneuvers are demonstrated to illustrate the efficacy of our method. This study offers an intuitive, effective, and efficient way to reposition FE-HBMs. It benefits all posture-sensitive works, e.g., out-of-position occupant safety and adaptive pedestrian protection. Pose parameters, as an intermediate representation, join our method with recently prosperous perception and reconstruction techniques of the human body. In the future, it is promising to build a high-fidelity digital twin of real-world accidents using the proposed method and investigate human biomechanics therein, which is of profound significance in reshaping transportation safety studies in the future.

Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging

Non-rigid registration between 3D surfaces is an important but notorious problem in medical imaging, because finding correspondences between non-isometric instances is mathematically non-trivial. We propose a novel self-supervised method to learn shape correspondences directly from a group of bone surfaces segmented from CT scans, without any supervision from time-consuming and error-prone manual annotations. Relying on a Siamese architecture, DiffusionNet as the feature extractor is jointly trained with a pair of randomly rotated and scaled copies of the same shape. The learned embeddings are aligned in spectral domain using eigenfunctions of the Laplace-Beltrami Operator. Additional normalization and regularization losses are incorporated to guide the learned embeddings towards a similar uniform representation over spectrum, which promotes the embeddings to encode multiscale features and advocates sparsity and diagonality of the inferred functional maps. Our method achieves state-of-the-art results among the unsupervised methods on several benchmarks, and presents greater robustness and efficacy in registering moderately deformed shapes. A hybrid refinement strategy is proposed to retrieve smooth and close-to-conformal point-to-point correspondences from the inferred functional map. Our method is orientation and discretization-invariant. Given a pair of near-isometric surfaces, our method automatically computes registration in high accuracy, and outputs anatomically meaningful correspondences. In this study, we show that it is possible to use neural networks to learn general embeddings from 3D shapes in a self-supervised way. The learned features are multiscale, informative, and discriminative, which might potentially benefit almost all types of morphology-related downstream tasks, such as diagnostics, data screening and statistical shape analysis in future.

Influence of walking on knee ligament response in car-to-pedestrian collisions

Pedestrians are likely to experience walking before accidents. The walking process imposes cyclic loading on knee ligaments and increases knee joint temperature. Both cyclic loading and temperature affect the material properties of ligaments, which further influence the risk of ligament injury. However, the effect of such walking-induced material property changes on pedestrian ligament response has not been considered. Therefore, in this study, we investigated the influence of walking on ligament response in car-to-pedestrian collisions. Using Total Human Model for Safety (THUMS) model, knee ligament responses (i.e., cross-sectional force and local strain) were evaluated under several crash scenarios (i.e., two impact speeds, two knee contact heights, and three pedestrian postures). In worst case scenarios, walking-induced changes in ligament material properties led to a 10% difference in maximum local strain and a 6% difference in maximum cross-sectional force. Further considering the material uncertainty caused by experimental dispersion, the ligament material property changes due to walking resulted in a 28% difference in maximum local strain and a 26% difference in maximum cross-sectional force. This study demonstrates the importance of accounting for walking-induced material property changes for the reliability of safety assessments and injury analysis.

Effect of Tissue Erosion Modeling Techniques on Pedestrian Impact Kinematics

The pedestrian is one of the most vulnerable road users and has experienced increased numbers of injuries and deaths caused by car-to-pedestrian collisions over the last decade. To curb this trend, finite element models of pedestrians have been developed to investigate pedestrian protection in vehicle impact simulations. While useful, modeling practices vary across research groups, especially when applying knee/ankle ligament and bone failure. To help better standardize modeling practices this study explored the effect of knee ligament and bone element elimination on pedestrian impact outcomes. A male 50th percentile model was impacted by three European generic vehicles at 30, 40, and 50 km/h. The pedestrian model was set to three element elimination settings: the "Off-model" didn't allow any element erosion, the "Lig-model" allowed lower-extremity ligament erosion, and the "All-model" allowed lower-extremity ligament and bone erosion. Failure toggling had a significant effect on impact outcomes (0 < p ≤ 0.03). The head impact time response was typically the smallest for the "Off-model" while the wrap around distance response was always largest for the All-model. Moderate differences in maximum vehicle-pedestrian contact forces across elimination techniques were reported in this study (0.1 - 1.7 kN). Future work will examine additional failure modelling approaches, model anthropometries and vehicles to expand this investigation.

A validated lower extremity model to investigate the effect of stabilizing knee components in pedestrian collisions

Lower extremity injuries account for over 50% of pedestrian orthopedic injuries in car-to-pedestrian collisions. Pedestrian finite element models are useful tools for studying pedestrian safety, but current models use simplified knee models that exclude potentially important stabilizing knee components. The effect of these stabilizing components in pedestrian impacts is currently unknown. The goal of this study was to develop a detailed lower-extremity model to investigate the effect of these stabilizing components on pedestrian biomechanics. In this study the Global Human Body Model Consortium male 50th percentile pedestrian model lower body was updated to include various stabilizing knee components, enhance geometric anatomical accuracy of previously modeled soft tissue structures, and update hard and soft tissue material models. The original and updated models were compared across 13 validation tests and the updated model reported significantly ( p = 0.01) larger CORA scores (0.73 ± 0.15) than the original model (0.56 ± 0.20). To investigate the effect of the new stabilizing knee components the updated model had its stabilizing components severed. The severed and intact models were impacted by the EuroNCAP SUV and family car models at 30 and 40 km/h. The intact and severed models reported nearly identical head impact times, wrap around distances, and lower-extremity injury outcomes in all four impacts, but the stabilizing components reduced the varus knee angle of the secondarily impacted leg by up to 4.9°. The stabilizing components may prevent secondary impacted leg injuries in lower intensity impacts but overall had little effect on pedestrian biomechanical outcomes.

Age-Related Study and Collision Response of Material Properties of Long Bones in Chinese Pedestrian Lower Limbs

In forensic examination cases, lower limb injuries are common, and pedestrians of different ages suffer different injuries when they are hit by vehicles, especially the injuries to the long bones of the lower limbs. Aging remains a challenging issue for the material properties and injury biomechanical properties of pedestrian lower limb long bones. We analyzed the regression relationship between the age of 50 Chinese pedestrians and the material properties of the lower limb long bones (femur, tibia). We compared them with previous studies to propose a regression model suitable for Chinese human long bone material properties. Through the established Human Active Lower Limb (HALL) model that conforms to the Chinese human anatomy, seven pedestrians’ (20/30/40/50/60/70/80 years old (YO)) lower limbs were parameterized to assign long bone material properties. In the finite element analysis, the Hall model was side-impacted by a family car (FCR) at speeds of 30/40/50/60/70 km/h, respectively. The results showed that an increase in age was negatively correlated with a decrease in the material properties of each long bone. Moreover, with an increase in age, the tolerance limit of long bones gradually decreases, but there will be a limit, and there is no obvious positive correlation with age. During a standing side impact, the stress change in the femur was significantly smaller than that of the tibia, and the stress of the femur and tibia decreased with age. Age is a more significant influencing factor for lower limb injuries. Older pedestrians have a higher risk of lower limb injuries. Forensic experts should pay attention to the critical factor of age when encountering lower limb traffic accident injuries in forensic identification work.

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts

The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle–pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects (“pedestrians”) to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian–vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the “pre-crash postures.” We recorded the kinetic and kinematic features of the pedestrian avoidance responses—including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels—using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.