Brain deformation caused by a head impact leads to traumatic brain injury (TBI). The maximum principal strain (MPS) was used to measure the extent of brain deformation and predict injury, and the recent evidence has indicated that incorporating the maximum principal strain rate (MPSR) and the product of MPS and MPSR, denoted as MPS × SR, enhances the accuracy of TBI prediction. However, ambiguities have arisen about the calculation of MPSR. Two schemes have been utilized: one is to use the time derivative of MPS (MPSR1), and another is to use the first eigenvalue of the strain rate tensor (MPSR2). Both MPSR1 and MPSR2 have been applied in previous studies to predict TBI. To quantify the discrepancies between these two methodologies, we compared them across eight in-vivo and one in-silico head impact datasets and found that 95MPSR1 was slightly larger than 95MPSR2 and 95MPS × SR1 was 4.85 % larger than 95MPS × SR2 in average. Across every element in all head impacts, the average MPSR1 was 12.73 % smaller than MPSR2, and MPS × SR1 was 11.95 % smaller than MPS × SR2. Furthermore, logistic regression models were trained to predict TBI using MPSR (or MPS × SR), and no significant difference was observed in the predictability. The consequence of misuse of MPSR and MPS × SR thresholds (i.e. compare threshold of 95MPSR1 with value from 95MPSR2 to determine if the impact is injurious) was investigated, and the resulting false rates were found to be around 1 %. The evidence suggested that these two methodologies were not significantly different in detecting TBI.

The development of autonomous vehicle platoons will lead to new accident patterns. Insufficient research exists on occupant injury and protection associated with this new type of accident. To provide a reference for the research and technological development of occupant protection in autonomous vehicle platoon collisions, a continuous crash accident scenario involving a typical three-car autonomous vehicle platoon under high-speed conditions was utilized to determine boundary conditions such as impact time and speed. A full-scale finite element simulation was conducted to obtain the driver kinematics and injury response in each collision condition, and driver injury risk in the autonomous vehicle platoon collision scenarios was analyzed. The results show that although the risk of skull fracture is less than 1%, the risk of severe craniocerebral injury is significant, with the highest predicted risk of AIS 3+ using the BrIC criterion reaching 70.2%. Owing to excessive forward bending and backward extension of the cervical spine, three types of ligaments are at risk of serious injury. Furthermore, the risk of chest rib fracture is relatively low, whereas the risk of viscera damage is contingent on the collision sequence. When the middle car first experiences a frontal collision and is then rear-ended, the maximum principal strain on the driver's heart and liver far exceeds the damage threshold of 0.3, resulting in significant damage risk. Conversely, when the middle car is rear-ended and then collides with the front car, the maximum principal strain on the driver's internal organs is less than 0.3, resulting in a low overall damage risk.

Vulnerable road users (VRUs) face high risks of injury and death from traffic accidents. The current VRU head protection program relies on a single head impact velocity and injury assessment criteria that fail to account for brain tissue strain. This limitation affects the effectiveness of simulating real-world impacts and the accuracy of head injury risk assessments. In this study, the VRU head impact boundary conditions were extracted based on the reconstruction of 40 real-world pedestrian VRU head impacts. Using the Total Human Model for Safety (THUMS) head finite element model and headform impactor, this study explored the effects of real head impact boundary conditions on head kinematics and injury under procedural test scenarios and compared these conditions with test procedure scenarios. The results indicate that the peak linear acceleration in the current test procedure scenarios is higher; however, the peak rotational velocity is significantly lower than that observed in real-world accidents. Different impact locations have significant effects on the head kinematics and injury response parameters, particularly in stiffer areas, such as the windshield edges and lower right corner, where the injury risk under regulatory conditions is higher than that in real accident cases. In contrast, the opposite is true in other windshield areas. This study suggests that future programs or virtual assessments should diversify the head impact boundaries and injury assessment criteria to consider the differences in impact locations and the effects of head rotation on brain tissue injuries. For most windshields (non-edge areas), increasing the linear velocity enhances head rotation, and rotational injury assessment criteria should therefore be introduced. For future virtual assessments, injury criteria based on brain tissue strain should be used to assess VRU head injury risk in real accidents more comprehensively and accurately.

Finite element (FE) models of the human head are important injury assessment tools but developing a high-quality, hexahedral-meshed FE head model without compromising geometric accuracy is a challenging task. Important brain features, such as the cortical folds and ventricles, were captured only in a handful of FE head models that were primarily developed from two meshing techniques, i.e., surface-based meshing with conforming elements to capture the interfacial boundaries and voxel-based meshing by converting the segmented voxels into elements with and without mesh smoothing. Despite these advancements, little knowledge existed of how similar the strain responses were between surface- and voxel-based FE head models. This study uniquely addressed this gap by presenting three anatomically detailed models - a surface-based model with conforming meshes to capture the cortical folds-subarachnoid cerebrospinal fluid and brain-ventricle interfaces, and two voxel-based models (with and without mesh smoothing) - derived from the same imaging dataset. All numerical settings in the three models were exactly the same, except for the meshes. These three models were employed to simulate head impacts. The results showed that, when calculating commonly used injury metrics, including the percentile strains below the maximum (e.g., 99 percentile strain) and the volume of brain element with the strain over certain thresholds, the responses of the three models were virtually identical. Different strain patterns existed between the surface- and the voxel-based models at the interfacial boundary (e.g., sulci and gyri in the cortex, regions adjacent to the falx and tentorium) with strain differences exceeding 0.1, but remarkable similarities were noted at the non-interfacial region. The mesh smoothing procedure marginally reduced the strain discrepancies between the voxel- and surface-based model. This study yielded new quantitative insights into the general similarity in the strain responses between the surface- and voxel-based FE head models and underscored that caution should be exercised when using the strain at the interface to predict injury.

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84–165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

Ski racing is a high-risk sport for traumatic brain injury. A better understanding of the injury mechanism and the development of effective protective equipment remains central to resolving this urgency. Finite element (FE) models are useful tools for studying biomechanical responses of the brain, especially in real-world ski accidents. However, real-world accidents are often captured by handheld monocular cameras; the videos are shaky and lack depth information, making it difficult to estimate reliable impact velocities and posture which are critical for injury prediction. Introducing novel computer vision and deep learning algorithms offers an opportunity to tackle this challenge. This study proposes a novel framework for estimating impact kinematics from handheld, shaky monocular videos of accidents to inform personalized impact simulations. The utility of this framework is demonstrated by reconstructing a ski accident, in which the extracted kinematics are input to a neuroimaging-informed, personalized FE model. The FE-derived responses are compared with imaging-identified brain injury sites of the victim. The results suggest that maximum principal strain may be a useful metric for brain injury. This study demonstrates the potential of video-informed accident reconstructions combined with personalized FE modeling to evaluate individual brain injury. Statement of significance: Reconstructing real-world sports accidents combined with finite element (FE) models presents a unique opportunity to study brain injuries, as it enables simulating complex loading conditions experienced in reality. However, a significant challenge lies in accurately obtaining kinematics from the often shaky, handheld video footage of such accidents. We propose a novel framework that bridges the gap between real-world accidents and video-informed injury predictions. By integrating video analysis, 3D kinematics estimation, and personalized FE simulation, we extract accurate impact kinematics of a ski accident captured from handheld shaky monocular videos to inform personalized impact simulations, predicting the injury pathology identified by multimodal neuroimaging. This study provides important guidance on how best to estimate impact conditions from video-recorded accidents, opening new opportunities to better inform the biomechanical study of head trauma with improved boundary conditions.

Falls among the elderly cause a huge number of hip fractures worldwide. Energy absorbing floors (EAFs) represent a promising strategy to decrease impact force and hip fracture risk during falls. Femoral neck force is an effective predictor of hip injury. However, the biomechanical effectiveness of EAFs in terms of mitigating femoral neck force remains largely unknown. To address this, a whole-body computational model representing a small-size elderly woman with a biofidelic representation of the soft tissue near the hip region was employed in this study, to measure the attenuation in femoral neck force provided by four commercially available EAFs (Igelkott, Kradal, SmartCells, and OmniSports). The body was positioned with the highest hip force with a -10 degrees trunk angle and +10 degrees degrees anterior pelvis rotation. At a pelvis impact velocity of 3 m/s, the peak force attenuation provided by four EAFs ranged from 5% to 19%. The risk of hip fractures also demonstrates a similar attenuation range. The results also exhibited that floors had more energy transferred to their internal energy demonstrated greater force attenuation during sideways falls. By comparing the biomechanical effectiveness of existing EAFs, these results can improve the floor design that offers better protection performance in high-fall-risk environments for the elderly.

Finite element (FE) modeling provides a means to examine how global kinematics of repetitive head loading in sports influences tissue level injury metrics. FE simulations of controlled soccer headers in two directions were completed using a human head FE model to estimate biomechanical loading on the brain by direction. Overall, headers were associated with 95th percentile peak maximum principal strains up to 0.07 and von Mises stresses up to 1450 Pa, and oblique headers trended toward higher values than frontal headers but below typical injury levels. These quantitative data provide insight into repetitive loading effects on the brain.