Evaluation of injury thresholds for predicting severe head injuries in vulnerable road users resulting from ground impact via detailed accident reconstructions

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.

Parametric analysis of craniocerebral injury mechanism in pedestrian traffic accidents based on finite element methods

PURPOSE

The toughest challenge in pedestrian traffic accident identification lies in ascertaining injury manners. This study aimed to systematically simulate and parameterize 3 types of craniocerebral injury including impact injury, fall injury, and run-over injury, to compare the injury response outcomes of different injury manners.

METHODS

Based on the Total Human Model for Safety (THUMS) and its enhanced human model THUMS-hollow structures, a total of 84 simulations with 3 injury manners, different loading directions, and loading velocities was conducted. Von Mises stress, intracranial pressure, maximum principal strain, cumulative strain damage measure, shear stress, and cranial strain were employed to analyze the injury response of all areas of the brain. To examine the association between injury conditions and injury consequences, correlation analysis, principal component analysis, linear regression, and stepwise linear regression were utilized.

RESULTS

There is a significant correlation observed between each criterion of skull and brain injury (p < 0.01 in all Pearson correlation analysis results). A 2-phase increase of cranio-cerebral stress and strain as impact speed increases. In high-speed impact (> 40 km/h), the Von Mises stress on the skull was with a high possibility exceed the threshold for skull fracture (100 MPa). When falling and making temporal and occipital contact with the ground, the opposite side of the impacted area experiences higher frequency stress concentration than contact at other conditions. Run-over injuries tend to have a more comprehensive craniocerebral injury, with greater overall deformation due to more adequate kinetic energy conduction. The mean value of maximum principal strain of brain and Von Mises stress of cranium at run-over condition are 1.39 and 403.8 MPa, while they were 1.31, 94.11 MPa and 0.64, 120.5 MPa for the impact and fall conditions, respectively. The impact velocity also plays a significant role in craniocerebral injury in impact and fall loading conditions (the p of all F test < 0.05). A regression equation of the craniocerebral injury manners in pedestrian accidents was established.

CONCLUSION

The study distinguished the craniocerebral injuries caused in different manners, elucidated the biomechanical mechanisms of craniocerebral injury, and provided a biomechanical foundation for the identification of craniocerebral injury in legal contexts.

Development of a head-weighted injury criterion for evaluation of multiple types of AIS 4+ injuries for vulnerable road users

Vulnerable Road users (VRUs) often suffer multiple fatal head injury types simultaneously in road accidents. In this study, a head-weighted injury criterion (HWIC4) was proposed for assessing the risk of head AIS 4+ injuries considering multiple injury types. Firstly, the kinematic characteristics of VRUs in the 50 in-depth accidents were reconstructed by using multi-body system models, and head injuries were reconstructed using eight head kinematic-based injury criteria and eight brain tissue injury criteria via the THUMS (Ver. 4.0.2) head finite element model. The predictive capability of each injury criterion to predict head AIS 4+ injuries was assessed and four better predictors (HIC15, angular acceleration, coup pressure, and maximum principal strain) were selected. The different head injury types and the weighting parameters for each injury type were taken into account in the development of HWIC4. Finally, the effectiveness and evaluation of HWIC4 for head AIS 4+ injury was validated based on the area under of receiver operating characteristic (AUROC) curve and reconstruction results from 10 additional selected accident cases. The results showed that HWIC4 has a good predictive capability for head AIS 4+ injuries with an AUROC of 0.983, which means that HWIC4 is superior and more reliable than a single head injury criterion. This knowledge further improves the capability of head injury criteria to predict head AIS 4+ injuries.

Strain-rate-dependent material properties of human lung parenchymal tissue using inverse finite element approach

Automobile crashes and blunt trauma often lead to life-threatening thoracic injuries, especially to the lung tissues. These injuries can be simulated using finite element-based human body models that need dynamic material properties of lung tissue. The strain-rate-dependent material parameters of human parenchymal tissues were determined in this study using uniaxial quasi-static (1 mm/s) and dynamic (1.6, 3, and 5 m/s) compression tests. A bilinear material model was used to capture the nonlinear behavior of the lung tissue, which was implemented using a user-defined material in LS-DYNA. Inverse mapping using genetic algorithm-based optimization of all experimental data with the corresponding FE models yielded a set of strain-rate-dependent material parameters. The bilinear material parameters are obtained for the strain rates of 0.1, 100, 300, and 500 s-1. The estimated elastic modulus increased from 43 to 153 kPa, while the toe strain reduced from 0.39 to 0.29 when the strain rate was increased from 0.1 to 500 s-1. The optimized bilinear material properties of parenchymal tissue exhibit a piecewise linear relationship with the strain rate.

Head impact kinematics and injury risks during E-scooter collisions against a curb

E-scooters as a mode of transportation is rapidly growing in popularity. This study evaluates head impact conditions and injury risk associated with E-scooter crashes. A multibody model of E-scooter falls induced by wheel-curb collision was built and compared with an experimental E-scooter crash test. A total of 162 crash scenarios were simulated to assess the effect of fall conditions (E-scooter initial speed and inclination, obstacle orientation, and user size) on the head impact kinematics. The forehead hit the ground first in 44% of simulations. The average tangential and normal impact speeds were 3.5 m/s and 4.8 m/s respectively. Nearly 100% of simulations identified a risk of concussion (linear acceleration peak >82 g and rotational acceleration peak >6383 rad/s2) and 90% of simulations suggested a risk of severe head injuries (HIC>700). This work provides preliminary data useful for the assessment and design of protective gears.

Evaluating the Intracranial Pressure Biofidelity and Response Repeatability of a Physical Head-Brain Model in Frontal Impacts

Headforms are widely used in head injury research and headgear assessment. Common headforms are limited to replicating global head kinematics, although intracranial responses are crucial to understanding brain injuries. This study aimed to evaluate the biofidelity of intracranial pressure (ICP) and the repeatability of head kinematics and ICP of an advanced headform subjected to frontal impacts. Pendulum impacts were performed on the headform using various impact velocities (1-5 m/s) and impactor surfaces (vinyl nitrile 600 foam, PCM746 urethane, and steel) to simulate a previous cadaveric experiment. Head linear accelerations and angular rates in three axes, cerebrospinal fluid ICP (CSFP), and intraparenchymal ICP (IPP) at the front, side, and back of the head were measured. The head kinematics, CSFP, and IPP demonstrated acceptable repeatability with coefficients of variation generally being less than 10%. The BIPED front CSFP peaks and back negative peaks were within the range of the scaled cadaver data (between the minimum and maximum values reported by Nahum et al.), while side CSFPs were 30.9-92.1% greater than the cadaver data. CORrelation and Analysis (CORA) ratings evaluating the closeness of two time histories demonstrated good biofidelity of the front CSFP (0.68-0.72), while the ratings for the side (0.44-0.70) and back CSFP (0.27-0.66) showed a large variation. The BIPED CSFP at each side was linearly related to head linear accelerations with coefficients of determination greater than 0.96. The slopes for the BIPED front and back CSFP-acceleration linear trendlines were not significantly different from cadaver data, whereas the slope for the side CSFP was significantly greater than cadaver data. This study informs future applications and improvements of a novel head surrogate.

Evaluation of injury threshold from the number of rib fracture for predicting pulmonary injuries in blunt chest trauma

Background

Blunt chest trauma is a common presentation in emergency departments. The relationship between bone fractures and organ injuries has not been studied in detail. The purpose of this study was to examine the degree of external force represented by the number of rib fractures that causes lung injury in blunt chest trauma.

Patients and methods

This study was performed retrospectively using trauma patients who received medical examinations in a single university hospital emergency center between April 2015 and March 2020. We examined the association between the number of rib fractures and pulmonary damage using multivariable regression analysis and considered the relationship between rib fracture location and each type of lung injury.

Results

A total of 317 patients were included. The mean age was 63.1 years, 65.0% were male, and traffic accidents were the most common mechanism of injury (55.8%). The number of mean rib fractures was 4.0, and the mean Injury Severity Score was 11.3. The number of rib fractures was associated with an increased risk of pulmonary injuries: pulmonary contusion (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.14-1.48, p < 0.05); hemothorax (OR 1.22, 95% CI 1.08-1.38, p < 0.05); pneumothorax (OR 1.15, 95% CI 1.02-1.30, p < 0.05); and hemopneumothorax (OR 1.14, 95% CI 1.01-1.28, p < 0.05). In addition, bilateral rib fractures were associated with fractures of the superior ribs more often and more severely, but were not associated with the occurrence of each type of lung injury.

Conclusion

The number of rib fractures was associated with an increased risk of pulmonary injuries. In addition, the type of pulmonary injury could be predicted from the number of rib fractures in blunt chest trauma.

Effect of different helmets against ground impact based on the in-depth reconstruction of electric two-wheeler accidents

Skull fracture and brain injury are frequent head injuries in electric two-wheeler (ETW) accidents, and the type of helmet and impact conditions affect the effectiveness of the helmet in protecting the rider's head. The purpose of this study was to conduct in-depth reconstructions of rider's head-to-ground impacts in ten ETW accidents by using a multi-body system combined with a finite element approach and to evaluate the effect of two typical full-face helmets (FFH) and one half-coverage helmet (HCH) through head accelerations and intracranial biomechanics injury metrics in ground impacts. The results showed that all three helmets reduced the risk of skull fracture in most cases, however, FFH performed better due to its wider protection area. In addition, three helmets showed varying degrees of overall reduction in measuring all indicators of brain injury. Although the effectiveness of the helmets on angular acceleration was largely influenced by the angle and location of impact, it was certain that wearing an FFH was more likely to reduce rotational head movements than an HCH, and that the FFH also offered the better advantage in reducing diffuse axonal injury (DAI) risk due to its better resistance to ejection in a crash.

Mapping the knowledge of traffic collision Reconstruction: A scientometric analysis in CiteSpace, VOSviewer, and SciMAT

Traffic collisions are incidents with high fatality rate which generate billions of US dollars of loss worldwide each year. Post-collision scene reconstruction, which involves knowledge of multiple disciplines, is an important approach to restore the traffic collision and infer the cause of it. This paper uses software CiteSpace, VOSviewer, and SciMAT to conduct a visualization study of knowledge mapping on the literature of traffic collision scene reconstruction from 2001 to 2021 based on the Web of Science database. Knowledge mapping is a cutting-edge research method in scientometric, which has been widely applied in medicine and informatics. Compared with traditional literature review, knowledge mapping with visual techniques identifies hot keywords and key literature in the field more scientifically, and displays them in schematic diagrams intuitively which allows to further predict potential hotspots. A total of 803 original papers are retrieved to analyze and discuss the evolution of the field in the past 20 years, from macro to micro, in term of background information, popular themes, and knowledge structure. Results indicate the number of publications in this field is limited, and collaborations among authors and among institutions are insufficient. In the meantime, mappings imply the top three hot themes being scene reconstruction, computer technology, and injuries. The introduction of AI related technologies, such as neural networks and genetic algorithms, into collision reconstruction would be a potential research direction.

Investigation on the Modeling and Reconstruction of Head Injury Accident Using ABAQUS/Explicit

A process of modeling and reconstructing human head injuries involved in traffic crashes based on ABAQUS/Explicit is presented in this paper. A high-fidelity finite element (FE) model previously developed by the authors is employed to simulate a real accident case that led to head injury. The most probable head impact position informed by CT images is used for the FE modeling and simulation since the head impact position is critical for accident reconstruction and future analysis of accidents that involve human head injuries. Critical von Mises stress on the skull surface of the head model is chosen as the evaluation criterion for the head injury and FE simulations on 60 cases with various human head-concrete ground impact conditions (impact speeds and angles) were run to obtain those stress values. The FE simulation results are compared with the CT images to determine the minimum speed that will cause skull fracture and the corresponding contact angle at that speed. Our study shows that the minimum speed that would cause skull fracture is 3.5 m/s when the contact angle between the occipital position of the injured head and the ground is about 30°. Effects of the impact speed and the contact angle on the maximum von Mises stress of the head model are revealed from the simulations. The method presented in this paper will help forensic pathologists to examine the head impact injuries and find out the real reasons that lead to those injuries.

Analysis of fall kinematics and injury risks in ground impact in car-pedestrian collisions using impulse

In vehicle-to-pedestrian collisions, pedestrian injuries occur due to contact with the car and the ground. Previous studies investigated pedestrian kinematic behavior using a parameter study or through statistical analysis although the force interaction between the pedestrian and the vehicle has not been considered. In this study, multibody analyses were conducted for vehicle-pedestrian collisions for adult and child pedestrian with various vehicle shapes. The impulse and impulse moment acting on the pedestrian from the vehicle were introduced, and the kinematic behavior, rotation and ground impact of the pedestrian model were examined. It was found that if an impulse moment acts on the pedestrian when the pedestrian re-contacts with the hood of the car, the angular velocity of the pedestrian's torso changes in the opposite direction (away from the car), and the torso angle prior to the ground contact decreases to less than 90°. This re-contact between the pedestrian and the vehicle was more likely to occur for cases where the collision involves an adult pedestrian, lower hood leading edge (HLE), longer hood length, and lower collision velocity. When the pedestrian torso angle in contact with the ground was less than 90°, the head vertical impact velocity with respect to the ground became less than 2.9 m/s which corresponds to the injury threshold of the head. This study demonstrated that pedestrian-vehicle re-contact is crucial for reducing ground injury. The vehicle shape, pedestrian size, and collision velocity can determine whether re-contact of the pedestrian with the vehicle occurs. This can then explain the factors affecting pedestrian ground impact injury (e.g., higher HLE, higher risk of ground head injury for children) that were shown in previous studies. A strategy to mitigate ground injury is to apply enough impulse moment onto the pedestrian's upper body from the hood in order to change the torso angular velocity during re-contact, thus making the torso angle less than 90°prior to the ground contact.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts

In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s² led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.

Evaluation of Urban Traffic Accidents Based on Pedestrian Landing Injury Risks

In comparison with vehicle-to-pedestrian collision, pedestrian-to-ground contact usually results in more unpredictable injuries (e.g., intracranial, neck, and abdominal injuries). Although there are many studies for different applications of such methods, this paper conducts an in-depth analysis of urban traffic pedestrian accidents. The effects of pedestrian rotation angle (PRA) and pedestrian facing orientation (PFO) on head and neck injury risk in a ground contact are investigated by the finite element numerical models and different probabilistic analyses. It goes without saying that this study provides a theoretical basis for the prediction and protection study of pedestrian ground contact injury risk. In our experiments, 24 pedestrian-to-ground simulations are carried out by the THUMS v4.0.2 model considering eight PRAs (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, 360°) and three PFOs (x+, x−, y+). Each test was simulated with loading the average linear and rotational velocities that obtained from real-world pedestrian accidents at the pedestrian’s center of gravity. The results show that both PRAs and PFOs have significant impacts on head and neck injuries. Head HIC value caused by PRA 0–135° is much higher than that caused by PRA 180–315°. Neck injury risk caused by PRA 180° is the greatest one in comparison with other PRAs. The PRAs 90° and 270° usually induce a relatively lower neck injury risk. For PFO, the risk of head and neck injury was lower than PFOy+ and PFOx+ or PFOx−, which means PFOy+ was a safer landing orientation for both head and neck. The potential risk of head and neck injuries caused by the ground contact was strongly associated with the symmetry/asymmetric features of human anatomy.

Impact and workload are dominating on-field data monitoring techniques to track health and well-being of team-sports athletes

Participation in sports has become an essential part of healthy living in today's world. However, injuries can often occur during sports participation. With advancements in sensor technology and data analytics, many sports have turned to technology-aided, data-driven, on-field monitoring techniques to help prevent injuries and plan better player management. This review searched three databases, Web of Science, IEEE, and PubMed, for peer-reviewed articles on on-field data monitoring techniques that are aimed at improving the health and well-being of team-sports athletes. It was found that most on-field data monitoring methods can be categorized as either player workload tracking or physical impact monitoring. Many studies covered during this review attempted to establish correlations between captured physical and physiological data, as well as injury risk. In these studies, workloads are frequently tracked to optimize training and prevent overtraining in addition to overuse injuries, while impacts are most often tracked to detect and investigate traumatic injuries. This review found that current sports monitoring practices often suffer from a lack of standard metrics and definitions. Furthermore, existing data-analysis models are created on data that are limited in both size and diversity. These issues need to be addressed to create ecologically valid approaches in the future.

An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations

Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes. First, an automatic operation framework, which can drive the MADYMO program and perform results analysis automatically, was built with four multi-objective optimization algorithms available - NSGA-Ⅱ, NCGA, AMGA, and MOPS; The optimization condition was controlled with 12 design variables, 5 objective functions, and 3 constraints. Then, a real e-bike accident with surveillance video was reconstructed through the proposed framework to verify its validity using comparisons of simulated and actual rest positions, initial variables, kinematic response, and head injury. Lastly, the simulation data were used to study the effects of the initial variables on objectives with a multiple linear regression model. The results showed that it took only about 24 h in total for optimization with 480 automatic operations. Optimal conditions were searched at run numbers of 469, 430, 323, and 474 for NSGA-Ⅱ, NCGA, AMGA, and MOPS, respectively. NSGA-Ⅱ had the best performance for e-bike accident reconstruction with average errors of objectives below 5%; Good consistencies for the rider's kinematic response in three stages after collision were observed between simulations and screenshots from the surveillance video, as well as for velocities between the simulation and those estimated from the surveillance video and for head injury between the simulation and the medical report. In contrast to the subjective trial-and-error method that highly depends on the analyst's intuition and experience, this intelligent method is based on multi-objective optimization theory, with which results can be optimized in terms of the automatic change of initial variables. All the above comparisons demonstrate that the method is valid for effectively improving efficiency without simultaneously compromising accuracy. This intelligent method, coupling automatic simulation and multi-objective optimization, can also be applied to other accident reconstructions, and the significant order of initial variables' effects on objectives can provide recommendations for further reconstructions.

Modeling small remotely piloted aircraft system to head impact for investigating craniocerebral response

Understanding small remotely piloted aircraft system (sRPAS) to human head impacts is needed to better protect human head during sRPAS ground collision accidents. Recent literature reported cadaveric data on sRPAS to human head impacts, which provided a unique opportunity for developing validated computational models. However, there lacks an understanding of skull stress and brain strain during these impacts. Meanwhile, how slight changes in sRPAS impact setting could affect human head responses remains unknown. Hence, a representative quadcopter style sRPAS finite element (FE) model was developed and applied to a human body model to simulate a total of 45 impacts. Among these 45 simulations, 17 were defined according to cadaveric setting for model validation and the others were conducted to understand the sensitivity of impact angle, impact location, and impacted sRPAS components. Results demonstrated that FE-model-predicted head linear acceleration and rotational velocity agreed with cadaveric data with average predicted linear acceleration 4.5% lower than experimental measurement and average predicted of rotational velocity 2% lower than experimental data. Among validated simulations, high skull stresses and moderate level of brain strains were observed. Also, sensitivity study demonstrated significant effect of impact angle and impact location with 3° variation inducing 30% changes in linear acceleration and 29% changes in rotational velocity. Arm-first impact was found to generate more than two times higher skull stresses and brain strains compared to regular body-shell-first impact.

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head-neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson's correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

The Head AIS 4+ Injury Thresholds for the Elderly Vulnerable Road User Based on Detailed Accident Reconstructions

Compared with the young, the elderly (age greater than or equal to 60 years old) vulnerable road users (VRUs) face a greater risk of injury or death in a traffic accident. A contributing vulnerability is the aging processes that affect their brain structure. The purpose of this study was to investigate the injury mechanisms and establish head AIS 4+ injury tolerances for the elderly VRUs based on various head injury criteria. A total of 30 elderly VRUs accidents with detailed injury records and video information were selected and the VRUs’ kinematics and head injuries were reconstructed by combining a multi-body system model (PC-Crash and MADYMO) and the THUMS (Ver. 4.0.2) FE models. Four head kinematic-based injury predictors (linear acceleration, angular velocity, angular acceleration, and head injury criteria) and three brain tissue injury criteria (coup pressure, maximum principal strain, and cumulative strain damage measure) were studied. The correlation between injury predictors and injury risk was developed using logistical regression models for each criterion. The results show that the calculated thresholds for head injury for the kinematic criteria were lower than those reported in previous literature studies. For the brain tissue level criteria, the thresholds calculated in this study were generally similar to those of previous studies except for the coup pressure. The models had higher (>0.8) area under curve values for receiver operator characteristics, indicating good predictive power. This study could provide additional support for understanding brain injury thresholds in elderly people.

The effect of large deformation on Poisson's ratio of brain white matter: An experimental study

A more Accurate description of the mechanical behavior of brain tissue could improve the results of computational models. While most studies have assumed brain tissue as an incompressible material with constant Poisson's ratio of almost 0.5 and constructed their modeling approach according to this assumption, the relationship between this ratio and levels of applied strains has not yet been studied. Since the mechanical response of the tissue is highly sensitive to the value of Poisson's ratio, this study was designed to investigate the characteristics of the Poisson's ratio of brain tissue at different levels of applied strains. Samples were extracted from bovine brain tissue and tested under unconfined compression at strain values of 5%, 10%, and 30%. Using an image processing method, the axial and transverse strains were measured over a 60-s period to calculate the Poisson's ratio for each sample. The results of this study showed that the Poisson's ratio of brain tissue at strain levels of 5% and 10% was close to 0.5, and assuming brain tissue as an incompressible material is a valid assumption at these levels of strain. For samples under 30% compression, this ratio was higher than 0.5, which could suggest that under strains higher than the brain injury threshold (approximately 18%), tissue integrity was impaired. Based on these observations, it could be concluded that for strain levels higher than the injury threshold, brain tissue could not be assumed as an incompressible material, and new material models need to be proposed to predict the material behavior of the tissue. In addition, the results showed that brain tissue under unconfined compression uniformly stretched in the transverse direction, and the bulging in the samples is negligible.

Structural Anisotropy vs. Mechanical Anisotropy: The Contribution of Axonal Fibers to the Material Properties of Brain White Matter

Brain's micro-structure plays a critical role in its macro-structure material properties. Since the structural anisotropy in the brain white matter has been introduced due to axonal fibers, considering the direction of axons in the continuum models has been mediated to improve the results of computational simulations. The aim of the current study was to investigate the role of fiber direction in the material properties of brain white matter and compare the mechanical behavior of the anisotropic white matter and the isotropic gray matter. Diffusion tensor imaging (DTI) was employed to detect the direction of axons in white matter samples, and tensile stress-relaxation loads up to 20% strains were applied on bovine gray and white matter samples. In order to calculate the nonlinear and time-dependent properties of white matter and gray matter, a visco-hyperelastic model was used. The results indicated that the mechanical behavior of white matter in two orthogonal directions, parallel and perpendicular to axonal fibers, are significantly different. This difference indicates that brain white matter could be assumed as an anisotropic material and axons have contribution in the mechanical properties. Also, up to 15% strain, white matter samples with axons parallel to the force direction are significantly stiffer than both the gray matter samples and white matter samples with axons perpendicular to the force direction. Moreover, the elastic moduli of white matter samples with axons both parallel and perpendicular to the loading direction and gray matter samples at 15-20% strain are not significantly different. According to these observations, it is suggested that axons have negligible roles in the material properties of white matter when it is loaded in the direction perpendicular to the axon direction. Finally, this observation showed that the anisotropy of brain tissue not only has effects on the elastic behavior, but also has effects on the viscoelastic behavior.