Cumulative Strain Damage Measure Research Articles

Helmets with lattice liners can mitigate traumatic brain injury from impacts

This study explores the effectiveness of architected lattice structures, specifically made of PA12 material, as potential helmet liners to mitigate traumatic brain injuries (TBI), with a focus on rotational acceleration. Evaluating three lattice unit cell topologies (simple cubic, dode-medium, and rhombic dodecahedron), the research builds upon prior investigations indicating that PA12 lattice liners may outperform conventional EPS liners. Employing a high-fidelity finite element male head model and utilizing direct and oblique impact scenarios, mechanical quantities, such as maximum principal strain (MPS) and shear strain, cumulative strain damage measure and intracranial pressure were measured at the tissue level in different brain regions, Results indicate that lattice liners, especially with dode-medium topology, exhibit promising reductions in brain tissue strains. On average, during oblique impacts, less than 1 % of the brain volume experienced an MPS level of 0.4 when the lattice liners were adopted, whereas that percentage was above 70 % with the expandable polystyrene (EPS) foam liners. Pressure-based assessments suggest that lattice liners may outperform EPS liners in oblique impacts, showcasing the limitations of EPS for effective TBI mitigation. Despite certain model limitations, this study emphasizes the need for advancements in helmet technology, particularly in the development of commercial lattice liners using additive manufacturing, to address the limitations of existing EPS liners in preventing rotational consequences of impacts and reducing TBI.

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

PurposeThe 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. MethodsBased 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 were 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. ResultsThere 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. ConclusionThe 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.

Investigation of Head Kinematics and Brain Strain Response During Soccer Heading Using a Custom-Fit Instrumented Mouthguard

Association football, also known as soccer in some regions, is unique in encouraging its participants to intentionally use their head to gain a competitive advantage, including scoring a goal. Repetitive head impacts are now being increasingly linked to an inflated risk of developing long-term neurodegenerative disease. This study investigated the effect of heading passes from different distances, using head acceleration data and finite element modelling to estimate brain injury risk. Seven university-level participants wore a custom-fitted instrumented mouthguard to capture linear and angular acceleration-time data. They performed 10 headers within a laboratory environment, from a combination of short, medium, and long passes. Kinematic data was then used to calculate peak linear acceleration, peak angular velocity, and peak angular acceleration as well as two brain injury metrics: head injury criterion and rotational injury criterion. Six degrees of freedom acceleration-time data were also inputted into a widely accepted finite element brain model to estimate strain-response using mean peak strain and cumulative strain damage measure values. Five headers were considered to have a 25% concussion risk. Mean peak linear acceleration equalled 26 ± 7.9 g, mean peak angular velocity 7.20 ± 2.18 rad/s, mean peak angular acceleration 1730 ± 611 rad/s2, and 95th percentile mean peak strain 0.0962 ± 0.252. Some of these data were similar to brain injury metrics reported from American football, which supports the need for further investigation into soccer heading.

Strain Response ofan Anatomically Accurate Nonhuman Primate Finite Element Brain Model Under Sagittal Loading.

Prevention and treatment of traumatic brain injuries is critical to preserving soldier brain health. Laboratory studies are commonly used to reproduce injuries, understand injury mechanisms, and develop tolerance limits; however, this approach has limitations for studying brain injury, which requires a physiological response. The nonhuman primate (NHP) has been used as an effective model for investigating brain injury for many years. Prior research using the NHP provides a valuable resource to leverage using modern analysis and modeling techniques to improve our understanding of brain injury. The objectives of the present study are to develop an anatomically accurate finite element model of the NHP and determine regional brain responses using previously collected NHP data. The finite element model was developed using a neuroimaging-based anatomical atlas of the rhesus macaque that includes both cortical and subcortical structures. Head kinematic data from 10 sagittal NHP experiments, four +Gx (rearward) and six -Gx (frontal), were used to test model stability and obtain brain strain responses from multiple severities and vectors. For +Gx tests, the whole-brain cumulative strain damage measure exceeding a strain threshold of 0.15 (CSDM15) ranged from 0.28 to 0.89, and 95th percentile of the whole-brain maximum principal strain (MPS95) ranged from 0.21 to 0.59. For -Gx tests, whole-brain CSDM15 ranged from 0.02 to 0.66, and whole-brain MPS95 ranged from 0.08 to 0.39. Recognizing that NHPs are the closest surrogate to humans combined with the limitations of conducting brain injury research in the laboratory, a detailed anatomically accurate finite element model of an NHP was developed and exercised using previously collected data from the Naval Biodynamics Laboratory. The presently developed model can be used to conduct additional analyses to act as pilot data for the design of newer experiments with statistical power because of the sensitivity and resources needed to conduct experiments with NHPs.

Regional Strain Response of an Anatomically Accurate Human Finite Element Head Model Under Frontal Versus Lateral Loading.

Because brain regions are responsible for specific functions, regional damage may cause specific, predictable symptoms. However, the existing brain injury criteria focus on whole brain response. This study developed and validated a detailed human brain computational model with sufficient fidelity to include regional components and demonstrate its feasibility to obtain region-specific brain strains under selected loading. Model development used the Simulated Injury Monitor (SIMon) model as a baseline. Each SIMon solid element was split into 8, with each shell element split into 4. Anatomical regions were identified from FreeSurfer fsaverage neuroimaging template. Material properties were obtained from literature. The model was validated against experimental intracranial pressure, brain-skull displacement, and brain strain data. Model simulations used data from laboratory experiments with a rigid arm pendulum striking a helmeted head-neck system. Data from impact tests (6 m/s) at 2 helmet sites (front and left) were used. Model validation showed good agreement with intracranial pressure response, fair to good agreement with brain-skull displacement, and good agreement for brain strain. CORrelation Analysis scores were between 0.72 and 0.93 for both maximum principal strain (MPS) and shear strain. For frontal impacts, regional MPS was between 0.14 and 0.36 (average of left and right hemispheres). For lateral impacts, MPS was between 0.20 and 0.48 (left hemisphere) and between 0.22 and 0.51 (right hemisphere). For frontal impacts, regional cumulative strain damage measure (CSDM20) was between 0.01 and 0.87. For lateral impacts, CSDM20 was between 0.36 and 0.99 (left hemisphere) and between 0.09 and 0.93 (right hemisphere). Recognizing that neural functions are related to anatomical structures and most model-based injury metrics focus on whole brain response, this study developed an anatomically accurate human brain model to capture regional responses. Model validation was comparable with current models. The model provided sufficient anatomical detail to describe brain regional responses under different impact conditions.

Regional brain strain dependance on direction of head rotation

Brain injuries in automated vehicles during crash events are likely to include mechanisms of head impact in non-standard positions and postures (i.e., occupants not facing forward in an upright position). Federal regulations currently focus on impact conditions in primary planes of motion, such as frontal or rear impacts (sagittal plane of motion) or side impact (coronal plane of motion) and do not account for out of position occupants or non-standard postures. The objective of the present study was to develop and use the anatomically accurate brain finite element model to parametrically determine the injury metrics under different vectors with head rotation. A custom developed brain finite element model with anatomical accuracy and several anatomical regions defined was used to evaluate whole-brain strain as well as regional brain strain. Cumulative Strain Damage Measure (CSDM) at a threshold of 20% strain and the 95th percentile of the maximum principal strain (MPS95) were calculated for the whole brain and each brain region under multiple rotational directions. The model was exposed to a sinusoidal angular acceleration pulse of 5000 rad per second squared (rad/s2–) over 12.5 ms. The same pulse was used in the primary axes of motion and (lateral bending, flexion, extension, axial rotation) and combined axes representing oblique flexion and oblique extension. Whole brain CSDM20 was highest for lateral bending. Whole brain MPS95 was highest for axial rotation. The rCSDM20 was more susceptible to impact direction, with several brain regions having substantial accumulation of strain for oblique flexion and lateral bending. Comparatively, rMPS95 was more consistent across all rotation directions. The present study quantified the regional brain strain response under multiple rotational vectors identifying a high amount of variability in the accumulation of strain (i.e., CSDM20) in the hypothalamus, hippocampus, and midbrain specifically. While there was a high amount of variability in the accumulation of strain for multiple regions, the maximum strain measured (i.e., MPS95) in the regions was more consistent.

Evaluation of Brain Rotational Injury Criteria (BrIC) in vehicle frontal crashes

Objective The objective of this study was to estimate strains in the human brain in regulatory, research, and due care frontal crashes by simulating those impacts. In addition, brain strain simulations were estimated for belted human volunteer tests and in impacts between two players in National Football League (NFL), some with no injury and some with mild Traumatic Brain Injuries (mTBI). Methods The brain strain responses were determined using version 5 of the Global Human Body Modeling Consortium (GHBMC) 50th percentile human brain model. One hundred and sixty simulations with the brain model were conducted using rotational velocities and accelerations of Anthropomorphic Test Devices (ATD’s) or those of human volunteers in sled or crash tests, as inputs to the model and strain related responses like Maximum Principal Strains (MPS) and Cumulative Strain Damage Measure (CSDM) in various regions of the brain were monitored. The simulated vehicle tests ranged from sled tests at 24 and 32 kph delta-V with three-point belts without airbags to full scale crash and sled tests at 56 kph and a series of Research Mobile Deformable Barrier (RMDB) tests described in Prasad et al. Results The severity of rotational input into the model as represented by BrIC, averaged between 0.5 and 1.2 for the various test conditions, and as high as 1.5 for an individual case. The MPS responses for the various test conditions averaged between 0.28 and 0.86 and as high as 1.3 in one test condition. The MPS responses in the brain for volunteers, low velocity sled, and NCAP tests were similar to those in the no-mTBI group in the NFL cases and consistent with real world accident data. The MPS responses of the brain in angular crash and sled tests were similar to those in the mTBI group. Conclusions The brain strain estimations do not indicate the likelihood of severe-to-fatal brain injuries in the crash environments studied in this paper. However, using the risk functions associated with BrIC, severe-to-fatal brain injuries (AIS4+) are predicted in several environments in which they are not observed or expected.

Effects of the windshield inclination angle on head/brain injuries in car-to-pedestrian collisions using computational biomechanics models

Abstract Car-to-pedestrian collision (CPC) accidents occur frequently, and pedestrians often suffer serious head/brain injuries. One major cause is the primary impact with the windshield. Here, we use a numerical simulation method to study the influence of the windshield inclination angle of a passenger car on pedestrian head/brain injury due to CPC accidents. The range of the windshield inclination angle was set at 24°–50°, with an interval of 2°. The results show that the windshield angle significantly affects the pedestrian kinematics and exerts different effects on the head injury when evaluating with various head injury criteria. Regarding the head peak linear/rotational acceleration and acceleration-based head injury criterion (HIC)/rotational injury criterion (RIC), the predictions at the secondary impact stage have no clear relationship with the windshield angle (R2=0.04, 0.07, 0.03 and 0.26, respectively) and their distributions are scattered. In the primary impact, the peak linear acceleration and HIC show a weak trend of first decreasing and then increasing with the increasing of the windshield angle, and the rotational acceleration and RIC tend to remain relatively constant. Regarding the cumulative strain damage measure (CSDM) criterion, the predictions at the primary impact are slightly lower than those at the secondary impact, and the trend of first decreasing and then increasing with the increase in the windshield angle is observed at both impact stages. When the windshield inclination angle is approximately 32°–40°, the head injury severity in both impact phases is generally lower than that predicted at other windshield angles.

A modified finite element dummy model of Chinese adult male used for train collision simulations

ABSTRACT This work established a modified finite element (FE) dummy model representing the Chinese 50th percentile males through the whole segment scaling method, referring to the Total Human Model for Safety (THUMS-AM50) of European and American 50th percentile males. The head, neck, chest, abdomen, and knee-thigh-hip (KTH) of the modified model were validated, respectively, by the cadaver experimental results. Integrating the dummy models with a four-car marshalling train collision FE model, we analysed the dynamic response in terms of human body posture, head, and neck injuries of the Chinese 50th percentile males and the European and American 50th percentile males during the frontal train collision process. The corresponding simulation results fitted well with the experimental tests, indicating that the established modified model exerted great biological fidelity. Compared with European-American 50th percentile males, the Von-Mises stress, shear stress, maximum principal strain (MPS), and the cumulative strain damage measure CSDM0.15 of the brain in the Chinese 50th percentile males were approximately higher by 22.21%, 19.85%, 36.58%, and 47.55%, respectively. That meant the Chinese males might be at a higher risk of brain contusion and diffuse axonal injury (DAI) during the train collision. Meanwhile, the Chinese 50th percentile males had more severe neck extension, especially the maximum cervical vertebrae Von-Mises stress and neck injury predictor (Nij) were increased by 30.3% and 50%, respectively. The established modified model could provide a more accurate simulation for predicting the Chinese body injury response during the train collision.

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 4706rad/s2 led to the highest piecewise linearity. This study may contribute to an improved method for therapid prediction of CSDM in the future.

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future.The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning.

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R2) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R2 of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

The difference in the kinematic and injury risk of cyclists between normal and emergency avoidance postures in vehicle collisions

To investigate the global kinematic differences between normal and emergency avoidance postures of cyclists at different crash speeds and the risk of head and lower extremity injuries during vehicle crashes. Seven postures (three normal and four emergency avoidance postures) were selected based on video information from 200 bicycle accidents in the VRU-Traffic Accident Video (VRU-TRAVi) database. Fourteen simulations were performed using the Total Human Safety Model (THUMS) 4.0 occupant model considering the seven postures and two-vehicle impact speeds (20 and 40 km/h). The global kinematics of the cyclists, as well as head injury parameters such as peak linear acceleration, head injury criterion (HIC), peak angular acceleration, brain injury criterion (BRIC), maximum principal strain (MPS), cumulative strain damage measure (CSDM), and lower extremity injury parameters such as von Mises stress and bending moment were calculated to assess the risk of head and lower extremity injury. The results showed that the moment of head contact with the vehicle was earlier and the slip distance of the pelvis was shorter in the emergency avoidance postures compared to the normal posture at vehicle speeds of 20 km/h and 40 km/h, which reduced the head wrap-around distance (WAD) and increased the head impact velocity. In addition, the emergency avoidance postures did not significantly reduce the risk of head injury compared to the normal posture; instead, they increased the risk of injury to the lower extremities.

Evaluation of the head protection effectiveness of cyclist helmets using full-scale computational biomechanics modelling of cycling accidents

Introduction: Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist’s head from injury continues. Method: We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist’s head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist’s body) of the cycling accidents for cyclists wearing a helmet and without a helmet. Results: The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS0.98) for helmeted cyclists was predicted for both impact scenarios. In contrast, wearing the helmet reduced the CSDM only for the skidding impact scenario. For the kerb-impact scenario, no clear influence of the helmet on the predicted CSDM was observed.

Header biomechanics in youth and collegiate female soccer

Concerns about the effects of intentional heading in soccer have led to regulatory restrictions on headers for youth players. However, there is limited data describing how header exposure varies across age levels, and few studies have attempted to compare head impact exposure across different levels of play with the same sensor. Additionally, little is known about the biomechanical response of the brain to header impacts. The objective of this study was to evaluate head kinematics and the resulting tissue-level brain strain associated with intentional headers among youth and collegiate female soccer players. Six youth and 13 collegiate participants were instrumented with custom mouthpiece-based sensors measuring six-degree-of-freedom head kinematics of headers during practices and games. Kinematics of film-verified headers were used to drive impact simulations with a detailed brain finite element model to estimate tissue-level strain. Linear and rotational head kinematics and strain metrics, specifically 95th percentile maximum principal strain (ε1,95) and the area under the cumulative strain damage measure curve (VSM1), were compared across levels of play (i.e., youth vs. collegiate) while adjusting for session type and ball delivery method. A total of 483 headers (n = 227 youth, n = 256 collegiate) were analyzed. Level of play was significantly associated with linear acceleration, rotational acceleration, rotational velocity, ε1,95, and VSM1. Headers performed by collegiate players had significantly greater mean head kinematics and strain metrics compared to those performed by youth players (all p < .001). Targeted interventions aiming to reduce head impact magnitude in soccer should consider factors associated with the level of play.

Head Kinematics and Injury Metrics for Laboratory Hockey-Relevant Head Impact Experiments.

Investigating head responses during hockey-related blunt impacts and hence understanding how to mitigate brain injury risk from such impacts still needs more exploration. This study used the recently developed hockey helmet testing methodology, known as the Hockey Summation of Tests for the Analysis of Risk (Hockey STAR), to collect 672 laboratory helmeted impacts. Brain strains were then calculated from the according 672 simulations using the detailed Global Human Body Models Consortium (GHBMC) finite element head model. Experimentally measured head kinematics and brain strains were used to calculate head/brain injury metrics including peak linear acceleration, peak rotational acceleration, peak rotational velocity, Gadd Severity Index (GSI), Head Injury Criteria (HIC15), Generalized Acceleration Model for Brain Injury Threshold (GAMBIT), Brain Injury Criteria (BrIC), Universal Brain Injury Criterion (UBrIC), Diffuse Axonal Multi-Axis General Equation (DAMAGE), average maximum principal strain (MPS) and cumulative strain damage measure (CSDM). Correlation analysis of kinematics-based and strain-based metrics highlighted the importance of rotational velocity. Injury metrics that use rotational velocity correlated highly to average MPS and CSDM with UBrIC yielding the strongest correlation. In summary, a comprehensive analysis for kinematics-based and strain-based injury metrics was conducted through a hybrid experimental (672 impacts) and computational (672 simulations) approach. The results can provide references for adopting brain injury metrics when using the Hockey STAR approach and guide ice hockey helmet designs that help reduce brain injury risks.

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.

Evaluation of brain injury criteria based on reliability analysis

Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. The cumulative strain damage measure and the BrIC provide the highest correlation (R² = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

Influences of impact scenarios and vehicle front-end design on head injury risk of motorcyclist

Motorcycle to vehicle collision is one of the most common accidents in the world and usually leads to serious or fatal head injuries to motorcyclists. This study aims to investigate the influences of impact scenarios and vehicle front-end design parameters on head injury risk of the motorcyclist. Five general vehicle types and different impact scenarios were selected for a parametric analysis. Impact scenarios were set according to ISO, 13232 regulation considering impact angles and impact speeds. Five vehicle types of Sedan, MPV (Multi-Purpose Vehicle), SUV (Sport Utility Vehicle), EV (Electric Vehicle) and 1-Box vehicle were included. HIC15 (Head Injury Criterion), head angular acceleration and CSDM (Cumulative Strain Damage Measure) were calculated to evaluate head injury risk of the motorcyclist. The results show that the critical impact speed for HIC15 and head angular acceleration was around 15 m/s, while the critical speed for CSDM was approximately 10 m/s. Impact angle of 45° show extremely high injury risk to the motorcyclist head. Bonnet leading edge height and its combination with other parameter present high influences on motorcyclist head injuries, and the increasing the bonnet leading edge height can potentially reduce head injury risk of motorcyclists. In summary, the present research results provide some theoretic bases for determining the test speed in motorcycle-vehicle crash regulation and design consideration for typical vehicle front end shape.