Brain Injury Criteria Research Articles

Pediatric intracranial pressure surveillance and quantitative outcomes in kids: (PIPSQK) Study

Objectives Intracranial pressure (ICP) monitoring in pediatric traumatic brain injury (TBI) patients remains controversial. This study aimed to characterize the association between ICP monitoring and mortality for pediatric severe TBI, hypothesizing that ICP monitoring would not be associated with improved survival. Methods Pediatric patients (≤14 years) presenting to Pennsylvania trauma centers from 2003 to 2018 who met severe brain injury criteria (Glasgow Coma Score <9 and Head Abbreviated Injury Scale 4–5) and presented with a blunt mechanism of injury were included. Patients presenting dead on arrival and transfer patients were excluded. Patients were categorized according to ICP monitor placement (ICP+ and ICP−). To assess the effects of ICP monitors on mortality, a multivariate logistic regression model adjusting for demographics and injury patterns was performed. Results A total of 802 pediatric patients met the inclusion criteria. Of those, 28.7% were in ICP+ group ( n = 230) and overall mortality was 33.9% ( n = 272). Despite a higher Injury Severity Score in ICP+ compared to ICP− (33 v. 27, p < 0.001), the mortality rate was lower (22% v. 39%, p < 0.001). The ICP+ group had lower functional statuses at discharge (8 v. 18, p < 0.001). The multivariate logistic regression revealed that presence of an ICP monitor significantly decreased odds of mortality (adjusted odds ratio:0.57, p = 0.027). Conclusion While ICP monitor placement remains controversial in pediatric trauma, there appears to be an association between ICP monitor placement and decreased mortality, despite the ICP monitor group having lower functional status at discharge.

Study on the influence of seating orientation during frontal impacts by child occupant human body model response analysis

Objective Autonomous driving cars must be developed to ensure that children will have the highest level of protection in case of collision. Changes to the vehicle cabin design (different seat orientations, fully reclining seats, etc.) may significantly impact child occupant safety. Understanding child occupant responses under these new conditions is necessary to decrease risk and enhance child safety. In this study, child occupant response in different seating orientations exposed to frontal impacts with a focus on the head injuries and kinematics was analyzed. Methods Finite elements simulations were performed using the PIPER 6-year-old human body model (HBM). All simulations were carried out in a generic full vehicle environment. The child model was positioned in an adequate generic car restraint system (CRS) in the left rear vehicle seat in 4 seating orientations: 0° (forward-facing position), 30°, 60°, and 90° (living room position). Two scenarios were evaluated for all seating orientations according to the left front seat backrest position: reclined position nominal upright and rest position (55°). All seat configurations were subjected to the mobile progressive deformable barrier frontal impact (European New Car Assessment Programme [Euro NCAP] frontal impact testing protocol). A total of 8 scenarios were simulated in LS-DYNA. Results Based on the Euro NCAP injury risk rate, 90° seating orientation (living room position) was the safest among all selected scenarios independent of the left front seat backrest position. The worst case was found in 60° seat rotation. The highest values for Head Injury Criterion (HIC) and head acceleration (Acc 3 ms) were noted for this case. Higher Brain Injury Criterion (BrIC) values were observed at higher seat rotation angles. Hence, a 90° seating orientation showed the highest BrIC value. Attending to the skull stress, greater head injuries were caused principally by contact with the vehicle interior (seat headrest). Maximum stress values were reached at 30° and 60° seating orientations with the front seat in rest position. In 90° seating orientation, high stress values were also identified. Conclusions These results show that attending to these new seating orientations, current child safety standards are not sufficient to ensure children the highest level of protection. Other additional criteria such as BrIC or skull stress that offer a way to capture brain injuries should be used.

Determining rigid body motion from accelerometer data through the square-root of a negative semi-definite tensor, with applications in mild traumatic brain injury

Mild Traumatic Brain Injuries (mTBI) are caused by violent head motions or impacts. Most mTBI prevention strategies explicitly or implicitly rely on a “brain injury criterion”. A brain injury criterion takes some descriptors of the head’s motion as input and yields a prediction for that motion’s potential for causing mTBI as the output. The inputs are descriptors of the head’s motion that are usually synthesized from accelerometer and gyroscope data. In the context of brain injury criterion, the head is modeled as a rigid body. We present an algorithm for determining the complete motion of the head using data from only four head mounted tri-axial accelerometers. In contrast to inertial measurement unit based algorithms for determining rigid body motion, the presented algorithm does not depend on data from gyroscopes, which consume much more power than accelerometers. Several algorithms that also make use of data from only accelerometers already exist. However, those algorithms, except for the recently presented AO (accelerometer-only) algorithm [Rahaman MM, Fang W, Fawzi AL, Wan Y, Kesari H (2020): J Mech Phys Solids 104014], give the rigid body’s acceleration field in terms of the body frame, which in general is unknown. Compared to the AO-algorithm the presented algorithm is much more insensitive to bias type errors, such as those that arise from inaccurate measurement of sensor positions and orientations.

Realistic Reference for Evaluation of Vehicle Safety Focusing on Pedestrian Head Protection Observed From Kinematic Reconstruction of Real-World Collisions.

Head-to-vehicle contact boundary condition and criteria and corresponding thresholds of head injuries are crucial in evaluation of vehicle safety performance for pedestrian protection, which need a constantly updated understanding of pedestrian head kinematic response and injury risk in real-world collisions. Thus, the purpose of the current study is to investigate the characteristics of pedestrian head-to-vehicle contact boundary condition and pedestrian AIS3+ (Abbreviated Injury Scale) head injury risk as functions of kinematic-based criteria, including HIC (Head Injury Criterion), HIP (Head Impact Power), GAMBIT (Generalized Acceleration Model for Brain Injury Threshold), RIC (Rotational Injury Criterion), and BrIC (Brain Injury Criteria), in real-world collisions. To achieve this, 57 vehicle-to-pedestrian collision cases were employed, and a multi-body modeling approach was applied to reconstruct pedestrian kinematics in these real-world collisions. The results show that head-to-windscreen contacts are dominant in pedestrian collisions of the analysis sample and that head WAD (Wrap Around Distance) floats from 1.5 to 2.3 m, with a mean value of 1.84 m; 80% of cases have a head linear contact velocity below 45 km/h or an angular contact velocity less than 40 rad/s; pedestrian head linear contact velocity is on average 83 ± 23% of the vehicle impact velocity, while the head angular contact velocity (in rad/s) is on average 75 ± 25% of the vehicle impact velocity in km/h; 77% of cases have a head contact time in the range 50–140 ms, and negative and positive linear correlations are observed for the relationships between pedestrian head contact time and WAD/height ratio and vehicle impact velocity, respectively; 70% of cases have a head contact angle floating from 40° to 70°, with an average value of 53°; the pedestrian head contact angles on windscreens (average = 48°) are significantly lower than those on bonnets (average = 60°); the predicted thresholds of HIC, HIP, GAMBIT, RIC, BrIC2011, and BrIC2013 for a 50% probability of AIS3+ head injury risk are 1,300, 60 kW, 0.74, 1,470 × 104, 0.56, and 0.57, respectively. The findings of the current work could provide realistic reference for evaluation of vehicle safety performance focusing on pedestrian protection.

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.

Assessment and prediction of acute subdural hematomas in vehicle collision accidents

Acute subdural hematomas (ASDHs) caused by vehicle collisions are common brain injuries that suffers prone to disability and death. However, there is currently no commonly accepted evaluation index for ASDHs, such as the head injury criterion (HIC) for evaluating skull injuries and the brain injury criteria (BrIC, BRIC) for evaluating diffuse axonal injuries. Therefore, we established a set of ASDH indexes (ASDHIs) based on the genetic algorithm for different biomechanical metrics and developed an ASDH prediction model based on an artificial neural network learning (ANN-L). First, simulation models were built using 218 sets of crash test data and the simulated injury monitor model (SIMon). We calculated the correlation coefficients between the kinematic parameters, such as the translational and rotational acceleration, and the biomechanical metrics, such as the intracranial pressure and strain of the bridging vein. The effect of some outlier data is removed using a combination of K-Nearest Neighbor and quadratic regression. We screened some parameters with strong correlations and calculated their weight factors based on the genetic algorithm to establish a set of evaluation indexes for ASDHs. Comparing with 19 existed injury criteria, the results show that each established ASDHI has a higher correlation than these traditional criteria, and also indicate that the evaluation performance of each established ASDHI is advantageous. Finally, on the basis of these established ASDHIs, we built an accurate prediction model of ASDHs using ANN-L, which makes it possible to realize the prediction of ASDHs by only depending on kinematic responses.

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation.

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different.

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Cavitation causes brain injury

In this study, an artificial transparent head surrogate with high-speed photography discovers the formation and collapse of cavitation bubbles near the contrecoup regions as the head is exposed to a sudden translational impact. The cavitation damages the brain surface and produces a shock wave through the brain matter. Based on a novel experimental design, this new finding uncovers the mystery of the motion and deformation of the soft brain matter, which is not visible otherwise. It suggests that current brain injury criteria may underestimate the risk of head collision.

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.

Human brain FE modeling including incompressible fluid dynamics of intraventricular cerebrospinal fluid

Elucidating the mechanisms of mild traumatic brain injuries (mild TBIs), including concussions, is important for developing brain injury criteria and designing head protection devices. Using a finite element (FE) model of the human brain to predict the deformation of the brain parenchyma during a head impact could provide mechanical insights on mild TBIs. However, most conventional brain FE models do not consider how fluid behavior and the perfusion pressure of the cerebrospinal fluid (CSF) will affect brain deformation. This study proposes a novel brain FE model that uses incompressible fluid dynamics (ICFD) to represent the fluid behavior of CSF in the ventricle. In the model with ICFD, the validation accuracy scores on the brain strain during a head impact with a rotational acceleration were significantly higher than those in the model without ICFD. Reconstruction simulations based on two reported mild TBI cases from a rear-end collision and an American football game were conducted using the model with and without ICFD. We found that the maximum principal strain values in the subcortical region and corpus callosum of the model with ICFD were higher and lasted longer than those of the model without ICFD, and this tendency was further enhanced when perfusion pressure was applied. These findings suggested that the fluid behavior and perfusion pressure of the intraventricular CSF could significantly affect the deformation of the brain parenchyma during head impacts. The proposed brain multiphysical FE model could enhance the understanding of mild TBI mechanisms. Statement of SignificanceMild TBIs or concussions can result from brain deformations caused by the rapid acceleration of the head in the situations such as falls, vehicular accidents, and collisions in sports-related activities. A FE analysis is an effective tool for simulating head impact scenarios associated with mild TBIs and estimating the brain strain. Accurate prediction of mild TBIs requires a brain FE model with high biofidelity. Here, we firstly revealed that the validation accuracy of the model on the brain strain can be improved by considering the fluid behavior of intraventricular CSF. By analyzing existing mild TBI cases using the proposed model, the fluid behavior and perfusion pressure of the CSF were found to significantly affect the brain strain history, resulting in an outcome similar to the clinical symptom. The proposed multiphysical brain model could potentially provide new mechanical insights and further understanding of mild TBIs. Additionally, these findings in this study could be useful in developing brain injury criteria and designing protective equipment.

Appropriate Neck Flexion Without Head-hitting Decreases The Thrown Player’S Risk Of Brain Injury In Judo

PURPOSE: Judo is one of the worldwide sports and its competitive athletic events have been held in Olympic Games. Serious brain injuries, however, have been occasionally noted in judo practices and/or competitions. Since the patterns and severities of brain injuries largely depend on the kinematics of the thrown players, it is essential to investigate the mechanism of brain injury from the biomechanical viewpoint in popular throwing techniques. The aim of this study is to evaluate the brain injury criterion (BrIC), an indicator of brain injury, with measuring the presence or absence of head-hitting on the anthropomorphic test device (ATD) in judo throwing technique Ouchi-gari. METHODS: A male judo expert (thrower) threw an ATD for 6 times by Ouchi-gari with or without head-hitting, respectively. The ATD with a straight neck component necessarily hit the occipital region on the mat, whereas an angled component was designed for preventing the ATD’s head-hitting. A 3-axis angular rate sensor, mounted in the gravity center of the ATD’s head, assessed head axial angular velocity in each trial. To evaluate the ATD’s head impact quantitatively, we calculated the BrIC from the head axial angular velocity. High-speed digital video cameras also recorded the kinematics of the ATD’s whole body during trials. RESULTS: In all trials, the largest angular velocities were recorded in the phase of the initial head (with head-hitting) or back (without head-hitting) contacting to the mat. The peak BrIC values of ATD ranged from 0.94 to 2.27 (with head-hitting) and 0.54 to 0.91 (without head-hitting). The average BrIC value with head-hitting (1.62 ± 0.22, mean ± S.E.) trials was significantly higher than those without head-hitting (0.73 ± 0.08) trials (p<0.05). Four out of six trials with head-hitting scored BrIC values larger than 1.0, which implies the human tolerance limit for serious brain injury. However, BrIC values were less than 1.0 in all other trials without head-hitting. CONCLUSIONS: These results suggested that thrown player’s break-fall technique, with appropriate neck flexion to avoid head-hitting, decreases the BrIC value effectively and the risk of serious brain injuries in judo.

Development of a Subhuman Primate Brain Finite Element Model to Investigate Brain Injury Thresholds Induced by Head Rotation.

An anatomically detailed rhesus monkey brain FE model was developed to simulate in vivo responses of the brain of sub-human primates subjected to rotational accelerations resulting in diffuse axonal injury (DAI). The material properties used in the monkey model are those in the GHBMC 50th percentile male head model (Global Human Body Model Consortium). The angular loading simulations consisted of coronal, oblique and sagittal plane rotations with the center of rotation in neck to duplicate experimental conditions. Maximum principal strain (MPS) and Cumulative strain damage measure (CSDM) were analyzed for various white matter structures such as the cerebrum subcortical white matter, corpus callosum and brainstem. The MPS in coronal rotation were 45% to 54% higher in the brainstem, 8% to 48% higher in the corpus callosum, 13% to 22% higher in the white matter when compared to those in oblique and sagittal rotations, suggesting that more severe DAI was expected from coronal and oblique rotations as compared to that from sagittal rotation. The level 1+ DAI was associated with 1.3 to 1.42 MPS and 50% CSDM (0.5) responses in the brainstem, corpus callosum and cerebral white matter. The mass scaling method, sometimes referred to as Holbourn's inverse 2/3 power law, used for development of human brain injury criterion was evaluated to understand the effect of geometrical and anatomical differences between human and animal head. Based on simulations conducted with the animal and human models in three different planes - sagittal, coronal and horizontal - the scaling from animal to human models are not supported due to lack of geometrical similitude between the animal and human brains. Thus, the scaling method used in the development of brain injury criterion for rotational acceleration/velocity is unreliable.

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

The aim of this study was to evaluate the effectiveness of various head injury criteria and associated risk functions in prediction of vulnerable road users (VRUs) severe head injuries caused by ground impact during vehicle collisions. Ten VRU accidents with video information were reconstructed by using Chalmers Pedestrian Model, vehicle multi-body system models and the THUMS (Ver. 4.0.2) finite element model. The head kinematics were used to calculate injury risks for seven head kinematics-based criteria: head angular velocity and acceleration, linear acceleration, head injury criterion (HIC), head impact power (HIP) and two versions of brain injury criterion (i.e., BRIC and BrIC). In addition, the intracranial responses were used to estimate seven tissue injury criteria, Von Mises stress, shear stress, coup pressure (C.P.) and countercoup pressure (CC.P.), maximum principal strain (MPS), cumulative strain damage measure (CSDM), and dilatation damage measure (DDM). A review of the medical reports for all cases indicated that each individual suffered severe head injuries and died. The injury risks predicted through simulations were compared to the head injuries recorded in the medical or forensic reports. The results indicated that 75-100% of the reconstructed ground impact accidents injuries were correctly predicted by angular acceleration, linear acceleration, HIC, C.P., MPS and CSDM0.15. Shear stress, CC.P. and CSDM0.25 correctly predicted 50-75% of the reconstructed accidents injuries. For angular velocity, HIP, BRIC and BrIC, the injuries were correctly predicted for less than 50% of the reconstructed accidents. The Von Mises stress and DDM did not correctly predict any reconstructed accidents injuries. The results could help to understand the effectiveness of the brain injury criteria for future head injury evaluation.

Head injury metric response in finite element ATDs and a human body model in multidirectional loading regimes

Objective: The objective was to quantify head injury metric sensitivity of the 50th percentile male Hybrid III, THOR, and Global Human Body Models Consortium simplified occupant (GHBMC M50-OS) to changes in loading conditions in loading regimes that may be experienced by occupants of spaceflight vehicles or highly autonomous vehicles (HAVs) with nontraditional seating configurations.Methods: A Latin hypercube (LHD) design of experiments (DOE) was employed to develop boundary conditions for 455 unique acceleration profiles. Three previously validated finite element (FE) models of the Hybrid III anthropomorphic test device (ATD), THOR ATD, and GHBMC M50-OS were positioned in an upright 90°-90°-90° seat and with a 5-point belt. Acceleration pulses were applied to each of the three occupants in the ± X, +Y, and ± Z directions, with peak resultant acceleration magnitudes ranging from 5 to 20 G and times to peak ranging from 32.5 to 120.8 ms with duration 250 ms, resulting in 1,248 simulations. Head injury metrics included peak linear head acceleration, peak rotational head acceleration, head injury criteria (HIC15), and brain injury criteria (BrIC). Injury metrics were regressed against boundary condition parameters using 2nd order multiple polynomial regression, and compared between occupants using matched pairs Wilcoxon signed rank analysis.Results: Across the 416 matched-simulations that reached normal termination with all three models, HIC15 values ranged from 1.0–396.5 (Hybrid III), 1.2–327.9 (THOR), and 0.6–585.6 (GHBMC). BrIC ranged from 0.03–0.95 (Hybrid III), 0.03–1.21 (THOR), and 0.04–0.84 (GHBMC). Wilcoxon signed rank analysis demonstrated significant pairwise differences between each of the three occupant models for head injury metrics. For HIC15, the largest divergence between GHBMC and the ATDs was observed in simulations with components of combined underbody and rear impact loading. The three models performed most similarly with respect to BrIC output when loaded in a frontal direction. Both the GHBMC and the Hybrid III produced lower values of BrIC than the THOR on average, with the differences most pronounced in rear impact loading.Conclusion: In conclusion, observed differences between the occupant models’ head injury metric output were quantified. Loading direction had a large effect on metric outcome and metric comparability across models, with frontal and rear impacts with low vertical acceleration tending to be the most similar. One explanation for these differences could be the differences in neck stiffness between the models that allowed more rotation in the GHBMC and THOR. Care should be taken when using ATDs as human volunteer surrogates in these low energy events.

Suitability of enhanced head injury criteria for vehicle rating

Objective: Euro NCAP is considering the implementation of a new head injury assessment with the introduction of THOR in the mobile progressive deformable barrier frontal impact crash test. The objective of this study is to assess the suitability of enhanced head injury criteria for practical application in consumer rating programs.Method: AIS2+ risk predictions from nine selected head injury criteria where calculated for 27 pairs of crash test results representing small and moderate overlap frontal crashes. The capability of each injury criteria to predict the real-world injury rates of these crash modes was evaluated. Next, the correlation coefficients between the head injury candidates were calculated and individual predictions were compared for all tests in scatter plots.Results: The results show that none of the crash tests head injury assessment predicted the four-times higher head injury rates observed in the accident data for small overlap crashes compared to moderate overlap crashes. Poor correlation was demonstrated between many leading brain injury metrics, and the risk predictions for individual vehicles differ quite substantially depending on the criterion considered. Conclusions: While preliminary, the results of this study demonstrate that more evaluation of the most suitable brain injury criteria is necessary before consideration into a consumer evaluation program. Convergence of the head injury criteria risks for individual cases should be part of the validation process for enhanced head injury criteria, since identical head signals should yield similar injury risks.

Sensitivity Analysis and Interval Multi-Objective Optimization for an Occupant Restraint System Considering Craniocerebral Injury

Abstract In vehicle collision accidents, an occupant restraint system (ORS) is crucial to protect the human body from injury, and it commonly involves a large number of design parameters. However, it is very difficult to quantify the importance of design parameters and determine them in the ORS design process. Therefore, an approach of the combination of the proposed approximate sensitivity analysis (SA) method and the interval multi-objective optimization design is presented to reduce craniocerebral injury and improve ORS protection performance. First, to simulate the vehicle collision process and obtain the craniocerebral injury responses, the integrated finite element model of vehicle-occupant (IFEM-VO) is established by integrating the vehicle, dummy, seatbelt, airbag, etc. Then, the proposed approximate SA method is used to quantify the importance ranking of design parameters and ignore the effects of some nonessential parameters. In the SA process, the Kriging metamodel characterizing the relationships between design parameters and injury responses is fitted to overcome the time-consuming disadvantage of IFEM-VO. Finally, according to the results of SA, considering the influence of uncertainty, an interval multi-objective optimization design is implemented by treating the brain injury criteria (BRIC, BrIC) as the objectives and regarding the head injury criterion (HIC) and the rotational injury criterion (RIC) as the constraints. Comparison of the results before and after optimization indicates that the maximum values of the translational and rotational accelerations are greatly reduced, and the ORS protection performance is significantly improved. This study provides an effective way to improve the protection performance of vehicle ORS under uncertainty.

Head and Neck Response of an Active Human Body Model and Finite Element Anthropometric Test Device During a Linear Impactor Helmet Test.

It has been proposed that neck muscle activation may play a role in head response resulting from impacts in American Football. The importance of neck stiffness and active musculature in the standard linear impactor helmet test was assessed using a detailed head and neck finite element (FE) model from a current human body model (HBM) compared to a validated hybrid III head and neck FE model. The models were assessed for bare-head and helmeted impacts at three speeds (5.5, 7.4, and 9.3 m/s) and three impact orientations. The HBM head and neck was assessed without muscle activation and with a high level of muscle activation representing a braced condition. The HBM and hybrid III had an average cross-correlation rating of 0.89 for acceleration in the primary impact direction, indicating excellent correspondence regardless of muscle activation. Differences were identified in the axial head acceleration, attributed to axial neck stiffness (correlation rating of 0.45), but these differences did not have a large effect on the overall head response using existing head response metrics (head injury criteria, brain injury criteria, and head impact power). Although responses that develop over longer durations following the impact differed slightly, such as the moment at the base of the neck, this occurred later in time, and therefore, did not considerably affect the short-term head kinematics in the primary impact direction. Though muscle activation did not play a strong role in the head response for the test configurations considered, muscle activation may play a role in longer duration events.

Validation of a Football Helmet Finite Element Model and Quantification of Impact Energy Distribution.

Head injury in contact sports can be mitigated, in part, through the enhancement of protective helmets that may be enabled by detailed finite element models. However, many contemporary helmet FE models include simplified geometry and material properties and have limited verification and validation over a representative range of impact conditions. To address these limitations, a detailed numerical model of a modern football helmet was developed, integrated with two headforms and assessed for 60 impact conditions with excellent ratings (0.79-0.93). The strain energy of the helmet components was investigated for eight impact locations and three impact speeds. In general, the helmet shell had the highest strain energy followed by the compression shocks; however, the facemask and straps had the highest strain energy for impacts involving the facemask. The component strain energy was positively correlated with the Head Injury Criterion, while the strain energy was not strongly correlated with the Brain Injury Criterion due to the dependence on rotational kinematics. This study demonstrated the applicability of a detailed football helmet finite element model to investigate a range of impact conditions and to assess energy distribution as a function of impact location and severity as a means of future helmet optimization.

Proposing a Radial Basis Function and CSDM Indices to Predict the Traumatic Brain Injury Risk

BackgroundMany head injury indices and finite element (FE) head models have been proposed to predict traumatic brain injury (TBI). Although FE head models are suitable methods with high accuracy, they are computationally intensive. Head motion-based brain injury criteria are usually fast tools with lower accuracy. So, the objective of this study is to propose new criteria along with an artificial neural network model to predict TBI risks, which can be fast and accurate. MethodsFor this purpose, 250 FE head simulations have been carried out at 5 magnitudes and 50 rotational impact directions using the SIMon model. The effects of directions and magnitudes of rotational impacts were assessed for cumulative strain damage measure (CSDM) values. Next, statistical analysis and neural network were applied to predict CSDM values. ResultsThe results of the present research showed that the direction of rotation in the sagittal and frontal planes had a considerable effect on the CSDM values. Furthermore, new brain injury indices and a radial basis function neural network have been proposed to predict CSDM values which having high correlation coefficients with SIMon responses. ConclusionsThe results of this research demonstrated that rotational impact directions should be used to develop new head injury criteria being able to predict CSDM values. However, findings of present research proved that head motion-based brain injury criteria and RBF network can be used to predict FE head model responses with high speed and accuracy.