Development of brain injury criteria (BrIC)

Laboratory Evaluation of a Wearable Instrumented Headband for Rotational Head Kinematics Measurement

PURPOSE

Mild traumatic brain injuries (mTBI) are a highly prevalent condition with heterogeneous outcomes between individuals. A key factor governing brain tissue deformation and the risk of mTBI is the rotational kinematics of the head. Instrumented mouthguards are a widely accepted method for measuring rotational head motions, owing to their robust sensor-skull coupling. However, wearing mouthguards is not feasible in all situations, especially for long-term data collection. Therefore, alternative wearable devices are needed. In this study, we present an improved design and data processing scheme for an instrumented headband.

METHODS

Our instrumented headband utilizes an array of inertial measurement units (IMUs) and a new data processing scheme based on continuous wavelet transforms to address sources of error in the IMU measurements. The headband performance was evaluated in the laboratory on an anthropomorphic test device, which was impacted with a soccer ball to replicate soccer heading.

RESULTS

When comparing the measured peak rotational velocities (PRV) and peak rotational accelerations (PRA) between the reference sensors and the headband for impacts to the front of the head, the correlation coefficients (r) were 0.80 and 0.63, and the normalized root mean square error (NRMSE) values were 0.20 and 0.28, respectively. However, when considering all impact locations, r dropped to 0.42 and 0.34 and NRMSE increased to 0.5 and 0.41 for PRV and PRA, respectively.

CONCLUSION

This new instrumented headband improves upon previous headband designs in reconstructing the rotational head kinematics resulting from frontal soccer ball impacts, providing a potential alternative to instrumented mouthguards.

The biomechanical injury calculator: a postprocessor software for a finite element human body model

An injury risk assessment postprocessor for the Global Human Body Model Consortium (GHBMC) model is presented. The Biomechanical Injury Calculator (BIC) calculates injury probabilities for the head, neck, spine, and pelvis post-simulation, along with a total injury probability for the entire complex. It also generates an injury heatmap. Developed for the GHBMC M50-OS v2.3 +DeformSpine, BIC was validated by comparing 103 airmen's seat ejection injuries to BIC-predicted injury probabilities in 30 vertical seat load simulations. Observed injury rates correlated strongly with BIC predictions (Spearman=0.943, Pearson=0.982) within 5.16% margin. The total injury probability of 58.48% closely matched the 56.3% observed rate.

Evaluating Polo Helmet Performance Across Different Impact Test Systems

PURPOSE

This study evaluated head impact response between different helmet impact test systems by comparing the performance of ten polo helmets.

METHODS

Helmets were evaluated using three test systems: a twin-wire guided drop tower, an oblique drop tower, and an impact pendulum. Impact tests were conducted at matched locations (front boss, side, rear boss) and speeds (3.46, 5.46 m/s). We employed a linear mixed model with helmet model as a random effect and calculated the least square mean differences between systems for peak linear acceleration (PLA), peak rotational acceleration (PRA), peak rotational velocity (PRV), and concussion risk. Correlations between systems by impact speed were explored, using linear models of each system as a function of the others, and calculated Spearman rank correlation coefficients between test systems for each dependent variable.

RESULTS

Our results found distinct differences in PRA and concussion risk between the oblique and the pendulum impact systems due to the driving force. The acceleration range across helmet models was substantial, and responses differed between test systems at matched impact conditions. However, there were similarities between test systems in the rank order of helmet models. Head acceleration differences between helmets translated to larger differences in concussion risk between helmet models.

CONCLUSION

These trends provide a framework for comparing the headform's response across varying loading conditions. When selecting a test system to evaluate helmets for a specific sport, it is essential to consider the relevant impact conditions and loading patterns to ensure that laboratory tests accurately represent real-world scenarios.

An evaluation of the performance of medical helmets used in healthcare for the protection of vulnerable patients

Introduction

Medical helmets (MHs) are used by individuals with an increased vulnerability to falls and are essentially unregulated in the UK; therefore, their impact performance is unproven. This study investigated the performance of a selection of medical helmets available to clinicians using general techniques to determine their protective performance against impacts. Additionally, clinicians have stated that medical helmets need to consider focal vulnerabilities to impact (often a postsurgical site of a decompressive craniectomy); therefore, novel techniques were specifically employed for measuring the protection of a focal site.

Materials and Methods

A freefall drop test methodology was used to assess six medical helmets (MH1-6) and two sports helmets (SH1 and SH2). The headform was instrumented with six degrees of freedom instrumentation to quantify global kinematics metrics related to injury risk (peak linear acceleration (PLA), peak angular velocity (PAV), peak angular acceleration (PAA), head injury criterion (HIC), and brain injury criterion (BrIC)), and a thin-film contact pressure measurement system was used to quantify the contact area (above a threshold of 560 kPa) focal to the impact. Due to the advanced nature of these measurements, a novel biofidelic headform was used to more accurately represent local deformation. Additionally, impact performance was plotted against two proxy measures of comfort.

Results

The difference in performance between the worst and best helmets ranged from 90% to 2844%, showing a substantial variation. HIC, PLA, and PAA showed the largest range, whereas PAV showed the smallest range. Nonetheless, there was good agreement between each kinematic metric regarding the rank order of the medical helmets. The contact pressure was a consistent outlier. Each metric included at least one injury threshold, which MH4 and MH6 consistently exceeded (15/18 occasions).

Discussion

MH2 and MH3 were the only medical helmets comparable to sports helmets in terms of both comfort and performance. MH1 showed excellent performance metrics but exhibited possible discomfort, while MH4 was above average across both measurement categories. MH4 and MH6 were significantly deficient compared to the sample of helmets. These results highlight the need for standardisation.

Effects of anatomy and head motion on spatial patterns of deformation in the human brain

PURPOSE

To determine how the biomechanical vulnerability of the human brain is affected by features of individual anatomy and loading.

METHODS

To identify the features that contribute most to brain vulnerability, we imparted mild harmonic acceleration to the head and measured the resulting brain motion and deformation using magnetic resonance elastography (MRE). Oscillatory motion was imparted to the heads of adult participants using a lateral actuator (n = 24) or occipital actuator (n = 24) at 20 Hz, 30 Hz, and 50 Hz. Displacement vector fields and strain tensor fields in the brain were obtained from MRE measurements. Anatomical images, as well as displacement and strain fields from each participant were rigidly and deformably aligned to a common atlas (MNI-152). Vulnerability of the brain to deformation was quantified by the ratio of strain energy (SE) to kinetic energy (KE) for each participant. Similarity of deformation patterns between participants was quantified using strain field correlation (CV). Linear regression models were used to identify the effect of similarity of brain size, shape, and age, as well as similarity of loading, on CV.

RESULTS

The SE/KE ratio decreased with frequency and was larger for participants undergoing lateral, rather than occipital, actuation. Head rotation about the inferior-superior axis was correlated with larger SE/KE ratio. Strain field correlations were primarily affected by the similarity of rigid-body motion.

CONCLUSION

The motion applied to the skull is the most important factor in determining both the vulnerability of the brain to deformation and the similarity between strain fields in different individuals.

Brain injury metrics and their risk functions in frontal automotive collisions

OBJECTIVES

The objective of this study was to develop abbreviated injury scale (AIS) 1, AIS2, AIS3 and AIS4+ injury risk functions (IRFs) for traumatic brain injuries (TBIs) as estimated by the rotational kinematics of the head, in accordance with AIS1998. The effectiveness of the IRFs was investigated by comparisons with real-world accident data of frontal crash configurations. In addition, links of the IRFs developed in accordance with AIS1998 to other AIS versions were discussed.

METHODS

AIS1, AIS2, AIS3 and AIS4+ IRFs based on finite element analysis (FEA)-based metrics in this study were developed using a TBI database used for developing mild TBI (concussion) and severe TBI (diffuse axonal injury (DAI) and intracerebral hemorrhage (ICH)) IRFs in our previous study. The TBI database includes head kinematics, clinical outcomes, and FEA-based metrics such as maximum principal strain (MPS) obtained from reconstructions using harmonized species-specific finite element (FE) brain models. In this study, TBI severities in the TBI database were reclassified in accordance with AIS1998 to evaluate IRFs in comparison with field accident data for application to automotive safety. IRFs based on kinematics-based metrics were developed by transforming FEA-based IRFs via linear regression models between the FEA-based and kinematics-based metrics. The FEA-based and kinematics-based IRFs were evaluated by comparing TBI risk predictions using frontal crash test data with real-world TBI rates in similar crash configurations.

RESULTS

The MPS95 IRFs exhibited better quality (lower quality index (QI) values) and better goodness of fit with the TBI database (lower AIC value) among the FEA-based IRFs. Kinematics-based metrics exhibited the greatest coefficients of determination (R2) with MPS95. The accident data evaluation demonstrated that the MPS95 IRFs and kinematics-based IRFs derived from the MPS95 IRFs generally overpredicted most frontal crash configurations, with the full engagement conditions tending to have smaller errors and the oblique crash conditions having the largest overprediction.

CONCLUSIONS

The TBI risks predicted by the MPS95 IRFs and kinematics-based IRFs derived from the MPS95 IRFs were relatively more aligned with the real-world TBI rates for drivers in the full engagement crash configuration. However, further investigations are needed to minimize the gap between predicted TBI risks and real-world TIB rates. In addition, AIS coding of TBIs has changed through version upgrades, especially for concussion. This change in AIS coding has affected IRFs for AIS1 and AIS2. Further revisions of TBI IRFs will be required in the future if the AIS definitions change.

A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations

Police forensic investigations are not immune to our society's ubiquitous search for better predictive ability. In the particular and very topical case of Traumatic Brain Injury (TBI), police forensic investigations aim at evaluating whether a given impact or assault scenario led to the clinically observed TBI. This question is traditionally answered by means of forensic biomechanics and neurosurgical expertise which cannot provide a fully objective probabilistic measure. To this end, we propose here a numerical framework-based solution coupling biomechanical simulations of a variety of injurious impacts to machine learning training of police reports provided by the UK's Thames Valley Police and the National Crime Agency's National Injury Database. In this approach, the biomechanical predictions of mechanical metrics such as strain and stress distributions are interpreted by the machine learning model by additionally considering assault specific metadata to predict brain injury outcomes. The framework, only taking as input information typically available in police reports, reaches prediction accuracies exceeding 94% for skull fracture, 79% for loss of consciousness and intracranial haemorrhage, and is able to identify the best predictive features for each targeted injury. Overall, the proposed framework offers new avenues for the prediction, directly from police reports, of any TBI related symptom as required by forensic law enforcement investigations.

Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we developed deep learning models that predict the peak rotational velocity and acceleration of a dummy head protected by various liner materials. The deep learning models exhibited a remarkable correlation coefficient of 0.99 within the testing dataset with mean absolute error of 0.8 rad.s-1 and 0.6 krad.s-2 respectively, highlighting their predictive ability. Deep learning-based material optimization demonstrated a significant reduction in the risk of brain injuries, ranging from -5% to -65%, for impact energies between 250 and 500 Joules. This result emphasizes the effectiveness of material design to mitigate sport-related brain injury risks. This research introduces promising avenues for optimizing helmet designs to enhance their protective capabilities.

Impact testing methods to simulate head impacts due to falls from standing height

Fall is one of the leading causes of traumatic brain injury (TBI), and thus, there is an increasing interest in validated tools and protective devices to prevent fall-related TBI. Developing head protective technologies for fall requires a reliable testing method to realistically mimic kinematics of head impacts due to fall to evaluate the injury attenuation of such protective headgears. The objective of this study is to recommend an appropriate and repeatable testing method for simulating fall-related head impacts due to standing height falls. To that end, several impact testing methods that commonly use to assess the efficacy of protective headgear were evaluated and compared. The four different test methods include: (1) a whole-body anthropomorphic test device (ATD) drop; (2) a drop-tower equipped with a Hybrid III head and neck assembly; (3) ASTM F429/F1446 standard; and (4) a linear impactor equipped with a Hybrid III head and neck assembly. Although the ATD drop system simulates fall-related head impacts realistically by considering the whole-body kinematics during falls from standing height, this method showed low repeatability. Among the three repeatable testing methods, only the drop tower with Hybrid III head and neck assembly showed statistically similar results to the ATD drop system for front and rear head impacts for all parameters examined in this study including peak linear acceleration, Head Injury Criterion, peak angular acceleration and peak angular velocity. The results suggested that drop-tower with Hybrid III head and neck assembly can realistically captured both translational and rotational motions of the head during impact due to standing height falls in a repeatable manner.

How Well Do Popular Bicycle Helmets Protect from Different Types of Head Injury?

Bicycle helmets are designed to protect against skull fractures and associated focal brain injuries, driven by helmet standards. Another type of head injury seen in injured cyclists is diffuse brain injuries, but little is known about the protection provided by bicycle helmets against these injuries. Here, we examine the performance of modern bicycle helmets in preventing diffuse injuries and skull fractures under impact conditions that represent a range of real-world incidents. We also investigate the effects of helmet technology, price, and mass on protection against these pathologies. 30 most popular helmets among UK cyclists were purchased within 9.99-135.00 GBP price range. Helmets were tested under oblique impacts onto a 45° anvil at 6.5 m/s impact speed and four locations, front, rear, side, and front-side. A new headform, which better represents the average human head's mass, moments of inertia and coefficient of friction than any other available headforms, was used. We determined peak linear acceleration (PLA), peak rotational acceleration (PRA), peak rotational velocity (PRV), and BrIC. We also determined the risk of skull fractures based on PLA (linear risk), risk of diffuse brain injuries based on BrIC (rotational risk), and their mean (overall risk). Our results show large variation in head kinematics: PLA (80-213 g), PRV (8.5-29.9 rad/s), PRA (1.6-9.7 krad/s2), and BrIC (0.17-0.65). The overall risk varied considerably with a 2.25 ratio between the least and most protective helmet. This ratio was 1.76 for the linear and 4.21 for the rotational risk. Nine best performing helmets were equipped with the rotation management technology MIPS, but not all helmets equipped with MIPS were among the best performing helmets. Our comparison of three tested helmets which have MIPS and no-MIPS versions showed that MIPS reduced rotational kinematics, but not linear kinematics. We found no significant effect of helmet price on exposure-adjusted injury risks. We found that larger helmet mass was associated with higher linear risk. This study highlights the need for a holistic approach, including both rotational and linear head injury metrics and risks, in helmet design and testing. It also highlights the need for providing information about helmet safety to consumers to help them make an informed choice.

Accuracy of Instrumented Mouthguards During Direct Jaw Impacts Seen in Boxing

PURPOSE

Measuring head kinematics data is important to understand and develop methods and standards to mitigate head injuries in contact sports. Instrumented mouthguards (iMGs) have been developed to address coupling issues with previous sensors. Although validated with anthropomorphic test devices (ATDs), there is limited post-mortem human subjects (PMHS) data which provides more accurate soft tissue responses. This study evaluated two iMGs (Prevent Biometrics (PRE) and Diversified Technical Systems (DTS) in response to direct jaw impacts.

METHODS

Three unembalmed male cadaver heads were properly fitted with two different boil-and-bite iMGs and impacted with hook (4 m/s) and uppercut (3 m/s) punches. A reference sensor (REF) was rigidly attached to the base of the skull, impact kinematics were transformed to the head center of gravity and linear and angular kinematic data were compared to the iMGs including Peak Linear Acceleration, Peak Angular Acceleration, Peak Angular Velocity, Head Injury Criterion (HIC), HIC duration, and Brain Injury Criterion.

RESULTS

Compared to the REF sensor, the PRE iMG underpredicted most of the kinematic data with slopes of the validation regression line between 0.72 and 1.04 and the DTS overpredicted all the kinematic data with slopes of the regression line between 1.4 and 8.7.

CONCLUSION

While the PRE iMG was closer to the REF sensor compared to the DTS iMG, the results did not support the previous findings reported with use of ATDs. Hence, our study highlights the benefits of using PMHS for validating the accuracy of iMGs since they closely mimic the human body compared to any ATD's mandible.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user's head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.

Biofidelity and Limitations of Instrumented Mouthguard Systems for Assessment of Rigid Body Head Kinematics

Instrumented mouthguard systems (iMGs) are commonly used to study rigid body head kinematics across a variety of athletic environments. Previous work has found good fidelity for iMGs rigidly fixed to anthropomorphic test device (ATD) headforms when compared to reference systems, but few validation studies have focused on iMG performance in human cadaver heads. Here, we examine the performance of two boil-and-bite style iMGs in helmeted cadaver heads. Three unembalmed human cadaver heads were fitted with two instrumented boil-and-bite mouthguards [Prevent Biometrics and Diversified Technical Systems (DTS)] per manufacturer instructions. Reference sensors were rigidly fixed to each specimen. Specimens were fitted with a Riddell SpeedFlex American football helmet and impacted with a rigid impactor at three velocities and locations. All impact kinematics were compared at the head center of gravity. The Prevent iMG performed comparably to the reference system up to ~ 60 g in linear acceleration, but overall had poor correlation (CCC = 0.39). Prevent iMG angular velocity and BrIC generally well correlated with the reference, while underestimating HIC and overestimating HIC duration. The DTS iMG consistently overestimated the reference across all measures, with linear acceleration error ranging from 10 to 66%, and angular acceleration errors greater than 300%. Neither iMG demonstrated consistent agreement with the reference system. While iMG validation efforts have utilized ATD testing, this study highlights the need for cadaver testing and validation of devices intended for use in-vivo, particularly when considering realistic (non-idealized) sensor-skull coupling, when accounting for interactions with the mandible and when subject-specific anatomy may affect device performance.

AI-Based Denoising of Head Impact Kinematics Measurements With Convolutional Neural Network for Traumatic Brain Injury Prediction

OBJECTIVE

Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring.

METHODS

We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts.

RESULTS

On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%).

CONCLUSION

The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts.

SIGNIFICANCE

This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach

INTRODUCTION

Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

MATERIALS AND METHODS

Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape.

RESULTS

An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible.

CONCLUSIONS

Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

Numerical investigation of driver injury risks in car-to-end terminal crashes using a human finite element model

Although the safety performance of guardrail end terminals is tested using crash tests in the U.S., occupant injury risks are evaluated based on the flail-space model. This approach developed in the early 1980s neglects the influence of safety features (e.g. seatbelt, airbags, etc.) installed in late model vehicles. In this study, a vehicle (sedan, 1100 kg), a guardrail end terminal (ET-Plus) and a human body model (Global Human Body Model Consortium, GHBMC) were integrated to simulate car-to-end terminal crashes. Five velocities, two offsets, and two angles were used as pre-impact conditions. In all the 20 simulations, kinematics and kinetic data were recorded in GHBMC and vehicle models to calculate the GHBMC injury probabilities and vehicle-based injury metrics, correspondingly. Pre-impact velocity was observed to have the largest effect on the occupant injury measures. All the body-region and full-body injury risks increased with the increasing velocity. Meanwhile, the angles had a larger effect than offset to the change of full-body injury risk (9.1% vs. 0.3%). All the vehicle-based metrics had good correlations to full-body injury probabilities. Occupant Impact Velocity (OIVx), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) had a good correlation to chest, thigh, upper tibia, and lower tibia injuries. All the other correlations (e.g. brain/head injuries) were not statistically significant. The results pointed out that more vehicle-based metrics (ASI and THIV) could help improve the predictability in terms of occupant injury risks in the tests. Numerical methodology could be used to assess head and brain injury probabilities, which were not predictable by any vehicle-based metrics.

American Football On-Field Head Impact Kinematics: Influence of Acceleration Signal Characteristics on Peak Maximal Principal Strain

Recorded head kinematics from head-impact measurement devices (HIMd) are pivotal for evaluating brain stress and strain through head finite element models (hFEM). The variability in kinematic recording windows across HIMd presents challenges as they yield inconsistent hFEM responses. Despite establishing an ideal recording window for maximum principal strain (MPS) in brain tissue, uncertainties persist about the impact characteristics influencing vulnerability when this window is shortened. This study aimed to scrutinize factors within impact kinematics affecting the reliability of different recording windows on whole-brain peak MPS using a validated hFEM. Utilizing 53 on-field head impacts recorded via an instrumented mouthguard during a Canadian varsity football game, 10 recording windows were investigated with varying pre- and post-impact-trigger durations. Tukey pair-wise comparisons revealed no statistically significant differences in MPS responses for the different recording windows. However, specific impacts showed marked variability up to 40%. It was found, through correlation analyses, that impacts with lower peak linear acceleration exhibited greater response variability across different pre-trigger durations. Signal shape, analyzed through spectral analysis, influenced the time required for MPS development, resulting in specific impacts requiring a prolonged post-trigger duration. This study adds to the existing consensus on standardizing HIMd acquisition time windows and sheds light on impact characteristics leading to peak MPS variation across different head impact kinematic recording windows. Considering impact characteristics in research assessments is crucial, as certain impacts, affected by recording duration, may lead to significant errors in peak MPS responses during cumulative longitudinal exposure assessments.

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.

Evaluation of DAMAGE Algorithm in Frontal Crashes

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 - BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world. Although DAMAGE correlates well with MPS in the human brain model across several test scenarios, the predicted risk of AIS2+ brain injuries are too high compared to real-world experience. The prediction of AIS4+ brain injury risk in lower velocity crashes is good, but too high in NCAP-like and high speed angular frontal crashes.

An Assessment of Sikh Turban's Head Protection in Bicycle Incident Scenarios

Due to religious tenets, Sikh population wear turbans and are exempted from wearing helmets in several countries. However, the extent of protection provided by turbans against head injuries during head impacts remains untested. One aim of this study was to provide the first-series data of turbans' protective performance under impact conditions that are representative of real-world bicycle incidents and compare it with the performance of bicycle helmets. Another aim was to suggest potential ways for improving turban's protective performance. We tested five different turbans, distinguished by two wrapping styles and two fabric materials with a size variation in one of the styles. A Hybrid III headform fitted with the turban was dropped onto a 45 degrees anvil at 6.3 m/s and head accelerations were measured. We found large difference in the performance of different turbans, with up to 59% difference in peak translational acceleration, 85% in peak rotational acceleration, and 45% in peak rotational velocity between the best and worst performing turbans. For the same turban, impact on the left and right sides of the head produced very different head kinematics, showing the effects of turban layering. Compared to unprotected head impacts, turbans considerably reduce head injury metrics. However, turbans produced higher values of peak linear and rotational accelerations in front and left impacts than bicycle helmets, except from one turban which produced lower peak head kinematics values in left impacts. In addition, turbans produced peak rotational velocities comparable with bicycle helmets, except from one turban which produced higher values. The impact locations tested here were covered with thick layers of turbans and they were impacted against flat anvils. Turbans may not provide much protection if impacts occur at regions covered with limited amount of fabric or if the impact is against non-flat anvils, which remain untested. Our analysis shows that turbans can be easily compressed and bottom out creating spikes in the headform's translational acceleration. In addition, the high friction between the turban and anvil surface leads to higher tangential force generating more rotational motion. Hence, in addition to improving the coverage of the head, particularly in the crown and rear locations, we propose two directions for turban improvement: (i) adding deformable materials within the turban layers to increase the impact duration and reduce the risk of bottoming out; (ii) reducing the friction between turban layers to reduce the transmission of rotational motion to the head. Overall, the study assessed Turbans' protection in cyclist head collisions, with a vision that the results of this study can guide further necessary improvements for advanced head protection for the Sikh community.

Characteristics of head frequency response in blunt impacts: a biomechanical modeling study

Existing evaluation criteria for head impact injuries are typically based on time-domain features, and less attention has been paid to head frequency responses for head impact injury assessment. The purpose of the current study is, therefore, to understand the characteristics of human body head frequency response in blunt impacts via finite element (FE) modeling and the wavelet packet analysis method. FE simulation results show that head frequency response in blunt impacts could be affected by the impact boundary condition. The head energy peak and its frequency increase with the increase in impact; a stiffer impact block is associated with a higher head energy peak, and a bigger impact block could result in a high proportion of the energy peak. Regression analysis indicates that only the head energy peak has a high correlation with exiting head injury criteria, which implies that the amplitude-frequency aggregation characteristic but not the frequency itself of the head acceleration response has predictability for head impact injury in blunt impacts. The findings of the current study may provide additional criteria for head impact injury evaluation and new ideas for head impact injury protection.

Assessment of brain response in operators subject to recoil force from firing long-range rifles

Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. While it is important to protect against injurious head impacts, the repeated exposure of Canadian Armed Forces (CAF) service members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. This study investigates how the exposure to the recoil force of long-range rifles results in head motion and brain deformation. Direct measurements of head kinematics of a controlled population of military personnel during firing events were obtained using instrumented mouthguards. The experimentally measured head kinematics were then used as inputs to a finite element (FE) head model to quantify the brain strains observed during each firing event. The efficacy of a concept recoil mitigation system (RMS), designed to mitigate loads applied to the operators was quantified, and the RMS resulted in lower loading to the operators. The outcomes of this study provide valuable insights into the magnitudes of head kinematics observed when firing long-range rifles, and a methodology to quantify effects, which in turn will help craft exposure guidelines, guide training to mitigate the risk of injury, and improve the quality of lives of current and future CAF service members and veterans.

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.

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

A technique for in situ intracranial strain measurement within a helmeted deformable headform

Despite the broad use of helmets, incidence of concussion remains high. Current methods for helmet evaluation focus on the measurement of head kinematics as the primary tool for quantifying risk of brain injury. Though the primary cause of mild Traumatic Brain Injury (mTBI) is thought to be intracranial strain, helmet testing methodologies are not able to directly resolve these parameters. Computational injury models and impact severity measures are currently used to approximate intracranial strains from head kinematics and predict injury outcomes. Advancing new methodologies that enable experimental intracranial strain measurements in a physical model would be useful in the evaluation of helmet performance. This study presents a proof-of-concept head surrogate and novel helmet evaluation platform that allows for the measurement of intracranial strain using high-speed X-ray digital image correlation (XDIC). In the present work, the head surrogate was subjected to a series of bare and helmeted impacts using a pneumatically-driven linear impactor. Impacts were captured at 5,000 fps using a high-speed X-ray cineradiography system, and strain fields were computed using digital image correlation. This test platform, once validated, will open the door to using brain tissue-level measurements to evaluate helmet performance, providing a tool that can be translated to represent mTBI injury mechanisms, benefiting the helmet design processes.

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.

Simulated Astronaut Kinematics and Injury Risk for Piloted Lunar Landings and Launches While Standing

During future lunar missions, astronauts may be required to pilot vehicles while standing, and the associated kinematic and injury response is not well understood. In this study, we used human body modeling to predict unsuited astronaut kinematics and injury risk for piloted lunar launches and landings in the standing posture. Three pulses (2-5 g; 10-150 ms rise times) were applied in 10 directions (vertical; ± 10-degree offsets) for a total of 30 simulations. Across all simulations, motion envelopes were computed to quantify displacement of the astronaut's head (max 9.0 cm forward, 7.0 cm backward, 2.1 cm upward, 7.3 cm downward, 2.4 cm lateral) and arms (max 25 cm forward, 35 cm backward, 15 cm upward, 20 cm downward, 20 cm lateral). All head, neck, lumbar, and lower extremity injury metrics were within NASA's tolerance limits, except tibia compression forces (0-1543 N upper tibia; 0-1482 N lower tibia; tolerance-1350 N) and revised tibia index (0.04-0.58 upper tibia; 0.03-0.48 lower tibia; tolerance-0.43) for the 2.7 g/150 ms pulse. Pulse magnitude and duration contributed over 80% to the injury metric values, whereas loading direction contributed less than 3%. Overall, these simulations suggest piloting a lunar lander vehicle in the standing posture presents a tibia injury risk which is potentially outside NASA's acceptance limits and warrants further investigation.

Whitewater Helmet STAR: Evaluation of the Biomechanical Performance and Risk of Head Injury for Whitewater Helmets

More than six million people participate in whitewater kayaking and rafting in the United States each year. Unfortunately, with these six million whitewater participants come 50 deaths annually, making it one of the highest fatality rates of all sports. As the popularity in whitewater activities grows, the number of injuries, including concussions, also increases. The objective of this study was to create a new rating system for whitewater helmets by evaluating the biomechanical performance and risk of head injury of whitewater helmets using the Summation of Tests for the Analysis of Risk (STAR) system. All watersport helmets that passed the EN: 1385: 2012 standard and that were clearly marketed for whitewater use were selected for this study. Two samples of each helmet model were tested on a custom pendulum impactor under conditions known to be associated with the highest risk of head injury and death. A 50th percentile male NOCSAE headform instrumented with three linear accelerometers and a triaxial angular rate sensor coupled with a Hybrid III 50th percentile neck were used for data collection. A total of 126 tests were performed using six different configurations. These included impacts to the front, side, and rear using two speeds of 3.1 and 4.9 m/s that modeled whitewater river flow rates. Each helmet's STAR score was calculated using the combination of exposure and injury risk that was determined from the linear and rotational head accelerations. The resulting head impact accelerations predicted a very high risk of concussion for all impact locations at the 4.9 m/s speed. The STAR score varied between helmets indicating that some helmets provide better protection than others. Overall, these results show a clear need for improvement in whitewater helmets, and the methodologies developed in this research project should provide manufacturers a design tool for improving these products.

Impact Mitigation Properties and Material Characterization of Women's Lacrosse Headgear

The purpose of this study was to examine the impact attenuation properties of women's lacrosse headgear and to characterize mechanical properties of the materials of which they are composed. Impacts using a linear impactor (2.2, 2.9, and 5.0 m/s) and a projectile shooter (13.4 and 27.0 m/s) were applied to a Hybrid III 50th male head-neck assembly at six impact locations to replicate realistic women's lacrosse head impacts. Individual materials that make up the headgear were tested in compression at two quasi-static strain rates, 0.01/s and1/s, and 100/s using uniaxial test machines. For the linear impactor tests, results showed a significant decrease in peak linear and rotational acceleration (PLA and PRA), peak rotational velocity (PRV), head injury criteria and brain injury criteria in the helmeted impacts (p < 0.022). During the ball impacts PRV and PRA were significantly lower for both helmeted conditions compared with no helmet (p < 0.01). Material characterization tests indicated a range of rate effects in these materials ranging from weak to pronounced, and these effects correspondingly influenced the strain energy density graphs. The connection of the materials' rate effects to the performance of the headgear is described in general and in relation to the impact tests.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

Oblique impact responses of Hybrid III and a new headform with more biofidelic coefficient of friction and moments of inertia

New oblique impact methods for evaluating head injury mitigation effects of helmets are emerging, which mandate measuring both translational and rotational kinematics of the headform. These methods need headforms with biofidelic mass, moments of inertia (MoIs), and coefficient of friction (CoF). To fulfill this need, working group 11 of the European standardization head protection committee (CEN/TC158) has been working on the development of a new headform with realistic MoIs and CoF, based on recent biomechanics research on the human head. In this study, we used a version of this headform (Cellbond) to test a motorcycle helmet under the oblique impact at 8 m/s at five different locations. We also used the Hybrid III headform, which is commonly used in the helmet oblique impact. We tested whether there is a difference between the predictions of the headforms in terms of injury metrics based on head kinematics, including peak translational and rotational acceleration, peak rotational velocity, and BrIC (brain injury criterion). We also used the Imperial College finite element model of the human head to predict the strain and strain rate across the brain and tested whether there is a difference between the headforms in terms of the predicted strain and strain rate. We found that the Cellbond headform produced similar or higher peak translational accelerations depending on the impact location (−3.2% in the front-side impact to 24.3% in the rear impact). The Cellbond headform, however, produced significantly lower peak rotational acceleration (−41.8% in a rear impact to −62.7% in a side impact), peak rotational velocity (−29.5% in a side impact to −47.6% in a rear impact), and BrIC (−29% in a rear-side impact to −45.3% in a rear impact). The 90th percentile values of the maximum brain strain and strain rate were also significantly lower using this headform. Our results suggest that MoIs and CoF have significant effects on headform rotational kinematics, and consequently brain deformation, during the helmeted oblique impact. Future helmet standards and rating methods should use headforms with realistic MoIs and CoF (e.g., the Cellbond headform) to ensure more accurate representation of the head in laboratory impact tests.

Drop Test Kinematics Using Varied Impact Surfaces and Head/Neck Configurations for Rugby Headgear Testing

World Rugby employs a specific drop test method to evaluate headgear performance, but almost all researchers use a different variation of this method. The aim of this study was, therefore, to quantify the differences between variations of the drop testing method using a Hybrid III headform and neck in the following impact setups: (1) headform only, with a flat steel impact surface, approximating the World Rugby method, (2 and 3) headform with and without a neck, respectively, onto a flat MEP pad impact surface, and (4) headform and neck, dropped onto an angled MEP pad impact surface. Each variation was subject to drop heights of 75-600 mm across three orientations (forehead, side, and rear boss). Comparisons were limited to the linear and rotational acceleration and rotational velocity for simplicity. Substantial differences in kinematic profile shape manifested between all drop test variations. Peak accelerations varied highly between variations, but the peak rotational velocities did not. Drop test variation also significantly changed the ratios of the peak kinematics to each other. This information can be compared to kinematic data from field head impacts and could inform more realistic impact testing methods for assessing headgear.

Assessment of brain injury characterization and influence of modeling approaches

In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions. Four modeling approaches of the skull-brain interface in the head/brain models were evaluated. They were the original models from THUMS and GHBMC, the THUMS model with skull-brain interface changed to sliding contact, and the THUMS model with increased shear modulus of cerebrum, respectively. The results have shown that the definition of skull-brain interface would significantly influence the magnitude and distribution of the load transmitted to the brain. With sliding brain-skull interface, the brain had lower maximum principal stress compared to that with strong connected interface, while the maximum principal strain slightly increased. In addition, greater shear modulus resulted in slightly higher the maximum principal stress and significantly lower the maximum principal strain. This study has revealed that using models with different modeling approaches, the same value of injury metric may correspond to different injury severity.

The Protective Performance of Modern Motorcycle Helmets Under Oblique Impacts

Motorcyclists are at high risk of head injuries, including skull fractures, focal brain injuries, intracranial bleeding and diffuse brain injuries. New helmet technologies have been developed to mitigate head injuries in motorcycle collisions, but there is limited information on their performance under commonly occurring oblique impacts. We used an oblique impact method to assess the performance of seven modern motorcycle helmets at five impact locations. Four helmets were fitted with rotational management technologies: a low friction layer (MIPS), three-layer liner system (Flex) and dampers-connected liner system (ODS). Helmets were dropped onto a 45° anvil at 8 m/s at five locations. We determined peak translational and rotational accelerations (PTA and PRA), peak rotational velocity (PRV) and brain injury criteria (BrIC). In addition, we used a human head finite element model to predict strain distribution across the brain and in corpus callosum and sulci. We found that the impact location affected the injury metrics and brain strain, but this effect was not consistent. The rear impact produced lowest PTAs but highest PRAs. This impact produced highest strain in corpus callosum. The front impact produced the highest PRV and BrIC. The side impact produced the lowest PRV, BrIC and strain across the brain, sulci and corpus callosum. Among helmet technologies, MIPS reduced all injury metrics and brain strain compared with conventional helmets. Flex however was effective in reducing PRA only and ODS was not effective in reducing any injury metrics in comparison with conventional helmets. This study shows the importance of using different impact locations and injury metrics when assessing head protection effects of helmets. It also provides new data on the performance of modern motorcycle helmets. These results can help with improving helmet design and standard and rating test methods.

Development and Validation of a New Anisotropic Visco-Hyperelastic Human Head Finite Element Model Capable of Predicting Multiple Brain Injuries

This paper reports on the latest refinement of the Finite Element Global Human Body Models Consortium 50th percentile (GHBMC M50) adult male head model by the development and incorporation of a new material model into the white matter tissue of the brain. The white matter is represented by an anisotropic visco-hyperelastic material model capable of simulating direction-dependent response of the brain tissue to further improve the bio-fidelity and injury predictive capability of the model. The parameters representing the material were optimized by comparing model responses to seven experimentally reported strain responses of brains of postmortem human subjects (PMHS) subjected to head impact. The head model was subjected to rigorous validation against experimental data on force–deflection responses in the skull and face, intracranial pressure, and brain strain responses from over 34 PMHS head impact experiments. Crash-induced injury indices (CIIs) for facial bone fracture, skull fracture, cerebral contusion, acute subdural hematomas (ASDHs), and diffuse brain injury were developed by reconstructing 32 PMHS and real-world injury cases with the model. Model predicted maximum principal strain (MPS) and stress were determined as fracture CIIs for compact bone and spongy bones, respectively, in the skull and face. Brain responses in terms of MPS, MPS rates, and pressure distribution in injury producing experimental impacts were determined using the model and analyzed with logistic regression and survival analysis to develop CIIs for brain contusions, diffuse brain injuries, and ASDH. The statistical models using logistic regression and survival analysis showed high accuracy with area under the receiver operating curve greater than 0.8. Because of lack of sufficient moderate diffuse brain injury data, a statistical model was not created, but all indications are that the MPS rate is an essential brain response that discriminates between moderate and severe brain injuries. The authors stated that the current GHBMC M50 v.6.0 is an advanced tool for injury prediction and mitigation of injuries in automotive crashes, sports, recreational, and military environments.

Quantification of Head Acceleration Events in Rugby League: An Instrumented Mouthguard and Video Analysis Pilot Study

Instrumented mouthguards (iMG) were used to collect head acceleration events (HAE) in men's professional rugby league matches. Peak linear acceleration (PLA), peak angular acceleration (PAA) and peak change in angular velocity (ΔPAV) were collected using custom-fit iMG set with a 5 g single iMG-axis recording threshold. iMG were fitted to ten male Super League players for thirty-one player matches. Video analysis was conducted on HAE to identify the contact event; impacted player; tackle stage and head loading type. A total of 1622 video-verified HAE were recorded. Approximately three-quarters of HAE (75.7%) occurred below 10 g. Most (98.2%) HAE occurred during tackles (59.3% to tackler; 40.7% to ball carrier) and the initial collision stage of the tackle (43.9%). The initial collision stage resulted in significantly greater PAA and ΔPAV than secondary contact and play the ball tackle stages (p < 0.001). Indirect HAE accounted for 29.8% of HAE and resulted in significantly greater ΔPAV (p < 0.001) than direct HAE, but significantly lower PLA (p < 0.001). Almost all HAE were sustained in the tackle, with the majority occurring during the initial collision stage, making it an area of focus for the development of player protection strategies for both ball carriers and tacklers. League-wide and community-level implementation of iMG could enable a greater understanding of head acceleration exposure between playing positions, cohorts, and levels of play.

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.

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 (v_rot) and the corresponding rotational acceleration (a_rot) 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.

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.

Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40 ms and a post-trigger time of 70 ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200 ms time window recorded by the mouthguard. Therefore, approximately 110 ms is recommended for complete modeling of impacts for football.

An Experimental Platform Generating Simulated Blunt Impacts to the Head Due to Rearward Falls

Impacts to the back of the head due to rearward falls, also referred to as "backfall" events, represent a common source of TBI for athletes and soldiers. A new experimental apparatus is described for replicating the linear and rotational kinematics of the head during backfall events. An anthropomorphic test device (ATD) with a head-borne sensor suite was configured to fall backwards from a standing height, inducing contact between the rear of the head and a ground surface simulant. A pivoting swing arm and release strap were used to generate consistent and realistic head kinematics. Backfall experiments were performed with the ATD fitted with an American football helmet and the resulting linear and rotational head kinematics, as well as calculated injury metrics, compared favorably with those of football players undergoing similar impacts during games or play reconstructions. This test method complements existing blunt impact helmet performance experiments, such as drop tower and pneumatic ram test methods, which may not be able to fully reproduce head-neck-torso kinematics during a backfall event.

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.

A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions

A finite element (FE)-guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body's response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC₁₅) trends were predicted from Kriging models across varied impact velocities (10-45 mph; 16.1-72.4 km/h), locations (near side, far side, front, and rear), and angles (-45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC₁₅ increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC₁₅ under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously. This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10-45 mph), locations, and angles (-45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.

Cervical Muscle Activation Due to an Applied Force in Response to Different Types of Acoustic Warnings

Mild traumatic brain injury (mTBI) and whiplash-associated disorder are the most common head and neck injuries and result from a sudden head or body acceleration. The head and neck injury potential is correlated with the awareness, level of muscle activation, and posture changes at the time of the perturbation. Environmental acoustic stimuli or a warning system can influence muscle activation and posture during a head perturbation. In this study, different acoustic stimuli, including Non-Directional, Directional, and Startle, were provided 1000 ms before a head impact, and the amplitude and timing of cervical muscle electromyographic (EMG) data were characterized based on the type of warning. The startle warning resulted in 49% faster and 80% greater EMG amplitude compared to the Directional and Non-Directional warnings after warning and before the impact. The post-impact peak EMG amplitudes in Unwarned trials were lower by 18 and 21% in the retraction and rebound muscle groups, respectively, compared to any of the warned conditions. When there was no warning before the impact, the retraction and rebound muscle groups also reached their maximum activation 38 and 54 ms sooner, respectively, compared to the warned trials. Based on these results, the intensity and complexity of information that a warning sound carries change the muscle response before and after a head impact and has implications for injury potential.

A New Assessment of Bicycle Helmets: The Brain Injury Mitigation Effects of New Technologies in Oblique Impacts

New helmet technologies have been developed to improve the mitigation of traumatic brain injury (TBI) in bicycle accidents. However, their effectiveness under oblique impacts, which produce more strains in the brain in comparison with vertical impacts adopted by helmet standards, is still unclear. Here we used a new method to assess the brain injury prevention effects of 27 bicycle helmets in oblique impacts, including helmets fitted with a friction-reducing layer (MIPS), a shearing pad (SPIN), a wavy cellular liner (WaveCel), an airbag helmet (Hövding) and a number of conventional helmets. We tested whether helmets fitted with the new technologies can provide better brain protection than conventional helmets. Each helmeted headform was dropped onto a 45° inclined anvil at 6.3 m/s at three locations, with each impact location producing a dominant head rotation about one anatomical axes of the head. A detailed computational model of TBI was used to determine strain distribution across the brain and in key anatomical regions, the corpus callosum and sulci. Our results show that, in comparison with conventional helmets, the majority of helmets incorporating new technologies significantly reduced peak rotational acceleration and velocity and maximal strain in corpus callosum and sulci. Only one helmet with MIPS significantly increased strain in the corpus collosum. The helmets fitted with MIPS and WaveCel were more effective in reducing strain in impacts producing sagittal rotations and a helmet fitted with SPIN in coronal rotations. The airbag helmet was effective in reducing brain strain in all impacts, however, peak rotational velocity and brain strain heavily depended on the analysis time. These results suggest that incorporating different impact locations in future oblique impact test methods and designing helmet technologies for the mitigation of head rotation in different planes are key to reducing brain injuries in bicycle accidents.