Brain Injury Criteria Research Articles

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.

Assessing Kinematic Variables in Short-Track Speed Skating Helmets: A Comparative Study between Traditional Rigid Foam and Anti-Rotation Designs

Purpose: Short-track speed skating results in high-energy crashes with an elevated risk of head injury. The goal of this study was to evaluate the resulting kinematics of an anti-rotation helmet technology for speed skating. Methods: Two traditional rigid foam speed-skating helmets (BT and ST) were compared with one anti-rotation speed skating helmet (MIPS). Each helmet was impacted with a pneumatic device across three locations. The resulting linear or rotational accelerations (PLA or PRA) and rotational velocities (PRV) were measured with accelerometers placed on a Hybrid III head form. Additionally, the head impact criterion (HIC) was calculated from accelerations and the brain injury criterion (BrIC) was obtained from rotational velocities. Results: MIPS showed significantly higher values of accelerations (PLA = 111.24 ± 9.21 g and PRA = 8759.11 ± 2601.81 rad/s2) compared with the other helmets at all three impact locations (p < 0.01, ES = 3.00 to 4.11). However, velocities were lowest, but not significantly different, for the MIPS helmet (25.77 ± 1.43 rad/s). Furthermore, all resulting kinematics except peak linear accelerations were significantly different across impact locations. Conclusion: Helmet designs specific to the collision characteristics of speed skating may still be lacking, but would decrease the risk of sport-related concussions.

Accuracy of Instrumented Mouthguards During Direct Jaw Impacts Seen in Boxing.

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. Three unembalmed male cadaver heads were properly fitted with two different boil-and-bite iMGs and impacted with hook (4m/s) and uppercut (3m/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. 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. 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.

Cognitive Assessment in Grappling Athletes Following Choke versus Nonchoke Submissions.

Participation in Brazilian jiu-jitsu and mixed martial arts has increased over the last three decades. These sports feature submission attacks, including strangles. These strangles, termed "chokes" in this context, primarily limit blood flow to the brain via compression of neck vasculature. There has been discussion in literature of the possibility of measurable cognitive effects following transient choking episodes. The present study used the King-Devick test (KDT) platform, a tablet-based reaction time and accuracy task designed to measure participants' number recognition, cognition, and verbal expression. This task requires functional vision, saccadic eye movements, comprehension, and expression. Volunteer participants were screened for exclusion (prior brain injury) criteria and survey information before testing. Athletes were tested with the KDT immediately before a Brazilian jiu-jitsu training session, again immediately after succumbing to either a choke ("Choke" arm) or nonchoke ("Non-Choke" arm) submission while sparring, and again after a 10-min rest period following the postsubmission test. Analysis was done on test failures, total test times, and individual difference scores between baseline and subsequent testing. Sixty-two (32 Choke, 30 Non-Choke) participants were analyzed. There was no significant difference between Choke and Non-Choke in test failures ( χ2 (1,62) = 1.25, P = 0.263), total times ( t (60) = 0.62, P = 0.540; 95% CI, -3.44 to 6.51), and individual difference scores ( t (60) = 0.29, P = 0.776; 95% CI, -2.41 to 3.21). There were no significant differences between study arms in any of the three analyzed measures. This suggests that cognitive functioning, as measured by the KDT, is not affected by transient choking episodes.

A study on the application of diffuse axonal multi-axis general evaluation for brain injury assessment in small overlap barrier crash test

PurposeHead injury criterion (HIC) companied by a rotation-based metric was widely believed to be helpful for head injury prediction in road traffic accidents. Recently, the Euro-New Car Assessment Program utilized a newly developed metric called diffuse axonal multi-axis general evaluation (DAMAGE) to explain test device for human occupant restraint (THOR) head injury, which demonstrated excellent ability in capturing concussions and diffuse axonal injuries. However, there is still a lack of comprehensive understanding regarding the effectiveness of using DAMAGE for Hybrid Ⅲ 50th percentile male dummy (H50th) head injury assessment. The objective of this study is to determine whether the DAMAGE could capture the risk of H50th brain injury during small overlap barrier tests. MethodsTo achieve this objective, a total of 24 vehicle crash loading curves were collected as input data for the multi-body simulation. Two commercially available mathematical dynamic models, namely H50th and THOR, were utilized to investigate the differences in head injury response. Subsequently, a decision method known as simple additive weighting was employed to establish a comprehensive brain injury metric by incorporating the weighted HIC and either DAMAGE or brain injury criterion. Furthermore, 35 sets of vehicle crash test data were used to analyze these brain injury metrics. ResultsThe rotational displacement of the THOR head is significantly greater than that of the H50th head. The maximum linear and rotational head accelerations experienced by H50th and THOR models were (544.6 ± 341.7) m/s2, (2468.2 ± 1309.4) rad/s2 and (715.2 ± 332.8) m/s2, (3778.7 ± 1660.6) rad/s2, respectively. Under the same loading condition during small overlap barrier (SOB) tests, THOR exhibits a higher risk of head injury compared to the H50th model. It was observed that the overall head injury response during the small overlap left test condition is greater than that during the small overlap right test. Additionally, an equation was formulated to establish the necessary relationship between the DAMAGE values of THOR and H50th. ConclusionIf H50th rather than THOR is employed as an evaluation tool in SOB crash tests, newly designed vehicles are more likely to achieve superior performance scores. According to the current injury curve for DAMAGE and brain injury criterion, it is highly recommended that HIC along with DAMAGE was prioritized for brain injury assessment in SOB tests.

The influence of helmet certification in motorcycle helmets protective performance

The convenience of online shopping has increased access to a vast array of helmet options and deals for motorcyclists. However, the e-commerce enables an influx of unverified and potentially hazardous helmets lacking the rigorous quality control into the market, hence, placing unaware bargain seekers at risk. The non-certified variants questions in terms of impact protection abilities because they visually look similar to certified helmets. This study compared certified full face and open face helmets against their non-certified counterparts by analysing injury predictor metrics. Using a test rig simulating 5.58 ± 0.29 m/s impacts, an anthropomorphic test device wearing both helmet types and certification statuses measured peak resultant linear and angular accelerations, head injury criterion alongside brain injury criteria scores. The data revealed comparable side and rear impact performance between non-certified and certified helmets. However, frontal impacts exposed deficiencies without certification. The non-certified full face helmets registered over twice the peak linear acceleration of certified while open face types still exceeded certified by 40% in frontal impacts. Additionally, non-certified full face helmets indicated up to 100% predicted concussion risks in side and frontal crashes based on the angular accelerations. The poorer frontal impact and elevated injury odds demonstrate certification's key safety advantages that certification should not be ignored while it still providing more protection than no helmet. However, individual needs to carefully select helmets due to performance differences of helmets. Riders should ultimately prioritize proven protection given the severe consequences of head trauma though non-certified may suffice for some low-risk environments.

A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions

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

AI-based denoising of head impact kinematics measurements with convolutional neural network for traumatic brain injury prediction.

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

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

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

The Effect of Motorcycle Helmet Type on Head Response in Oblique Impact

In Malaysia, riding motorcycles is a popular mode of transportation, particularly in urban areas where traffic congestion is prevalent. Additionally, motorcycles are relatively affordable and have low fuel consumption, which makes them an attractive option for many. Per Malaysian traffic laws, riders must wear helmets while riding. As a result, various brands and types of helmets are available for purchase. However, with the increasing popularity of online shopping platforms, many individuals opt to purchase helmets online despite the uncertain quality control of these products. This study aims to assess the effectiveness of three different types of motorcycle helmets in protecting the head from injury. The helmet types evaluated in this unbiased study include full-face, open-face, and half-coverage helmets. The head injury predictors used in this study include Peak Linear Acceleration (PLA), Peak Rotational Acceleration (PRA), Head Injury Criterion (HIC), and Brain Injury Criterion (BrIC). Each helmet was subjected to an impact in a controlled environment using a 6-kg cylinder attached to a pendulum arm, with the impact directed at the front of the helmet at a speed of approximately 6 m/s. Full-face and open-face helmets performed exceptionally well in terms of linear parameters (PLA and HIC). The PLA and HIC of half-coverage helmets are nearly 70% and 50% higher than full-face and open-face helmets. All helmets perform poorly against rotational impact (PRA and BrIC). This shows that helmet design needs to be improved to enhance protection against rotational impact. This study represents the first case study in Malaysia to gather mechanical head injury data comparing the protective performance of different helmet types under both linear and rotational impact. These findings may provide a more accurate understanding of helmet performance in protecting against head injuries.

Understanding Head Injury Risks During Car-to-Pedestrian Collisions Using Realistic Vehicle and Detailed Human Body Models.

Traumatic brain injury (TBI) is the leading cause of death and long-term disability in road traffic accidents (RTAs). Researchers have examined the effect of vehicle front shape and pedestrian body size on the risk of pedestrian head injury. On the other hand, the relationship between vehicle front shape parameters and pedestrian TBI risks involving a diverse population with varying body sizes has yet to be investigated. Thus, the purpose of this study was to comprehensively study the effect of vehicle front shape parameters and various pedestrian bodies ranging from 95th percentile male (AM95) to 6 years old (YO) child on the dynamic response of the head and the risk of TBIs during primary (vehicle) impact. At three different collision speeds (30, 40, and 50 km/h), a total of 36 car-to-pedestrian collisions (CPCs) were reconstructed using three different vehicle types (Subcompact passenger sedan, mid-sedan, and sports utility vehicle (SUV)) and four distinct THUMS pedestrian finite element (FE) models (AM50, AM95, AF05, and 6YO). We assessed skull stress and brain strains besides head linear and rotational kinematics. Our findings indicate that vehicle shape parameters especially bonnet leading edge height (BLEH), when being divided by the height of the Center of Gravity of the human body, correlated positively to head kinematics. The data from this study using realistic vehicle structures and detailed human body models showed that smaller BLEH/CG ratios reduced head injury criteria (HIC) and brain injury criteria (BrIC) values for the car center to mid-stance walking pedestrian impacts but with low-to-moderate R squared values between 0.2 to 0.5. Smaller BLEH/CG reduced head lateral bending velocities with R squared values of 0.57 to 0.63 for all impact velocities, and reduced HIC with R squared value of 0.62 for 50 km/h cases. In the future, simulations with realistic car structures and detailed human body models will be further used to simulate impacts at different locations and with various body shapes/postures.

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20mph. Results: In a lateral movement, the excursions of the dummy head were 100mm and 70mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150mm and 180mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

Design and proof-of-concept of an advanced protective system for the dissipation of tangential impact energy in helmets, based on non-Newtonian fluids

We have designed and tested a device based on non-Newtonian fluids for the attenuation of tangential impacts energy in helmets. A shear thickening fluid based on borurated silicones has given the best results in terms of impact energy attenuation in a system that selectively mimics tangential impacts and for this reason it has been used to fill the chamber of a pad, prepared by additive manufacturing, for impact energy dissipation. The pad is composed of a case containing the fluid in which is immersed a rigid pin that is free to move in all direction and therefore is able to absorb energy during its movement inside the fluid. A motorcycle helmet, already composed of two concentrical expanded polystyrene (EPS) liners, has been implemented with seven pads between the two EPS layers. The two layers have only been connected by means of the dissipating pad and are free to rotate and to dissipate energy during the rotation. The results of oblique impacts according to ECE 22.06 with three impact positions, shows a reduction of the brain injury criterion of 14% for the helmet with the pads, compared to the standard helmet, with values well below the threshold imposed by the norm. On the contrary, the maximum of the peak rotational acceleration show a 3% increase. Nevertheless, the rotational acceleration versus time curves indicates that in all three orientations the time of the maximum is shifted towards longer times for the helmets with the pads, indicating that the pads retard the acceleration of the head due to the efficient rotation of the two EPS parts connected by the pads. The described system could be implemented with minimal modifications in existing protective sport and motorcycle helmets.

Head injury criteria assessment using head kinematics from crash tests and accident reconstructions

Objective The aim of this study was to assess head injury criteria based on their correlation to brain strain in a Finite Element (FE) head model (the KTH Royal Institute of Technology model), by simulation of head kinematics data from frontal and side crash tests with Anthropomorphic Test Devices (ATDs), and from Human Body Model (HBM) accident reconstructions. Methods Six Degrees of Freedom (DoF) head kinematic data was extracted from 221 crash tests, consisting of frontal impacts with the THOR-50M ATD, near-side and far-side impacts with the WorldSID-50M ATD, and from 19 FE HBM accident reconstructions. The head injury criteria HIC15, HIP, BrIC, UBrIC, DAMAGE and CIBIC were calculated, and FE head model simulations were conducted using the six DoF kinematics data. The 100th, 99th, and 95th percentile Maximum Principal Strains (MPS) of the brain were extracted and linear regression models with respect to the injury criteria were created. The injury criteria were then evaluated based on the coefficient of determination, R2, and the Normalized Root Mean Square Error (NRMSE) of each regression model. Results For all the data sets combined and for the WorldSID far-side data, CIBIC had the best goodness of fit, with R2 of 0.76 and 0.85. For frontal impacts with THOR and the combined ATD data set, DAMAGE had highest R2, 0.83 and 0.78, respectively. Injury criteria including translational accelerations were ranked lower, and BrIC were among the three lowest ranked for most data sets evaluated. UBrIC generally ranked after DAMAGE and CIBIC with respect to the goodness of fit but had the lowest NRMSE for all data sets. Conclusions The two mass-spring-damper brain surrogate model criteria, DAMAGE and CIBIC, were best in capturing the head model MPS response for both the THOR and WorldSID data sets. BrIC had lower correlation to the head model MPS and performed marginally better than the linear acceleration only criteria for all the data sets combined. This study supports the suitability of DAMAGE and CIBIC as brain injury criteria to be used with THOR-50M and WorldSID-50M in vehicle crash test conditions, as they outperform BrIC.

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 5.0m/s) and a projectile shooter (13.4 and 27.0m/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.

Deep learning method to assess brain injury risk

Model-based brain injury criteria can present higher potential to predict injury than global head kinematic parameters. Numerical head injury prediction tools are time consuming and require Finite Element (FE) skilled users. To address these difficulties, a deep learning technique was applied to an existing previously developed brain FE model for which an injury risk curve has been proposed in terms of maximum Von Mises stress to predict moderate diffuse axonal injury. A total of 4492 experimental head impacts coming from experimental helmet testing were considered as input for the analysis. Each input was expressed in terms of three linear accelerations and three angular velocities versus time, when the target metric was the time history Von Mises Stress () curve computed within the brain via the FE analysis. The architecture used for the Deep Learning (DL) model is the U-Net and four models based on it were evaluated. The dataset was split into three datasets dedicated for learning and testing. The quality of the DL models were assessed via the Maximum Absolute Error between FE and DL models computed brain maximum Further, a regression analysis of brain response with both methods was conducted. The results demonstrated that deep learning methods can be applied in the context of brain response estimation when helmet assessment is considered without any FE computation, only by considering the 6 D head kinematic vs time demonstrating that the deep learning approach should be further developed for research and industrial applications.

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.

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

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

The Effect of MIPS, Headform Condition, and Impact Orientation on Headform Kinematics Across a Range of Impact Speeds During Oblique Bicycle Helmet Impacts.

Bicycle helmets are designed to attenuate both the linear and rotational response of the head during an oblique impact. Here we sought to quantify how the effectiveness of one popular rotation-attenuating system (MIPS) varied across 3 test headform conditions (bare, covered in stockings, and hair), 3 oblique impact orientations, and 4 impact speeds. We conducted 72 freefall drop tests of a single helmet model with and without MIPS onto a 45° angled anvil and measured the peak linear (PLA)and angular acceleration (PAA) and computed the angular velocitychange (PAV) and brain injury criterion (BrIC). Across all headform conditions, MIPS reduced PAA and PAV by 38.2 and 33.2% respectively during X-axis rotation, 47.4 and 38.1% respectively during Y-axis rotation, and 22.9 and 20.5% during a combined ZY-axis rotation. Across all impact orientations, PAA was reduced by 39% and PAV by 32.4%withthe bare headform while adding stockings reduced PAA and PAV by 41.6 and 36% respectively and the hair condition reduced PAA and PAV by 30.2 and 24.4% respectively. In addition, our data reveal the importance of using consistent headform conditions when evaluating the effect of helmet systems designed to attenuate head rotations during oblique impacts.

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.