Consensus Head Acceleration Measurement Practices (CHAMP): Origins, Methods, Transparency and Disclosure.

The use of head kinematic measurement devices has recently proliferated owing to technology advances that make such measurement more feasible. In parallel, demand to understand the biomechanics of head impacts and injury in sports and the military has increased as the burden of such loading on the brain has received focused attention. As a result, the field has matured to the point of needing methodological guidelines to improve the rigor and consistency of research and reduce the risk of scientific bias. To this end, a diverse group of scientists undertook a comprehensive effort to define current best practices in head kinematic measurement, culminating in a series of manuscripts outlining consensus methodologies and companion summary statements. Summary statements were discussed, revised, and voted upon at the Consensus Head Acceleration Measurement Practices (CHAMP) Conference in March 2022. This manuscript summarizes the motivation and methods of the consensus process and introduces recommended reporting checklists to be used to increase transparency and rigor of future experimental design and publication of work in this field. The checklists provide an accessible means for researchers to apply the best practices summarized in the companion manuscripts when reporting studies utilizing head kinematic measurement in sport and military settings.

Laboratory Evaluation of Shell Add-On Products for American Football Helmets for Professional Linemen.

The Guardian Cap NXT (GC NXT) and the ProTech Helmet Cap (ProTech) are commercially available aftermarket products designed to augment the energy attenuation characteristics of American football helmets. The ability of these helmet shell add-on products to mitigate the severity of impacts typically experienced by professional offensive and defensive linemen was evaluated for seven helmet models using two test series. In linear impactor tests, the GC NXT reduced head impact severity as measured by the head acceleration response metric (HARM) by 9% relative to the helmets only, while the ProTech reduced HARM by 5%. While both products significantly improved the performance of the football helmets tested overall, effects varied by impact condition and helmet model with the add-ons worsening helmet performance in some conditions. The GC NXT had a strong effect size (Cohen's d = 0.8) whereas the ProTech had a medium effect (Cohen's d = 0.5). A second study investigated add-on performance for helmet-to-helmet impacts with eccentric impact vectors and resulted in a mixture of increased and decreased HARM when either add-on was placed on one or both helmets. Estimated risk for serious neck injury with add-ons and without differed by less than 4% for these eccentric impacts.

An omni-directional model of injury risk in planar crashes with application for autonomous vehicles.

Objective: Automated driving systems (ADS) are actively being deployed within the driving fleet. ADS are designed to safely navigate roadways, which entails an expectation of encountering varying degrees of potential conflict with other road users. The ADS design and evaluation process benefits from estimating injury severity probabilities for collisions that may occur. Current regression models in the literature are typically bespoke analyses involving targeted principal directions of force (PDOFs) and occupant positions. It is preferable to rely on injury severity models derived from a single source to provide a continuous function of risk for all planar collisions, while also accounting for specific vehicle and occupant characteristics. The novel feature of the proposed models is continuous, parametric injury risk surfaces that encompass the full spectrum of available United States field data.Methods: We used years 2001-2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and years 2017-2019 of the Crash Investigation Sampling System (CISS) to estimate injury risk at the maximum abbreviated injury scale (MAIS) 3 and higher (3+) and 5 and higher (5+) levels for all adult occupants traveling in 2002 or newer passenger vehicles which were less than 10 years old at the time of the crash. The models account for occupant, vehicle, and crash characteristics. Interactions with vulnerable road users (e.g., pedestrian, bicyclist) were not considered.Results: We present statistical models suitable to predict injury in all non-rollover crashes at the maximum MAIS3+ and 5+ levels, and show that these models can be comparable to similar single scenario (e.g., frontal) crash models. We discuss challenges with imputing missing field data, and discuss handling of covariates that may not be known at the time of the crash.Conclusions: Collision severity assessment is a vital component of the ADS design process. We developed a novel injury risk function that can assess occupant injury risks across the spectrum of foreseeable planar collisions. These models can provide insight on potential outcomes of counterfactual simulations, injury risk and crashworthiness considerations for human driven vehicles, and provide an evaluation tool that can be applied in ADS safety impact evaluation.

Development of a Low-Power Instrumented Mouthpiece for Directly Measuring Head Acceleration in American Football.

Instrumented mouthpieces (IM) offer a means of measuring head impacts that occur in sport. Direct measurement of angular head kinematics is preferential for accuracy; however, existing IMs measure angular velocity and differentiate the measurement to calculate angular acceleration, which can limit bandwidth and consume more power. This study presents the development and validation of an IM that uses new, low-power accelerometers for direct measurement of linear and angular acceleration over a broad range of head impact conditions in American football. IM sensor accuracy for measuring six-degree-of-freedom head kinematics was assessed using two helmeted headforms instrumented with a custom-fit IM and reference sensor instrumentation. Head impacts were performed at 10 locations and 6 speeds representative of the on-field conditions associated with injurious and non-injurious impacts in American football. Sensor measurements from the IM were highly correlated with those from the reference instrumentation located at the maxilla and skull center of gravity. Based on pooled data across headform and impact location, R2 ≥ 0.94, mean absolute error (AE) ≤ 7%, and mean relative impact angle ≤ 11° for peak linear and angular acceleration and angular velocity while R2 ≥ 0.90 and mean AE ≤ 7% for kinematic-based injury metrics used in helmet tests.

Methodology for vehicle safety development and assessment accounting for occupant response variability to human and non-human factors.

The use of standardized anthropomorphic test devices and test conditions prevent current vehicle development and safety assessments from capturing the breadth of variability inherent in real-world occupant responses. This study introduces a methodology that overcomes these limitations by enabling the assessment of occupant response while accounting for sources of human- and non-human-related variability. Although the methodology is generic in nature, this study explores the methodology in its application to human response in far-side motor vehicle crashes as an example. A total of 405 human body model simulations were conducted in a mid-sized sedan vehicle environment to iteratively train two neural networks to predict occupant head excursion and thoracic injury as a function of occupant anthropometry, impact direction and restraint configuration. The neural networks were utilized in Monte Carlo simulations to calculate the probability of head-to-intruding-door impacts and thoracic AIS 3+ as a function of the restraint configuration. This analysis indicated that the vehicle used in this study would lead to a range of 667 to 2,448 head-to-intruding-door impacts and a range of 3,041 to 3,857 cases of thoracic AIS 3+ in the real world, depending on the seatbelt load limiter. These real-world results were later successfully validated using United States field data. This far-side assessment illustrates how the methodology incorporates the human and non-human variability, generates response surfaces that characterize the effects of the variability, and ultimately permits vehicle design considerations and injury predictions appropriate for real-world field conditions.

Occupant Restraint in Far-Side Impacts: Cadaveric and WorldSID Responses to a Far-Side Airbag.

Previous studies indicate that seatbelts may require supplementary restraints to increase their effectiveness in far-side impacts. This study aimed to evaluate the effectiveness of a novel, far-side-specific airbag in restraining and preventing injuries in far-side impacts, and to evaluate the WorldSID's response to the presence of a far-side airbag. A series of tests with three Post-Mortem Human Subjects and the WorldSID was conducted in a vehicle-based sled environment equipped with a far-side airbag. Results of these tests were evaluated and compared to a previous test series conducted without the airbag. All of the PMHS retained the shoulder belt on the shoulder. The airbag significantly reduced PMHS injury severity and maximum lateral head excursion. While the WorldSID exhibited a similar decrease in lateral excursion, it was unable to represent PMHS thoracic deflection or injury probability, and it consistently slipped out of the shoulder belt. This indicates that the WorldSID is limited both in its ability to evaluate the effect of changes in the seatbelt system and in its ability to predict thoracic injury risk and assess airbag-related injury mitigation countermeasures.

Laboratory Reconstructions of Concussive Helmet-to-Helmet Impacts in the National Football League.

Seventeen concussive helmet-to-helmet impacts occurring in National Football League (NFL) games were analyzed using video footage and reconstructed by launching helmeted crash test dummies into each other in a laboratory. Helmet motion on-field and in the laboratory was tracked in 3D before, during, and after impact in multiple high frame rate video views. Multiple (3-10) tests were conducted for each of the 17 concussive cases (100 tests total) with slight variations in input conditions. Repeatability was assessed by duplicating one or two tests per case. The accuracy of the input conditions in each reconstruction was assessed based on how well the closing velocity, impact locations, and the path eccentricity of the dummy heads matched the video analysis. The accuracy of the reconstruction output was assessed based on how well the changes in helmet velocity (translational and rotational) from the impact matched the video analysis. The average absolute error in helmet velocity changes was 24% in the first test, 20% in the tests with the most accurate input configuration, and 14% in the tests with minimal error. Coefficients of variation in 22 repeated test conditions (1-2 per case) averaged 3% for closing velocity, 7% for helmet velocity changes, and 8% for peak head accelerations. Iterative testing was helpful in reducing error. A combination of sophisticated video analysis, articulated physical surrogates, and iterative testing was required to reduce the error to within half of the effect size of concussion.

Characterization of Concussive Events in Professional American Football Using Videogrammetry.

Sports concussions offer a unique opportunity to study head kinematics associated with mild traumatic brain injury. In this study, a model-based image matching (MBIM) approach was employed to analyze video footage of 57 concussions which occurred in National Football League (NFL) games. By utilizing at least two camera views, higher frame rate footage (> 60 images s-1), and laser scans of the field and helmets involved in each case, it was possible to calculate the change in velocity of the helmet during impact in six degrees of freedom. The average impact velocity for these concussive events was 8.9 ± 2.0 m s-1. The average changes in translational and rotational velocity for the concussed players' helmets were 6.6 ± 2.1 m s-1 and 29 ± 13 rad s-1, respectively. The average change in translational velocity was higher for helmet-to-ground (n = 16) impacts compared to helmet-to-helmet (n = 30) or helmet-to-shoulder (n = 11) events (p < 0.001), while helmet-to-shoulder impacts had a smaller change in rotational velocity compared to the other impact sources (p < 0.001). By quantifying the impact velocities and locations associated with concussive impacts in professional American football, this study provides information that may be used to improve upon current helmet testing methodologies.

Comparison of Laboratory and On-Field Performance of American Football Helmets.

The relationship between laboratory and on-field performance of football helmets was assessed for 31 football helmet models selected from those worn by players in the 2015-2019 National Football League (NFL) seasons. Linear impactor tests were conducted with helmets placed on an instrumented Hybrid III head and neck assembly mounted on a sliding table. Based on impacts to each helmet at six impact locations and three velocities, a helmet performance score (HPS) was calculated using a linear combination of the head injury criterion (HIC) and the diffuse axonal multi-axis general evaluation (DAMAGE). To determine the on-field performance of helmets, helmet model usage, player participation, and incident concussion data were collected from the five NFL seasons and used to calculate helmet model-specific concussion rates. Comparison of laboratory HPS to the helmet model-specific concussion rates on a per play basis showed a positive correlation (r2 = 0.61, p < 0.001) between laboratory and on-field performance of helmet models, indicating that helmets which exhibited reduced impact severity in the laboratory tests were also generally associated with lower concussion rates on-field. Further analysis showed that NFL-prohibited helmet models exhibited a significantly higher odds of concussion (OR 1.24; 95% CI 1.04-1.47; p = 0.017) relative to other helmet models.

Development and Evaluation of a Test Method for Assessing the Performance of American Football Helmets.

As more is learned about injury mechanisms of concussion and scenarios under which injuries are sustained in football games, methods used to evaluate protective equipment must adapt. A combination of video review, videogrammetry, and laboratory reconstructions was used to characterize concussive impacts from National Football League games during the 2015-2017 seasons. Test conditions were generated based upon impact locations and speeds from this data set, and a method for scoring overall helmet performance was created. Head kinematics generated using a linear impactor and sliding table fixture were comparable to those from laboratory reconstructions of concussive impacts at similar impact conditions. Impact tests were performed on 36 football helmet models at two laboratories to evaluate the reproducibility of results from the resulting test protocol. Head acceleration response metric, a head impact severity metric, varied 2.9-5.6% for helmet impacts in the same lab, and 3.8-6.0% for tests performed in a separate lab when averaged by location for the models tested. Overall inter-lab helmet performance varied by 1.1 ± 0.9%, while the standard deviation in helmet performance score was 7.0%. The worst helmet performance score was 33% greater than the score of the best-performing helmet evaluated by this study.

Position-Specific Circumstances of Concussions in the NFL: Toward the Development of Position-Specific Helmets.

Consideration of position-specific features of the NFL concussion environment could enable improved risk mitigation through the design of position-specific helmets to improve self-protection as well as protection for the other player with whom the contact occurs. The purpose of this paper is to quantify position-specific features of scenarios resulting in concussions to NFL players, and the players they contact, by reviewing all game footage (broadcast and non-broadcast) over 4 seasons. Position-specific features were documented for 647 concussions in which a primary exposure could be visualized, including impact source, helmet impact location, activity, and the other player with whom the contact occurred. Findings include the over-representation of helmet-to-ground impacts to the rear of the quarterback's helmet, the high frequency of impacts to the side (upper) location of both concussed players and the players they contacted regardless of position, and distinct differences in the circumstances of concussions to cornerbacks and safeties. The study shows that some features of concussion scenarios are common to all positions, but several position-specific features exist and can inform the design of position-specific helmets for NFL players.

On-Field Performance of an Instrumented Mouthguard for Detecting Head Impacts in American Football.

Wearable sensors that accurately record head impacts experienced by athletes during play can enable a wide range of potential applications including equipment improvements, player education, and rule changes. One challenge for wearable systems is their ability to discriminate head impacts from recorded spurious signals. This study describes the development and evaluation of a head impact detection system consisting of a mouthguard sensor and machine learning model for distinguishing head impacts from spurious events in football games. Twenty-one collegiate football athletes participating in 11 games during the 2018 and 2019 seasons wore a custom-fit mouthguard instrumented with linear and angular accelerometers to collect kinematic data. Video was reviewed to classify sensor events, collected from instrumented players that sustained head impacts, as head impacts or spurious events. Data from 2018 games were used to train the ML model to classify head impacts using kinematic data features (127 head impacts; 305 non-head impacts). Performance of the mouthguard sensor and ML model were evaluated using an independent test dataset of 3 games from 2019 (58 head impacts; 74 non-head impacts). Based on the test dataset results, the mouthguard sensor alone detected 81.6% of video-confirmed head impacts while the ML classifier provided 98.3% precision and 100% recall, resulting in an overall head impact detection system that achieved 98.3% precision and 81.6% recall.

Surface Contact Features, Impact Obliquity, and Preimpact Rotational Motion in Concussive Helmet-to-Ground Impacts: Assessment via a New Impact Test Device.

This paper reports the development of a test device for replicating unique features of concussion-causing helmet-to-ground impacts. Helmet-to-ground impacts are characterized by an oblique impact velocity vector, preimpact rotational motion of the helmeted head, and an impact into a compliant frictional surface of unknown effective mass. No helmet assessment testing program replicates these impact characteristics, yet they influence brain injury risk and therefore may influence helmet design priorities. To replicate these mechanics, the carriage of a drop tower was modified by the addition of a curvilinear bearing track and a hinged torso-neck fixture to which a helmeted head of a Hybrid III anthropomorphic test device was mounted. Preimpact rotational motion of the head was imparted by forcing a link arm to follow the curvilinear path as the carriage fell under gravity. At impact, the rotating helmeted head struck a vertically mounted surface. The ground impact features of head kinematics are illustrated by comparing rear impacts into a rigid, low-friction surface against those into a compliant frictional surface simulating turf. With the rigid, low-friction surface, the head experienced a change in rotational rate of approximately 40 rad/s, which corresponded to a peak rotational acceleration of approximately αy = - 4000 rad/s2. In contrast, peak rotational acceleration with the compliant frictional surface was approximately αy = - 1000 rad/s2 while the helmet was in contact with the surface. Neck loads were significantly greater with the compliant frictional surface. Translational head acceleration was less sensitive to the surface characteristics, with the peak of the anterior-posterior component essentially unchanged.

Monte carlo method for estimating whole-body injury metrics from pedestrian impact simulation results.

The goal of the current study was to develop a method to estimate whole-body injury metrics (WBIMs), which measure the overall impact of injuries, using stochastic injury prediction results from a computational human surrogate. First, hospitalized pedestrian data was queried to identify injuries sustained by pedestrians and their frequencies. Second, with consideration for an understanding of injury mechanisms and the capability of the computational human surrogate, the whole-body was divided into 17 body regions. Then, an injury pattern database was constructed for each body region for various maximum abbreviated injury scale (MAIS) levels. Third, a two-step Monte Carlo sampling process was employed to generate N virtual pedestrians with an assigned list of injuries in AIS codes. Then, the expected values of WBIMs such as injury severity score (ISS), probability of death, whole-body functional capacity index (WBFCI), and lost years of life (LYL), were estimated. Lastly, the proposed method was verified using injury information from the inpatient pedestrian database. Also, the proposed method was applied to pedestrian impact simulations with various impact speeds to estimate the probability of death with respect to the impact speed. The probability of death from the proposed method was compared with those from epidemiological studies. The proposed method accurately estimated WBIMs such as ISS and WBFCI using either for a given distribution of injury risk or MAIS levels. The predicted probability of death with respect to the impact speed showed a good correlation with those from the epidemiological study. These results imply that if we have a human surrogate that can predict the risk of injury accurately, we can accurately estimate WBIMs using the proposed method. The proposed method can simplify a vehicle design optimization process by transforming the multi-objective optimization problem into the single-objective one. Lastly, the proposed method can be applied to other human surrogates such as occupant models.

Sensitivity and Specificity of On-Field Visible Signs of Concussion in the National Football League.

BACKGROUND: On-field visible signs (VS) are used to help identify sport-related concussion (SRC) in the National Football League (NFL). However, the predictive utility of a VS checklist for SRC is unknown. OBJECTIVE: To report the frequency, sensitivity, specificity, and predictive value of VS in a cohort of NFL athletes. METHODS: On-field VS ratings from 2 experts who independently reviewed video footage of a cohort of 251 injury plays that resulted in an SRC diagnosis (n = 211) and no diagnosis (n = 40) from the 2017 NFL season were examined. The frequency, sensitivity, specificity, and a receiver operating characteristic (ROC) curve with area under the curve (AUC) were calculated for each VS. RESULTS: Slow to get up (65.9%) and motor incoordination (28.4%) were the most frequent VS in concussed athletes, and slow to get up (60.0%) was the most common VS among nonconcussed athletes. The most sensitive VS was slow to get up (66%); the most specific signs in concussed NFL athletes were blank/vacant look and impact seizure (both 100%). Approximately 26% of concussed NFL players did not exhibit a VS, and the overall sensitivity and specificity for the VS checklist to detect SRC were 73% and 65%, respectively. The VS checklist demonstrated "poor" ability to discriminate between SRC and non-SRC groups (AUC = 0.66). CONCLUSION: In the NFL, the diagnosis of concussion cannot be made from on-field VS alone. The VS checklist is one part of the comprehensive sideline/acute evaluation of concussion, and the diagnosis remains a multimodal clinical decision.

Concussions in the National Football League: the evolution of video review for assessing the frequency and reliability of visible signs.

Background: The use of video review to document visible signs (VS) of sport-related concussion in the National Football League (NFL) is a novel method to recognize head injuries. Hypothesis/Purpose: The current pilot studies used varying methodologies to (1) examine the frequency of VS in concussed NFL players using the Australian Football League's (AFL) checklist, and (2) assess the reliability of VS between non-expert and expert raters. Study design: Cohort study Methods: In the first pilot study, two non-expert raters rated VS of SRC occurring in the 2015 NFL season (n = 96) using a single VS from the AFL checklist. Based on this pilot study, two expert raters then rated VS of SRC during the 2017 NFL season (n = 211) using all VS from the AFL checklist. The frequency, total percent agreement (TPA), and reliability (kappa coefficients) were calculated for all VS of concussion for the two seasons. Kappa agreement was classified as fair (.41-.60), moderate (.61-.80), or substantial (.81-1.00). Significance was set at p < .05. Results: The most frequent VS of concussion identified by both non-expert and expert raters were no behavior observed, slow to get up, and motor incoordination. The least frequent VS were impact seizure, blank/vacant look, and facial injury. For non-expert raters, the average TPA for VS ranged from 84% to 100% and kappa coefficients ranged from .52 to .68. For expert raters, the average TPA ranged from 83% to 100%, and kappa coefficients ranged from .56 to .86. Conclusion: In these preliminary analyses, use of multiple VS was a superior methodology, and the reliability of VS rating was stronger for experts. Due to the inherent differences in gameplay and protective equipment used in the NFL compared to other professional sports, it is our hope these data can generate new ways to improve existing practices and identify potentially novel VS of SRC.

Validation of a videogrammetry technique for analysing American football helmet kinematics.

Professional American football games are recorded in digital video with multiple cameras, often at high resolution and high frame rates. The purpose of this study was to evaluate the accuracy of a videogrammetry technique to calculate translational and rotational helmet velocity before, during and after a helmet impact. In total, 10 football impacts were staged in a National Football League (NFL) stadium by propelling helmeted 50th percentile male crash test dummies into each other or the ground at speeds and orientations representative of concussive impacts for NFL players. The tests were recorded by experienced sports film crews to obtain video coverage and quality typically available for NFL games. A videogrammetry procedure was used to track the position and rotation of the helmet throughout the relevant time interval of the head impact. Compared with rigidly mounted retroreflective marker three dimensional (3-D) motion tracking that was concurrently collected in the experiments, videogrammetry accurately calculated changes in translational and rotational velocity of the helmet using high frame rate (two cameras at 240 Hz) video (7% and 15% error, respectively). Low frame rate (2 cameras at 60 Hz) video was adequate for calculating pre-impact translational velocity but not for calculating the translational or rotational velocity change of the helmet during impact.

Evaluating the influence of knee airbags on lower limb and whole-body injury.

Objective: Knee airbags (KABs) have become increasingly common in the vehicle fleet. Previous studies (Weaver et al., 2013, Patel et al. 2013) showed indications that KABs may be protective for some lower extremity injuries and associated with increased risk for others. Since KABs have become significantly more common in recent model year vehicles, we revisited these findings using the most recent available data.Methods: We compared injury rates below the knee, from the knee to the hip, and above the hip in years 2000-2015 of the National Automotive Sampling System, Crashworthiness Data System (NASS-CDS) and the Crash Injury Research and Engineering Network (CIREN). Injury rates were compared with matched analyses and with Bayesian multiple logistic regression.Results: Both analyses showed that KAB to have an Odds Ratio of approximately 0.6 for knee to hip injuries, with the Bayesian model strongly significant and the matched model borderline insignificant. In the Bayesian model, KAB was borderline significant for a decrease in above the waist injuries, while the matched model pointed toward a protective effect but was not significant. Both models pointed toward an increased risk of below knee injuries, but neither was statistically significant.Conclusions: Knee airbags may be protective for knee to hip injuries and above waist injuries. If KABs continue to be widely implemented in the vehicle fleet, the field should continue to monitor and evaluate below knee injuries.