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.

The Effect of Muscle Activation on Head Kinematics During Non-injurious Head Impacts in Human Subjects.

In this study, twenty volunteers were subjected to three, non-injurious lateral head impacts delivered by a 3.7 kg padded impactor at 2 m/s at varying levels of muscle activation (passive, co-contraction, and unilateral contraction). Electromyography was used to quantify muscle activation conditions, and resulting head kinematics were recorded using a custom-fit instrumented mouthpiece. A multi-modal battery of diagnostic tests (evaluated using neurocognitive, balance, symptomatic, and neuroimaging based assessments) was performed on each subject pre- and post-impact. The passive muscle condition resulted in the largest resultant head linear acceleration (12.1 ± 1.8 g) and angular velocity (7.3 ± 0.5 rad/s). Compared to the passive activation, increasing muscle activation decreased both peak resultant linear acceleration and angular velocity in the co-contracted (12.1 ± 1.5 g, 6.8 ± 0.7 rad/s) case and significantly decreased in the unilateral contraction (10.7 ± 1.7 g, 6.5 ± 0.7 rad/s) case. The duration of angular velocity was decreased with an increase in neck muscle activation. No diagnostic metric showed a statistically or clinically significant alteration between baseline and post-impact assessments, confirming these impacts were non-injurious. This study demonstrated that isometric neck muscle activation prior to impact can reduce resulting head kinematics. This study also provides the data necessary to validate computational models of head impact.

A reanalysis of football impact reconstructions for head kinematics and finite element modeling.

BACKGROUND: Head kinematics generated by laboratory reconstructions of professional football helmet impacts have been applied to computational models to study the biomechanics of concussion. Since the original publication of this data, techniques for evaluating accelerometer consistency and error correction have been developed. This study applies these techniques to the original reconstruction data and reanalyzes the results given the current state of concussion biomechanics. METHODS: Consistency checks were applied to the sensor data collected in the head of each test dummy. Inconsistent data were corrected using analytical techniques, and head kinematics were recalculated from the corrected data. Reconstruction videos were reviewed to identify artefactual impacts during the reconstruction to establish the region of applicability for simulations. Corrected head kinematics were input into finite element brain models to investigate strain response to the corrected dataset. FINDINGS: Multiple reconstruction cases had inconsistent sensor arrays caused by a problematic sensor; corrections to the arrays caused changes in calculated rotational head motion. These corrections increased median peak angular velocity for the concussion cases from 35.6 to 41.5 rad/s. Using the original kinematics resulted in an average error of 20% in maximum principal strain results for each case. Simulations of the reconstructions also demonstrated that simulation lengths less than 40 ms did not capture the entire brain strain response and under-predicted strain. INTERPRETATION: This study corrects data that were used to determine concussion risk, and indicates altered head angular motion and brain strain response for many reconstructions. Conclusions based on the original data should be re-examined based on this new study.

Evaluation of Head and Brain Injury Risk Functions Using Sub-Injurious Human Volunteer Data.

Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.