Recent research has suggested a possible link between sports-related concussions and neurodegenerative processes, highlighting the importance of developing methods to accurately quantify head impact tolerance. The use of kinematic parameters of the head to predict brain injury has been suggested because they are indicative of the inertial response of the brain. The objective of this study isto characterize the rotational kinematics of the head associated with concussive impacts using a large head acceleration dataset collected from human subjects. The helmets of 335 football players were instrumented with accelerometer arrays that measured head acceleration following head impacts sustained during play, resulting in data for 300,977 subconcussive and 57 concussive head impacts. The average subconcussive impact had a rotational acceleration of 1230 rad/s 2 and a rotational velocity of 5.5 rad/s, while the average concussive impact had a rotational acceleration of 5022 rad/s 2 and a rotational velocity of 22.3 rad/s. An injury risk curve was developed and a nominal injury value of 6383 rad/s 2 associated with 28.3 rad/s represents 50% risk of concussion. These data provide an increased understanding of the biomechanics associated with concussion, and they provide critical insight into injury mechanisms, human tolerance to mechanical stimuli, and injury prevention techniques.

The objective of this study was to investigate potential for traumatic brain injuries (TBI) using a newly developed, geometrically detailed, finite element head model (FEHM) within the concept of a simulated injury monitor (SIMon). The new FEHM is comprised of several parts: cerebrum, cerebellum, falx, tentorium, combined pia-arachnoid complex (PAC) with cerebro-spinal fluid (CSF), ventricles, brainstem, and parasagittal blood vessels. The model's topology was derived from human computer tomography (CT) scans and then uniformly scaled such that the mass of the brain represents the mass of a 50 th percentile male's brain (1.5 kg) with the total head mass of 4.5 kg. The topology of the model was then compared to the preliminary data on the average topology derived from Procrustes shape analysis of 59 individuals. Material properties of the various parts were assigned based on the latest experimental data. After rigorous validation of the model using neutral density targets (NDT) and pressure data, the stability of FEHM was tested by loading it simultaneously with translational (up to 400 g) combined with rotational (up to 24,000 rad/s 2) acceleration pulses in both sagittal and coronal planes. Injury criteria were established in the manner shown in Takhounts et al. (2003a). After thorough validation and injury criteria establishment (cumulative strain damage measure-CSDM for diffuse axonal injuries (DAI), relative motion damage measure-RMDM for acute subdural hematoma (ASDH), and dilatational damage measure-DDM for contusions and focal lesions), the model was used in investigation of mild TBI cases in living humans based on a set of head impact data taken from American football players at the collegiate level. It was found that CSDM and especially RMDM correlated well with angular acceleration and angular velocity. DDM was close to zero for most impacts due to their mild severity implying that cavitational pressure anywhere in the brain was not reached. Maximum principal strain was found to correlate well with RMDM and angular head kinematic measures. Maximum principal stress didn't correlate with any kinematic measure or injury metric. The model was then used in the investigation of brain injury potential in NHTSA conducted side impact tests. It was also used in parametric investigations of various "what if" scenarios, such as side versus frontal impact, to establish a potential link between head kinematics and injury outcomes. The new SIMon FEHM offers an advantage over the previous version because it is geometrically more representative of the human head. This advantage, however, is made possible at the expense of additional computational time.

Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

In American football, impacts to the helmet and the resulting head accelerations are the primary cause of concussion injury and potentially chronic brain injury. The purpose of this study was to quantify exposures to impacts to the head (frequency, location and magnitude) for individual collegiate football players and to investigate differences in head impact exposure by player position. A total of 314 players were enrolled at three institutions and 286,636 head impacts were recorded over three seasons. The 95th percentile peak linear and rotational acceleration and HITsp (a composite severity measure) were 62.7g, 4378 rad/s2, and 32.6, respectively. These exposure measures as well as the frequency of impacts varied significantly by player position and by helmet impact location. Running backs (RB) and quarter backs (QB) received the greatest magnitude head impacts, while defensive line (DL), offensive line (OL) and line backers (LB) received the most frequent head impacts (more than twice as many than any other position). Impacts to the top of the helmet had the lowest peak rotational acceleration (2387 rad/s2), but the greatest peak linear acceleration (72.4 g), and were the least frequent of all locations (13.7%) among all positions. OL and QB had the highest (49.2%) and the lowest (23%.7%) frequency, respectively, of front impacts. QB received the greatest magnitude (70.8g and 5428 rad/s2) and the most frequent (44% and 38.9%) impacts to the back of the helmet. This study quantified head impact exposure in collegiate football, providing data that is critical to advancing the understanding of the biomechanics of concussive injuries and sub-concussive head impacts.

In contrast to the publicly available data on the safety of automobiles, consumers have no analytical mechanism to evaluate the protective performance of football helmets. The objective of this article is to fill this void by introducing a new equation that can be used to evaluate helmet performance by integrating player head impact exposure and risk of concussion. The Summation of Tests for the Analysis of Risk (STAR) equation relates on-field impact exposure to a series of 24 drop tests performed at four impact locations and six impact energy levels. Using 62,974 head acceleration data points collected from football players, the number of impacts experienced for one full season was translated to 24 drop test configurations. A new injury risk function was developed from 32 measured concussions and associated exposure data to assess risk of concussion for each impact. Finally, the data from all 24 drop tests is combined into one number using the STAR formula that incorporates the predicted exposure and injury risk for one player for one full season of practices and games. The new STAR evaluation equation will provide consumers with a meaningful metric to assess the relative performance of football helmets.