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Soccer, one of the most popular sports in the world, has one of the highest rates of sports-related concussions. Additionally, soccer players are frequently exposed to nonconcussive impacts from intentionally heading the ball, a fundamental component of the sport. There have been many studies on head impact exposure in soccer, but few focus on soccer practices or practice activities. This study aimed to characterize the frequency and magnitude of head impacts in National Collegiate Athletic Association Division I female soccer practice activities using a custom-fit instrumented mouthpiece. Sixteen players were instrumented over the course of 54 practice sessions. Video analysis was performed to verify all mouthpiece-recorded events and classify practice activities. Category groupings of practice activities include technical training, team interaction, set pieces, position-specific, and other. Differences in head impact rates and peak resultant kinematics were observed across activity types and category groupings. Technical training had the highest impact rate compared to other category groupings. Impacts occurring during set piece activities had the highest mean kinematic values. Understanding drill exposure can help inform coaches on training plans aimed to reduce head impact exposure for their athletes.

The objective of this study was to utilize an instrumented mouthpiece sensor to characterize head kinematics experienced by grassroots dirt track race car drivers.Four dirt track race car drivers (ages 16-19) were instrumented with custom mouthpiece sensors capable of accurately measuring head motion during racing. Sensors were deployed before races and recorded tri-axial linear acceleration and rotational velocity for approximately 10 min at 200 Hz. Film review was performed to identify data associated with racing laps. For each lap, moving average kinematics were computed and subtracted from the head motion signals to obtain 'adjusted' head motion accounting for lower frequency variance due to periodic motion around the track. From adjusted data, linear and angular head perturbations (i.e., deviations from moving average) were extracted using a custom algorithm.Data was collected during 400 driver-races. A total of 2438 laps were segmented from mouthpiece recordings. The median (95th percentile) peak linear acceleration, rotational velocity, and rotational acceleration of all laps were 5.33 (8.28) g, 2.89 (4.60) rad/s, and 179 (310) rad/sMouthpiece sensors can be used to measure head kinematics during active racing. Laps, head perturbations, and crashes may be useful units of observation to describe typical head kinematic exposure experienced by drivers while racing. Subsequent research is needed to understand the associations between repetitive racing exposure and neurological function. Higher magnitude events (i.e., crashes) are not uncommon and may result in concussion or more severe injury. Results represent novel characterizations of head kinematic exposure experienced in a dirt track racing environment. This information may inform evidence-based strategies (e.g., vehicle/seat design) to improve driver safety.

Approximately 3.5 million youth and adolescents in the US play football, a sport with one of the highest rates of concussion. Repeated subconcussive head impact exposure (HIE) may lead to negative neurological sequelae. To understand HIE as an independent predictive variable, quantitative cumulative kinematic metrics have been developed to capture the volume (i.e., number), severity (i.e., magnitude), and frequency (i.e., time-weighting by the interval between head impacts). In this study, time-weighted cumulative HIE metrics were compared with directional changes in diffusion tensor imaging (DTI) metrics. Changes in DTI conducted on a per-season, per-player basis were assessed as a dependent variable. Directional changes were defined separately as increases and decreases in the number of abnormal voxels relative to non-contact sport controls. Biomechanical and imaging data from 117 athletes (average age 11.9 ± 1.0 years) enrolled in this study was analyzed. Cumulative HIE metrics were more strongly correlated with increases in abnormal voxels than decreases in abnormal voxels. Additionally, across DTI sub-measures, increases and decreases in mean diffusivity (MD) had the strongest relationships with HIE metrics (increases in MD: average R(2) = 0.1753, average p = 0.0002; decreases in MD: average R(2) = 0.0997, average p = 0.0073). This encourages further investigation into the physiological phenomena represented by directional changes.

The goal of this work was to collect on-track driver head kinematics using instrumented mouthpieces and characterize environmental exposure to accelerations and vibrations. Six NASCAR drivers were instrumented with custom-fit mouthpieces to collect head kinematic data. Devices were deployed at four tracks during practice and testing environments and configured to collect approximately 11 min of linear acceleration and rotational velocity data at 200 Hz. This continuous data collection, combined with film review, allowed extraction of complete laps of data. In addition to typical data processing methods, a moving-point average was calculated and subtracted from the overall signal for both linear acceleration and rotational velocity to determine the environmental component of head motion. The current analysis focuses on 42 full laps of data collected at four data collection events. The number of laps per track ranged from 2 to 23. Linear acceleration magnitudes for all 42 laps ranged from 2.46 to 7.48 g and rotational velocity ranged from 1.25 to 3.35 rad/s. After subtracting the moving average, linear acceleration ranged from 0.92 to 5.45 g and rotational velocity ranged from 0.57 to 2.05 rad/s. This study has established the feasibility of using an instrumented mouthpiece to measure head kinematics in NASCAR and presented a technique for isolating head motion due to cornering acceleration from those due to short-term perturbations experienced by the driver.

Understanding characteristics of head acceleration events (HAEs) in youth football is vital in developing strategies to improve athlete safety. This study aimed to characterize HAEs in youth football using an instrumented mouthpiece. Youth football athletes (ages 11-13) participating on two teams were enrolled in this study for one season. Each athlete was instrumented with a mouthpiece-based sensor throughout the season. HAEs were verified on film to ensure that mouthpiece-based sensors triggered during contact. The number of HAEs, peak resultant linear and rotational accelerations, and peak resultant rotational velocity were quantified. Mixed effects models were used to evaluate differences in mean kinematic metrics among all HAEs for session type, athlete position, and contact surface. A total of 5,292 HAEs were collected and evaluated from 30 athletes. The median (95th percentile) peak resultant linear acceleration, rotational acceleration, and rotational velocity was 9.5 g (27.0 g), 666.4 rad s

Soccer is a popular youth sport in the United States, but the effect of repetitive head impacts experienced during training on neurocognitive outcomes is not well understood. Modifying practice structure may be an avenue for reducing head impact exposure and concussion risk in soccer, but research has yet to characterize head impact exposure across common soccer activities. The objective of this study was to compare head impact exposure across common training activities in soccer. Eight female soccer players practicing on an age 15 and under (U15) club team participated in this study for two soccer seasons. Players wore an instrumented mouthpiece sensor during all practice and game sessions. Research personnel recorded duration of all activities performed by each player to characterize player-specific exposure time. Film review was performed to identify head contact events during each session and classify events according to the activity and drill the player was performing. Head impact exposure for each athlete was quantified in terms of peak kinematics and impact rate. Mixed effects models were used to compare peak kinematics and generalized linear models were used to compare impact rates across drills and activity type. Drill and activity type were associated with peak kinematics and impact rate. Technical training activities accounted for the second-highest exposure time and were associated with higher impact rates and lower mean kinematics than other activity types. Team interaction activities and game play were associated with the highest rotational kinematics but the lowest impact rates. Head impact exposure in female youth soccer is influenced by the type of activity in which the athlete is engaged. Interventions designed to reduce head impact frequency in female youth soccer can benefit from targeting technical training activities; whereas, interventions designed to reduce head impact magnitude should target team interaction and game activities.

Ice hockey has one of the highest concussion rates among youth sports. Sensor technology has been implemented in contact and collision sports to inform the frequency and severity of head impacts experienced on-ice. However, existing studies have utilized helmet-mounted sensors with limited accuracy. The objective of this study was to characterize head kinematics of contact events in a sample of youth boys' hockey players using a validated instrumented mouthpiece with improved accuracy. Head kinematics from 892 video-verified events were recorded from 18 athletes across 127 sessions. Median peak resultant linear acceleration, rotational velocity, and rotational acceleration of video-verified events were 7.4 g, 7.7 rad/s, and 576 rad/s2, respectively. Contact events occurred at a higher rate in games (2.48 per game) than practices (1.30 per practice). Scenarios involving head contact had higher peak kinematics than those without head contact. This study improves our understanding of head kinematics in boys' youth hockey.

Hockey is a fast-paced sport known for body checking, or intentional collisions used to separate opponents from the puck. Exposure to these impacts is concerning, as evidence suggests head impact exposure (HIE), even if noninjurious, can cause long-term brain changes. Currently, there is limited understanding of the effect of impact direction and collision speed on HIE. Video analysis was used to determine speed and direction for 162 collisions from 13 youth athletes. These data were paired with head kinematic data collected with an instrumented mouthpiece. Relationships between peak resultant head kinematics and speeds were evaluated with linear regression. Mean athlete speeds and relative velocity between athletes ranged from 2.05 to 2.76 m/s. Mean peak resultant linear acceleration, rotational velocity, and rotational acceleration were 13.1 g, 10.5 rad/s, and 1112 rad/s2, respectively. Significant relationships between speeds and head kinematics emerged when stratified by contact characteristics. HIE also varied by direction of collision; most collisions occurred in the forward-oblique (ie, offset from center) direction; frontal collisions had the greatest magnitude peak kinematics. These findings indicate that HIE in youth hockey is influenced by speed and direction of impact. This study may inform future strategies to reduce the severity of HIE in hockey.

The purpose of this study is to assess the relationship between regional white matter diffusion imaging changes and finite element strain measures in nonconcussed youth football players. Pre- and post-season diffusion-weighted imaging was performed in 102 youth football subject-seasons, in which no concussions were diagnosed. The diffusion data were normalized to the IXI template. Percent change in fractional anisotropy (%ΔFA) images were generated. Using data from the head impact telemetry system, the cumulative maximum principal strain one times strain rate (CMPS1 × SR), a measure of the cumulative tensile brain strain and strain rate for one season, was calculated for each subject. Two linear regression analyses were performed to identify significant positive or inverse relationships between CMPS1 × SR and %ΔFA within the international consortium for brain mapping white matter mask. Age, body mass index, days between pre- and post-season imaging, previous brain injury, attention disorder diagnosis, and imaging protocol were included as covariates. False discovery rate correction was used with corrected alphas of 0.025 and voxel thresholds of zero. Controlling for all covariates, a significant, positive linear relationship between %ΔFA and CMPS1 × SR was identified in the bilateral cingulum, fornix, internal capsule, external capsule, corpus callosum, corona radiata, corticospinal tract, cerebral and middle cerebellar peduncle, superior longitudinal fasciculus, and right superior fronto-occipital fasciculus. Post hoc analyses further demonstrated significant %ΔFA differences between high-strain football subjects and noncollision control athletes, no significant %ΔFA differences between low-strain subjects and noncollision control athletes, and that CMPS1 × SR significantly explained more %ΔFA variance than number of head impacts alone.

Various studies have characterized head kinematics in specific everyday activities by looking at linear and/or rotational acceleration characteristics, but each has evaluated a limited number of activities. Furthermore, these studies often present dissimilar and sometimes incomplete descriptions of the resulting kinematics, so the characteristics of normal everyday activities as a whole are not easily collectively summarized. The purpose of this study was to evaluate the literature investigating head kinematics associated with everyday activities and to generate a comprehensive kinematic boundary envelope describing these motions. The envelope constructed constitutes the current state of published knowledge regarding ‘normally occurring’ head accelerations. The envelope of kinematics represents activities commonly encountered and posing zero to minimal risk of injury to healthy individuals. Several kinematic measures, including linear accelerations, rotational velocities, and rotational accelerations, one may encounter as a result of normal everyday activities are summarized. A total of 11 studies encompassing 49 unique activities were evaluated. Examples of activities include sitting in a chair, jumping off a step, running, and walking. The peak resultant linear accelerations of the head reported in the literature were all less than 15 g, while the peak resultant rotational accelerations and rotational velocities approach 1375 rad/s2 and 12.8 rad/s, respectively. The resulting design envelope can be used to understand the range of acceleration magnitudes a typical active person can expect to experience. The results are also useful to compare to other activities exposing the head to motion or impact including sports, military, automotive, aerospace and other sub-injurious and injurious events.

While many research efforts have focused on head impact exposure in professional soccer, there have been few studies characterizing exposure at the youth level. The aim of this study is to evaluate a new instrumentation approach and collect some of the first head impact exposure data for youth female soccer players. Athletes were instrumented with custom-fit mouthpieces that measure head impacts. Detailed video analysis was conducted to identify characteristics describing impact source (e.g., kick, header, throw). A total of 763 verified head impacts were collected over 23 practices and 8 games from 7 athletes. The median peak linear accelerations, rotational velocities, and rotational accelerations of all impacts were 9.4 g, 4.1 rad/s, and 689 rad/s2, respectively. Pairwise comparisons resulted in statistically significant differences in kinematics by impact source. Headers following a kicked ball had the highest accelerations and velocity when compared to headers from thrown or another header.

OBJECTIVE The objective of this study was to characterize changes in head impact exposure (HIE) across multiple football seasons and to determine whether changes in HIE correlate with changes in imaging metrics in youth football players. METHODS On-field head impact data and pre- and postseason imaging data, including those produced by diffusion tensor imaging (DTI), were collected from youth football athletes with at least two consecutive seasons of data. ANCOVA was used to evaluate HIE variations (number of impacts, peak linear and rotational accelerations, and risk-weighted cumulative exposure) by season number. DTI scalar metrics, including fractional anisotropy, mean diffusivity, and linear, planar, and spherical anisotropy coefficients, were evaluated. A control group was used to determine the number of abnormal white matter voxels, which were defined as 2 standard deviations above or below the control group mean. The difference in the number of abnormal voxels between consecutive seasons was computed for each scalar metric and athlete. Linear regression analyses were performed to evaluate relationships between changes in HIE metrics and changes in DTI scalar metrics. RESULTS There were 47 athletes with multiple consecutive seasons of HIE, and corresponding imaging data were available in a subsample (n = 19) of these. Increases and decreases in HIE metrics were observed among individual athletes from one season to the next, and no significant differences (all p > 0.05) in HIE metrics were observed by season number. Changes in the number of practice impacts, 50th percentile impacts per practice session, and 50th percentile impacts per session were significantly positively correlated with changes in abnormal voxels for all DTI metrics. CONCLUSIONS These results demonstrate a significant positive association between changes in HIE metrics and changes in the numbers of abnormal voxels between consecutive seasons of youth football. Reducing the number and frequency of head impacts, especially during practice sessions, may decrease the number of abnormal imaging findings from one season to the next in youth football.

Concerns about the effects of intentional heading in soccer have led to regulatory restrictions on headers for youth players. However, there is limited data describing how header exposure varies across age levels, and few studies have attempted to compare head impact exposure across different levels of play with the same sensor. Additionally, little is known about the biomechanical response of the brain to header impacts. The objective of this study was to evaluate head kinematics and the resulting tissue-level brain strain associated with intentional headers among youth and collegiate female soccer players. Six youth and 13 collegiate participants were instrumented with custom mouthpiece-based sensors measuring six-degree-of-freedom head kinematics of headers during practices and games. Kinematics of film-verified headers were used to drive impact simulations with a detailed brain finite element model to estimate tissue-level strain. Linear and rotational head kinematics and strain metrics, specifically 95th percentile maximum principal strain (e1,95) and the area under the cumulative strain damage measure curve (VSM1), were compared across levels of play (i.e., youth vs. collegiate) while adjusting for session type and ball delivery method. A total of 483 headers (n = 227 youth, n = 256 collegiate) were analyzed. Level of play was significantly associated with linear acceleration, rotational acceleration, rotational velocity, e1,95, and VSM1. Headers performed by collegiate players had significantly greater mean head kinematics and strain metrics compared to those performed by youth players (all p

Head impact exposure is often quantified using peak resultant kinematics. While kinematics describes the inertial response of the brain to impact, they do not fully capture the dynamic brain response. Strain, a measure of the tissue-level response of the brain, may be a better predictor of injury. In this study, kinematic and strain metrics were compared to contact characteristics in youth football. Players on 2 opposing teams were instrumented with head impact sensors to record impact kinematics. Video was collected to identify contact scenarios involving opposing instrumented players (ie, paired contact scenarios) and code contact characteristics (eg, player role, impact location). A previously validated, high-resolution brain finite element model, the atlas-based brain model, was used to simulate head impacts and calculate strain metrics. Fifty-two paired contact scenarios (n = 105 impacts) were evaluated. Lighter players tended to have greater biomechanical metrics compared to heavier players. Impacts to the top of the helmet were associated with lower strain metrics. Overall, strain was better correlated with rotational kinematics, suggesting these metrics may be better predictors of the tissue-level brain response than linear kinematics. Understanding the effect of contact characteristics on brain strain will inform future efforts to improve sport safety.

Athletes participating in contact sports are exposed to repetitive subconcussive head impacts that may have long-term neurological consequences. To better understand these impacts and their effects, head impacts are often measured during football to characterize head impact exposure and estimate injury risk. Despite widespread use of kinematic-based metrics, it remains unclear whether any single metric derived from head kinematics is well-correlated with measurable changes in the brain. This shortcoming has motivated the increasing use of finite element (FE)-based metrics, which quantify local brain deformations. Additionally, quantifying cumulative exposure is of increased interest to examine the relationship to brain changes over time. The current study uses the atlas-based brain model (ABM) to predict the strain response to impacts sustained by 116 youth football athletes and proposes 36 new, or derivative, cumulative strain-based metrics that quantify the combined burden of head impacts over the course of a season. The strain-based metrics developed and evaluated for FE modeling and presented in the current study present potential for improved analytics over existing kinematically-based and cumulative metrics. Additionally, the findings highlight the importance of accounting for directional dependence and expand the techniques to explore spatial distribution of the strain response throughout the brain.

Soccer players are regularly exposed to head impacts by intentionally heading the ball. Evidence suggests repetitive subconcussive head impacts may affect the brain, and females may be more vulnerable to brain injury than males. This study aimed to characterize head impact exposure among National Collegiate Athletic Association women’s soccer players using a previously validated mouthpiece-based sensor. Sixteen players were instrumented during 72 practices and 24 games. Head impact rate and rate of risk-weighted cumulative exposure were compared across session type and player position. Head kinematics were compared across session type, impact type, player position, impact location, and ball delivery method. Players experienced a mean (95% confidence interval) head impact rate of 0.468 (0.289 to 0.647) head impacts per hour, and exposure rates varied by session type and player position. Headers accounted for 89% of head impacts and were associated with higher linear accelerations and rotational accelerations than nonheader impacts. Headers in which the ball was delivered by a long kick had greater peak kinematics (all P

The objective of this research was to characterize head impacts with a validated mouthpiece sensor in competitive youth female soccer players during a single season with a validated mouthpiece sensor. Participants included 14 youth female soccer athletes across 2 club-level teams at different age levels (team 1, ages 12–13 y; team 2, ages 14–15 y). Head impact and time-synchronized video data were collected for 66 practices and games. Video data were reviewed to characterize the type and frequency of contact experienced by each athlete. A total of 2216 contact scenarios were observed; heading the ball (n = 681, 30.7%) was most common. Other observed contact scenarios included collisions, dives, falls, and unintentional ball contact. Team 1 experienced a higher rate of headers per player per hour of play than team 2, while team 2 experienced a higher rate of collisions and dives. A total of 935 video-verified contact scenarios were concurrent with recorded head kinematics. While headers resulted in a maximum linear acceleration of 56.1g, the less frequent head-to-head collisions (n = 6) resulted in a maximum of 113.5g. The results of this study improve the understanding of head impact exposure in youth female soccer players and inform head impact exposure reduction in youth soccer.

Lower extremity injuries are the most frequent Abbreviated Injury Scale (AIS) 2 injury for drivers in frontal crashes. The objective was to reconstruct 11 real-world motor vehicle crashes (2 with AIS 2+ distal lower extremity injury and 9 without lower extremity injury) and to analyze the vehicle parameters and driver attributes that affect injury risk.Eleven frontal crashes were reconstructed with a finite element simplified vehicle model (SVM) using a semi-automated optimization method. The SVM was tuned to each corresponding vehicle and the Total HUman Model for Safety (THUMS) Ver 4.01 was scaled and positioned in a baseline configuration to mimic the documented precrash driver posture. The event data recorder crash pulse was applied as the boundary condition for each case. Additionally, for the 2 cases with lower extremity injury, 120 simulations to quantify the uncertainty and response variation were performed varying the following parameters using a Latin hypercube design of experiment (DOE): seat track position, seatback angle, steering column angle, steering column position, and D-ring height. Injury metrics implemented within THUMS were calculated from the femur, tibia, and ankle and cross-compared among the 11 baseline cases using tibia index and multiple injury risk functions. Kinetic and kinematic data from the 120-simulation DOE were analyzed and fit to regression models to examine any causal relationship between occupant positioning and lower extremity injury risk.Of the 11 real-world crashes, both cases with lower extremity injuries resulted in elevated tibia axial forces and resultant bending moments, compared to the 9 cases without lower extremity injury. The average tibia index of the 2 cases with distal lower extremity injury (left: 1.79; right: 1.19) was higher than that in the 9 cases without lower extremity injury (left: 1.16, P =.024; right: 0.82, P =.024). An increased risk of AIS 2+ tibia shaft (33.6%), distal tibia and hindfoot (20.0%), as well as ankle malleolar (14.5%) fracture was also observed for the injured compared to the noninjured cases. Rearward seat track position, reclined seat back angle, and reduced seat height were correlated with elevated tibia axial force and increased tibia index, imposing additional lower extremity injury risk.This study provides a computational framework for assessing lower extremity injuries and elucidates the effect of precrash driver posture on lower extremity injury risk while accounting for vehicle parameters and driver attributes. Results from the study aid in the evaluation of real-world injury data, the understanding of factors contributing to injury risk, and the prevention of lower extremity injuries.

Although most head impacts in soccer are headers, limited knowledge exists about how header magnitude varies by on-field scenario. This study aimed to compare head kinematics during on-field headers by play state (i.e., corner kick, goal kick, free kick, throw-in, drill, or live ball), intent (i.e., pass, shot, or clearance), and outcome (i.e., successful or unsuccessful). Fifteen female collegiate soccer players were instrumented with mouthpiece-based head impact sensors during 72 practices and 24 games. A total of 336 headers were verified and contextualized via film review. Play state was associated with peak linear acceleration, rotational acceleration, and rotational velocity (all p .001) while outcome was associated with peak linear acceleration (p .010). Header intent was not significantly associated with any kinematic metric. Headers during corner kicks (22.9 g, 2189.3 rad/s

Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

OBJECTIVEThis study evaluated the frequency, magnitude, and location of head impacts in practice drills within a youth football team to determine how head impact exposure varies among different types of drills.METHODSOn-field head impact data were collected from athletes participating in a youth football team for a single season. Each athlete wore a helmet instrumented with a Head Impact Telemetry (HIT) System head acceleration measurement device during all preseason, regular season, and playoff practices. Video was recorded for all practices, and video analysis was performed to verify head impacts and assign each head impact to a specific drill. Eleven drills were identified: dummy/sled tackling, install, special teams, Oklahoma, one-on-one, open-field tackling, passing, position skill work, multiplayer tackle, scrimmage, and tackling drill stations. Generalized linear models were fitted to log-transformed data, and Wald tests were used to assess differences in head accelerations and impact rates.RESULTSA total of 2125 impacts were measured during 30 contact practices in 9 athletes (mean age 11.1 ± 0.6 years, mean mass 44.9 ± 4.1 kg). Open-field tackling had the highest median and 95th percentile linear accelerations (24.7g and 97.8g, respectively) and resulted in significantly higher mean head accelerations than several other drills. The multiplayer tackle drill resulted in the highest head impact frequency, with an average of 0.59 impacts per minute per athlete, but the lowest 95th percentile linear accelerations of all drills. The front of the head was the most common impact location for all drills except dummy/sled tackling.CONCLUSIONSHead impact exposure varies significantly in youth football practice drills, with several drills exposing athletes to high-magnitude and/or high-frequency head impacts. These data suggest that further study of practice drills is an important step in developing evidence-based recommendations for modifying or eliminating certain high-intensity drills to reduce head impact exposure and injury risk for all levels of play.

The objective of this study was to compare the performance of six validated brain finite element (FE) models to localized brain motion validation data in five experimental configurations. Model performance was measured using the objective metric CORA (CORrelation and Analysis), where higher ratings represent better correlation. The KTH model achieved the highest average CORA rating, and the ABM received the highest average rating among models robustly validated against five cadaver impacts in three directions. This technique can be more frequently employed to build better models and, when associated limitations are well understood, to compare inter-model performance under similar conditions.

The objective of this study was to develop a mouthpiece sensor with improved head kinematic measurement for use in non-helmeted and helmeted sports through laboratory validation and pilot field deployment in female youth soccer. For laboratory validation, data from the mouthpiece sensor was compared to standard sensors mounted in a headform at the center of gravity as the headform was struck with a swinging pendulum. Linear regression between peak kinematics measured from the mouthpiece and headform showed strong correlation, with r2 values of 0.95 (slope = 1.02) for linear acceleration, 1.00 (slope = 1.00) for angular velocity, and 0.97 (slope = 0.96) for angular acceleration. In field deployment, mouthpiece data were collected from four female youth soccer players and time-synchronized with film. Film-verified events (n = 915) were observed over 9 practices and 5 games, and 632 were matched to a corresponding mouthpiece event. This resulted in an overall sensitivity of 69.2% and a positive predictive value of 80.3%. This validation and pilot field deployment data demonstrates that the mouthpiece provides highly accurate measurement of on-field head impact data that can be used to further study the effects of impact exposure in both helmeted and non-helmeted sports.

To understand the biomechanical basis of head impacts, finite element (FE) modeling is used to estimate the response throughout the brain in various impact conditions. FE simulation of head motion requires a complete description of kinematics, such as six degrees of freedom (6DOF) linear and rotational acceleration curves defining the boundary conditions. These are not available from many common head impact sensors such as the Head Impact Telemetry (HIT) System. At the same time, there are hundreds of thousands of impacts, likely millions of impacts, collected by HITS which represent an underutilized resource for computational modeling. The goal of this study was to develop an algorithm to determine 6DOF acceleration curves based on the corresponding HITS output data for use in FE modeling. The transformation algorithm was developed from a dataset of 14,767 head impacts collected with the HIT System and the corresponding 6DOF information provided by a published algorithm for this study. The impacts were sorted into impact regions and classified by the polarity of peak accelerations, and characteristic curves for each polarity combination were calculated. The algorithm was validated against 50 random impacts by comparing predicted and true acceleration curves using an objective curve comparison metric, CORA, to quantify error. These results demonstrate the algorithm accurately estimates 6DOF motion characteristics from 5DOF inputs sufficient for the purpose of performing basic biomechanical analyses of the impacts through FE modeling.

ObjectiveThe objective of this pilot study was to characterize head impact exposure in a sample of youth boys' ice hockey using a novel instrumented mouthpiece, improving accuracy.BackgroundFrom 2010 to 2018 youth ice hockey saw a 15% increase in participation, despite growing concerns for concussion risk in contact sports. While contact sports with similar rates of concussion have been subjected to rigorous study, head impact exposure in youth ice hockey has been largely underexplored. Existing youth studies have utilized helmet-mounted sensors, which are associated with error due to poor coupling with the skull.Design/MethodsCustom mouthpieces containing a tri-axial accelerometer and gyroscope were fit to seven enrolled athletes, and monitored during practices and games throughout the season. Linear acceleration and rotational velocity of the head were recorded for 60 ms when 5 g was exceeded on any axis for at least 3 ms. Time-synchronized film was reviewed to identify the contact scenario and head contact. Summary statistics of kinematics were calculated by scenario and presence of head contact.ResultsA total of 465 events were recorded over 25 weeks. Of these events 25% involved head contact; 92% of all contact scenarios were board checks, falls, or ice checks. Events involving head contact (i.e., head impacts) had median [95th percentile] peak linear acceleration, rotational velocity, and angular acceleration of 8.1 [30.9] g, 7.9 [20.2] rad/s, and 614 [2673] rad/s2, respectively. Events not involving head contact had median [95th percentile] peak linear acceleration, rotational velocity, and angular acceleration of 6.6 [43.8] g, 6.5 [17.5] rad/s, and 455 [4115] rad/s2, respectively.ConclusionsThe majority of the recorded events could be classified as board checks, falls, or ice checks. Median peak kinematics were higher for head impacts than non-head impact events. In contrast, 95th percentile linear and angular accelerations were greater for impacts not involving head contact.

During normal participation in football, players are exposed to repetitive subconcussive head impacts, or impacts that do not result in signs and symptoms of concussion. To better understand the effects of repetitive subconcussive impacts, the biomechanics of on-field head impacts and resulting brain deformation need to be well characterized. The current study evaluates local brain response to typical youth football head impacts using the atlas-based brain model (ABM), an anatomically accurate brain finite element (FE) model. Head impact kinematic data were collected from three local youth football teams using the Head Impact Telemetry (HIT) System. The azimuth and elevation angles were used to identify impacts near six locations of interest, and low, moderate, and high acceleration magnitudes (5th, 50th, and 95th percentiles, respectively) were calculated from the grouped impacts for FE simulation. Strain response in the brain was evaluated by examining the range and peak maximum principal strain (MPS) values in each element. A total of 40,538 impacts from 119 individual athletes were analyzed. Impacts to the facemask resulted in 0.18 MPS for the high magnitude impact category. This was 1.5 times greater than the oblique impact location, which resulted in the lowest strain value of 0.12 for high magnitude impacts. Overall, higher strains resulted from a 95th percentile lateral impact (41.0g, 2556 rad/sec(2)) with two predominant axes of rotation than from a 95th percentile frontal impact (67.6g, 2641 rad/sec(2)) with a single predominant axis of rotation. These findings highlight the importance of accounting for directional dependence and relative contribution of axes of rotation when evaluating head impact response.

Head impact exposure in popular contact sports is not well understood, especially in the youth population, despite recent advances in impact-sensing technology which has allowed widespread collection of real-time head impact data. Previous studies indicate that a custom-instrumented mouthpiece is a superior method for collecting accurate head acceleration data. The objective of this study was to evaluate the efficacy of mounting a sensor device inside an acrylic retainer form factor to measure six-degrees-of-freedom (6DOF) head kinematic response. This study compares 6DOF mouthpiece kinematics at the head center of gravity (CG) to kinematics measured by an anthropomorphic test device (ATD). This study found that when instrumentation is mounted in the rigid retainer form factor, there is good coupling with the upper dentition and highly accurate kinematic results compared to the ATD. Peak head kinematics were correlated with r2 > 0.98 for both rotational velocity and linear acceleration and r2 = 0.93 for rotational acceleration. These results indicate that a rigid retainer-based form factor is an accurate and promising method of collecting head impact data. This device can be used to study head impacts in helmeted contact sports such as football, hockey, and lacrosse as well as nonhelmeted sports such as soccer and basketball. Understanding the magnitude and frequency of impacts sustained in various sports using an accurate head impact sensor, such as the one presented in this study, will improve our understanding of head impact exposure and sports-related concussion.

Approximately 5,000,000 athletes play organized football in the United States, and youth athletes constitute the largest proportion with ∼3,500,000 participants. Investigations of head impact exposure (HIE) in youth football have been limited in size and duration. The objective of this study was to evaluate HIE of athletes participating in three age- and weight-based levels of play within a single youth football organization over four seasons. Head impact data were collected using the Head Impact Telemetry (HIT) System. Mixed effects linear models were fitted, and Wald tests were used to assess differences in head accelerations and number of impacts among levels and session type (competitions vs. practices). The three levels studied were levels A (n = 39, age = 10.8 ± 0.7 years, weight = 97.5 ± 11.8 lb), B (n = 48, age = 11.9 ± 0.5 years, weight = 106.1 ± 13.8 lb), and C (n = 32, age = 13.0 ± 0.5 years, weight = 126.5 ± 18.6 lb). A total of 40,538 head impacts were measured. The median/95th percentile linear head acceleration for levels A, B, and C was 19.8/49.4g, 20.6/51.0g, and 22.0/57.9g, respectively. Level C had significantly greater mean linear acceleration than both levels A (p = 0.005) and B (p = 0.02). There were a significantly greater number of impacts per player in a competition than in a practice session for all levels (A, p = 0.0005, B, p = 0.0019, and C, p

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.

The brain-skull interface plays an important role in the strain and pressure response of the brain due to impact. In this study, a finite element (FE) model was developed from a brain atlas, representing an adult brain, by converting each 1mm isotropic voxel into a single element of the same size using a custom code developed in MATLAB. This model includes the brain (combined cerebrum and cerebellum), cerebrospinal fluid (CSF), ventricles, and a rigid skull. A voxel-based approach to develop a FE model causes the outer surface of each part to be stair-stepped, which may affect the stress and strain measurements at interfaces between parts. To improve the interaction between the skull, CSF, and brain surfaces, a previously developed mesh smoothing algorithm based on a Laplacian non-shrinking smoothing algorithm was applied to the FE model. This algorithm not only applies smoothing to the surface of the model, but also to the interfaces between the brain, CSF, and skull, while preserving volume and element quality. Warpage, jacobian, aspect ratio, and skew were evaluated and reveal that99% of the elements retain good element quality. Future work includes implementation of contact definitions to accurately represent the brain-skull interface and to ultimately better understand and predict head injury.

Traumatic brain injury (TBI) is a leading cause of disability and injury-related death, accounting for nearly one third of all injury-related deaths. To prevent and understand these types of injuries, finite element models can be employed. In this study, an anatomically accurate finite element model was developed from the International Consortium for Brain Mapping (ICBM) using a voxel-based mesh generation approach. The aim of this study was to compare relative brain displacement of the atlas-based brain model (ABM) to cadaveric data. In these experiments, neutral density targets (NDTs) were implanted in the brain and their relative motion with respect to the skull was recorded. The same boundary conditions were applied to ABM and the relative displacements of the nodes nearest to the physical location of each NDT were computed. Initial simulation and validation show good agreement with experimental data. The data obtained in this study and further development of this model will help us understand the biomechanics of head injury as well as provide a tool to predict and prevent brain injury.

Background Improved understanding of the occupant loading conditions in real world crashes is critical for injury prevention and new vehicle design. The purpose of this study was to develop a robust methodology to reconstruct injuries sustained in real world crashes using vehicle and human body finite element models. Methods A real world near-side impact crash was selected from the Crash Injury Research and Engineering Network (CIREN) database. An average sedan was struck at approximately the B-pillar with a 290 degree principal direction of force by a lightweight pickup truck, resulting in a maximum crush of 45 cm and a crash reconstruction derived Delta-V of 28 kph. The belted 73-year-old midsized female driver sustained severe thoracic injuries, serious brain injuries, moderate abdominal injuries, and no pelvic injury. Vehicle finite element models were selected to reconstruct the crash. The bullet vehicle parameters were heuristically optimized to match the crush profile of the simulated struck vehicle and the case vehicle. The Total Human Model for Safety (THUMS) midsized male finite element model of the human body was used to represent the case occupant and reconstruct her injuries using the head injury criterion (HIC), half deflection, thoracic trauma index (TTI), and pelvic force to predict injury risk. A variation study was conducted to evaluate the robustness of the injury predictions by varying the bullet vehicle parameters. Results The THUMS thoracic injury metrics resulted in a calculated risk exceeding 90% for AIS3+ injuries and 70% risk of AIS4+ injuries, consistent with her thoracic injury outcome. The THUMS model predicted seven rib fractures compared to the case occupant's 11 rib fractures, which are both AIS3 injuries. The pelvic injury risk for AIS2+ and AIS3+ injuries were 37% and 2.6%, respectively, consistent with the absence of pelvic injury. The THUMS injury prediction metrics were most sensitive to bullet vehicle location. The maximum 95% confidence interval width for the mean injury metrics was only 5% demonstrating high confidence in the THUMS injury prediction. Conclusions This study demonstrates a variation study methodology in which human body models can be reliably used to robustly predict injury probability consistent with real world crash injury outcome.