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Soccer is a unique sport where players purposefully and voluntarily use their unprotected heads to manipulate the direction of the ball. There are limited soccer head impact exposure data to further study brain injury risks. The objective of the current study was to combine validated mouthpiece sensors with comprehensive video analysis methods to characterize head impact exposure and biomechanics in university varsity women’s soccer. Thirteen female soccer athletes were instrumented with mouthpiece sensors to record on-field head impacts during practices, scrimmages, and games. Multi-angle video was obtained and reviewed for all on-field activity to verify mouthpiece impacts and identify contact scenarios. We recorded 1307 video-identified intentional heading impacts and 1011 video-verified sensor impacts. On average, athletes experienced 1.83 impacts per athlete-exposure, with higher exposure in practices than games/scrimmages. Median and 95th percentile peak linear and peak angular accelerations were 10.0, 22.2 g, and 765, 2296 rad/s2, respectively. Long kicks, top of the head impacts and jumping headers resulted in the highest impact kinematics. Our results demonstrate the importance of investigating and monitoring head impact exposure during soccer practices, as well as the opportunity to limit high-kinematics impact exposure through heading technique training and reducing certain contact scenarios.

Contact sports players frequently sustain head impacts, most of which are mild impacts exhibiting 10-30 g peak head center-of-gravity (CG) linear acceleration. Wearable head impact sensors are commonly used to measure exposure and typically detect impacts using a linear acceleration threshold. However, linear acceleration across the head can substantially vary during 6-degree-of-freedom motion, leading to triggering biases that depend on sensor location and impact condition. We conducted an analytical investigation with impact characteristics extracted from on-field American football and soccer data. We assumed typical mouthguard sensor locations and evaluated whether simulated multi-directional impacts would trigger recording based on per-axis or resultant acceleration thresholding. Across 1387 impact directions, a 10g peak CG linear acceleration impact would trigger at only 24.7% and 31.8% of directions based on a 10 g per-axis and resultant acceleration threshold, respectively. Anterior impact locations had lower trigger rates and even a 30 g impact would not trigger recording in some directions. Such triggering biases also varied by sensor location and linear-rotational head kinematics coupling. Our results show that linear acceleration-based impact triggering could lead to considerable bias in head impact exposure measurements. We propose a set of recommendations to consider for sensor manufacturers and researchers to mitigate this potential exposure measurement bias.

Standing balance deficits are prevalent after concussions and have also been reported after subconcussive head impacts. However, the mechanisms underlying such deficits are not fully understood. The objective of this review is to consolidate evidence linking head impact biomechanics to standing balance deficits. Mechanical energy transferred to the head during impacts may deform neural and sensory components involved in the control of standing balance. From our review of acute balance-related changes, concussions frequently resulted in increased magnitude but reduced complexity of postural sway, while subconcussive studies showed inconsistent outcomes. Although vestibular and visual symptoms are common, potential injury to these sensors and their neural pathways are often neglected in biomechanics analyses. While current evidence implies a link between tissue deformations in deep brain regions including the brainstem and common post-concussion balance-related deficits, this link has not been adequately investigated. Key limitations in current studies include inadequate balance sampling duration, varying test time points, and lack of head impact biomechanics measurements. Future investigations should also employ targeted quantitative methods to probe the sensorimotor and neural components underlying balance control. A deeper understanding of the specific injury mechanisms will inform diagnosis and management of balance deficits after concussions and subconcussive head impact exposure.

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: 1) a centralized, open-source platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and 2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics., 21 pages, 3 figures, 1 table

To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old. We used a multifrequency magnetic resonance elastography (MRE) protocol to analyze the effect of age on frequency-dependent (storage and loss moduli, G′ and G″, respectively) and frequency-independent parameters (μ1, μ2, and η, as determined by a standard linear solid model) of the cerebral parenchyma, cortical gray matter (GM), white matter (WM), and subcortical GM structures of 46 healthy male and female subjects. The multifrequency behavior of the brain and frequency-independent parameters were analyzed across different age groups. The annual change rate ranged from − 0.32 to − 0.36% for G′ and − 0.43 to − 0.55% for G″ for the cerebral parenchyma, cortical GM, and WM. For the subcortical GM, changes in G′ ranged from − 0.18 to − 0.23%, and G″ changed − 0.43%. Interestingly, males exhibited decreased elasticity, while females exhibited decreased viscosity with respect to age in some regions of subcortical GM. Significantly decreased values were also found in subjects over 60 years old. Values of G′ and G″ at 60 Hz and the frequency-independent μ2 of the caudate, putamen, and thalamus may serve as parameters that characterize the aging effect on the brain. The decrease in brain stiffness accelerates in elderly subjects. • We used a multifrequency MRE protocol to assess changes in the mechanical properties of the brain with age. • Frequency-dependent (storage moduli G′ and loss moduli G″) and frequency-independent (μ1, μ2, and η) parameters can bequantitatively measured by our protocol. • The decreased value of viscoelastic properties due to aging varies in different regions of subcortical GM in males and females, and the decrease in brain stiffness is accelerated in elderly subjects over 60 years old.

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.

Magnetic particle imaging is an emerging tomographic technique with the potential for simultaneous high-resolution, high-sensitivity, and real-time imaging. Magnetic particle imaging is based on the unique behavior of superparamagnetic iron oxide nanoparticles modeled by the Langevin theory, with the ability to track and quantify nanoparticle concentrations without tissue background noise. It is a promising new imaging technique for multiple applications, including vascular and perfusion imaging, oncology imaging, cell tracking, inflammation imaging, and trauma imaging. In particular, many neuroimaging applications may be enabled and enhanced with magnetic particle imaging. In this review, we will provide an overview of magnetic particle imaging principles and implementation, current applications, promising neuroimaging applications, and practical considerations.

The contact nature of American football has made head acceleration exposure a concern. We aimed to quantify the head kinematics associated with direct helmet contact and inertial head loading events in collegiate-level American football. A cohort of collegiate-level players were equipped with instrumented mouthguards synchronised with time-stamped multiple camera-view video footage of matches and practice. Video-verified contact events were identified as direct helmet contact or inertial head loading events and categorised as blocking, blocked, tackling, tackled or ground contact. Linear mixed-effects models were utilised to compare peak head kinematics between contact event categories. The timestamp-based cross-verification of the video analysis and instrumented mouthguard approach resulted in 200 and 328 direct helmet contact and inertial head loading cases, respectively. Median linear acceleration, angular acceleration and angular velocity for inertial head loading cases was greater than direct helmet contact events by 8% (p = 0.007), 55% (p < 0.001) and 4% (p = 0.007), respectively. Median head kinematics for all contact event categories appeared similar with no pairwise comparison resulting in statistical significance (p > 0.05). The study highlights the potential of combining qualitative video analysis with in-vivo head kinematics measurements. The findings suggest that a number of direct helmet contact events sustained in American football are of lower magnitude to what is sustained during regular play (i.e. from inertial head loading). Additionally, the findings illustrate the importance of including all contact events, including direct helmet contact and inertial head loading cases, when assessing head acceleration exposure and player load during a season of American football.

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand torque assistance to wheelchair users. Although the available power can reduce the physical load of wheelchair propulsion, it may also cause maneuverability and controllability issues. Commercially-available PAPAW controllers are insensitive to environmental changes, leading to inefficient and/or unsafe wheelchair movements. In this regard, adaptive velocity/torque control strategies could be employed to improve safety and stability. To investigate this objective, we propose a context-aware sensory framework to recognize terrain conditions. In this paper, we present a learning-based terrain classification framework for PAPAWs. Study participants performed various maneuvers consisting of common daily-life wheelchair propulsion routines on different indoor and outdoor terrains. Relevant features from wheelchair frame-mounted gyroscope and accelerometer measurements were extracted and used to train and test the proposed classifiers. Our findings revealed that a one-stage multi-label classification framework has a higher accuracy performance compared to a two-stage classification pipeline with an indoor-outdoor classification in the first stage. We also found that, on average, outdoor terrains can be classified with higher accuracy (90%) compared to indoor terrains (65%). This framework can be used for real-time terrain classification applications and provide the required information for an adaptive velocity/torque controller design.

The head is kinematically constrained to the torso through the spine and thus, the spine dictates the amount of output head angular motion expected from an input impact. Here, we investigate the spinal kinematic constraint by analyzing the head instantaneous center of rotation (HICOR) with respect to the torso in head/neck sagittal extension and coronal lateral flexion during mild loads applied to 10 subjects. We found the mean HICOR location was near the C5-C6 intervertebral joint in sagittal extension, and T2-T3 intervertebral joint in coronal lateral flexion. Using the impulse-momentum relationship normalized by subject mass and neck length, we developed a non-dimensional analytical ratio between output angular velocity and input linear impulse as a function of HICOR location. The ratio was 0.65 and 0.50 in sagittal extension and coronal lateral flexion respectively, implying 30% greater angular velocities in sagittal extension given an equivalent impulse. Scaling to subject physiology also predicts larger required impulses given greater subject mass and neck length to achieve equivalent angular velocities, which was observed experimentally. Furthermore, the HICOR has greater motion in sagittal extension than coronal lateral flexion, suggesting the head and spine can be represented with a single inverted pendulum in coronal lateral flexion, but requires a more complex representation in sagittal extension. The upper cervical spine has substantial compliance in sagittal extension, and may be responsible for the complex motion and greater extension angular velocities. In analyzing the HICOR, we can gain intuition regarding the neck’s role in dictating head motion during external loading.

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.

A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using six degree-of-freedom angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of the angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92 ± 0.03 and 0.95 ± 0.07 for biphasic shapes, respectively, vs. 0.80 ± 0.06 and 0.80 ± 0.08 for acceleration-only shapes). Peak maximum principal strain (ɛ p) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline ɛ p and CSDM from the directly simulated responses, regardless of the brain region, and by a large margin (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for ɛ p in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate model-based brain injury studies in the future.

Background and purpose The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM). Methods Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC). Results BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB. Conclusions Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

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.

Sport-related concussion is a major public health concern. In a recent BJSM publication, McGuine et al conducted a randomised controlled trial (RCT) to evaluate whether soccer headgear would reduce the rate or severity of concussions in adolescent athletes (nheadgear = 1505, nno_headgear = 1545).1 Both ‘control’ athletes (who wore no helmets) and the athletes who wore soccer headgear had similar concussion rates and recovery times after a concussion. In another study, headgear was also found to be ineffective in reducing concussion rates or recovery times in rugby union.2 Padding may seem like the most intuitive way to protect the head from injury. So why might this approach not be concussion-proof? Adding a foam pad can help ‘soften the blow’, by increasing the loading area and absorbing some of the impact energy. However, given a regular thickness pad, substantial impact energy is still transmitted to the head and produces a sharp head/skull acceleration. The skull acceleration in turn shakes and deforms its contents—the brain. Traditionally designed helmets and padding reduce focal loading and head linear accelerations that are associated with skull fracture risk.3 However, head rotation has been hypothesised to be a …

Traumatic brain injury (TBI) is a complex injury that is hard to predict and diagnose, with many studies focused on associating head kinematics to brain injury risk. Recently, there has been a push towards using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we developed a 3 degree-of-freedom lumped-parameter brain model, built based on the measured natural frequencies of a FE brain model simulated with live human impact data, to be used to rapidly estimate peak brain strains experienced during head rotational accelerations. On our dataset, the simplified model correlates with peak principal FE strain by an R2 of 0.80. Further, coronal and axial model displacement correlated with fiber-oriented peak strain in the corpus callosum with an R2 of 0.77. Using the maximum displacement predicted by our brain model, we propose an injury criteria and compare it against a number of existing rotational and translational kinematic injury metrics on a dataset of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that our proposed metric performed comparably to peak angular acceleration, linear acceleration, and angular velocity in classifying injury and non-injury events. Metrics which separated time traces into their directional components had improved deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work as a computationally efficient alternative to FE models for classifying injuries over a wide range of loading conditions., Comment: 10 figures, 3 tables

With 300,000,000 riders annually, roller coasters are a popular recreational activity. Although the number of roller coaster injuries is relatively low, the precise effect of roller coaster rides on our brains remains unknown. Here we present the quantitative characterization of brain displacements and deformations during roller coaster rides. For two healthy adult male subjects, we recorded head accelerations during three representative rides, and, for comparison, during running and soccer headers. From the recordings, we simulated brain displacements and deformations using rigid body dynamics and finite element analyses. Our findings show that despite having lower linear accelerations than sports head impacts, roller coasters may lead to brain displacements and strains comparable to mild soccer headers. The peak change in angular velocity on the rides was 9.9 rad/sec, which was higher than the 5.6 rad/sec in soccer headers with ball velocities reaching 7 m/sec. Maximum brain surface displacements of 4.0 mm and maximum principal strains of 7.6% were higher than in running and similar to soccer headers, but below the reported average concussion strain. Brain strain rates during roller coaster rides were similar to those in running, and lower than those in soccer headers. Strikingly, on the same ride and at a similar position, the two subjects experienced significantly different head kinematics and brain deformation. These results indicate that head motion and brain deformation during roller coaster rides are highly sensitive to individual subjects. Although our study suggests that roller coaster rides do not present an immediate risk of acute brain injury, their long-term effects require further longitudinal study.

Real-time quantification of head impacts using wearable sensors is an appealing approach to assess concussion risk. Traditionally, sensors were evaluated for accurately measuring peak resultant skull accelerations and velocities. With growing interest in utilizing model-estimated tissue responses for injury prediction, it is important to evaluate sensor accuracy in estimating tissue response as well. Here, we quantify how sensor kinematic measurement errors can propagate into tissue response errors. Using previous instrumented mouthguard validation datasets, we found that skull kinematic measurement errors in both magnitude and direction lead to errors in tissue response magnitude and distribution. For molar design instrumented mouthguards susceptible to mandible disturbances, 150-400% error in skull kinematic measurements resulted in 100% error in regional peak tissue response. With an improved incisor design mitigating mandible disturbances, errors in skull kinematics were reduced to

Wearable inertial sensors measure human head impact kinematics important to the on-going development and validation of head injury criteria. However, sensor specifications have not been scientifically justified in the context of the anticipated field impact dynamics. The objective of our study is to determine the minimum bandwidth and sample rate required to capture the impact frequency response relevant to injury. We used high-bandwidth head impact data as ground-truth measurements, and investigated the attenuation of various injury criteria at lower bandwidths. Given a 10% attenuation threshold, we determined the minimum bandwidths required to study injury criteria based on skull kinematics and brain deformation in three different model systems: helmeted cadaver (no neck), unhelmeted cadaver (no neck), and helmeted dummy impacts (with neck). We found that higher bandwidths are required for unhelmeted impacts in general and for studying strain rate injury criteria. Minimum gyroscope bandwidths of 300Hz in helmeted sports and 500Hz in unhelmeted sports are necessary to study strain rate based injury criteria. A minimum accelerometer bandwidth of 500Hz in unhelmeted sports is necessary to study most injury criteria. Current devices typically sample at 1000Hz, with gyroscope bandwidths below 200Hz, which are not always sufficient according to these requirements. With hard contact test conditions, the identified requirements may be higher than most soft contacts on the field, but should be satisfied to capture the worst contact, and often higher risk, scenarios relative to the specific sport or activity. Our findings will help establish standard guidelines for sensor choice and design in traumatic brain injury research.Copyright © 2016 Elsevier Ltd. All rights reserved. Language: en

Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29-0.42) than practices (0.20, 95% CI: 0.17-0.23) (p

Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain's function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull–brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull–brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain–skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.

Inertial sensors are commonly used to measure human head motion.(R1–3) Some sensors have been tested with dummy or cadaver experiments with mixed results, and methods to evaluate sensors in vivo are lacking. Here we present an in vivo(R3–10) method using high speed video to test teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6–13g(R1–20) sagittal soccer head impacts. Sensor coupling to the skull (R1–3) was quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (

This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

Injury from blunt head impacts causes acute neurological deficits and may lead to chronic neurodegeneration. A head impact detection device can serve both as a research tool for studying head injury mechanisms and a clinical tool for real-time trauma screening. The simplest approach is an acceleration thresholding algorithm, which may falsely detect high-acceleration spurious events such as manual manipulation of the device. We designed a head impact detection system that distinguishes head impacts from nonimpacts through two subsystems. First, we use infrared proximity sensing to determine if the mouthguard is worn on the teeth to filter out all off-teeth events. Second, on-teeth, nonimpact events are rejected using a support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity. The remaining events are classified as head impacts. In a controlled laboratory evaluation, the present system performed substantially better than a 10-g acceleration threshold in head impact detection (98% sensitivity, 99.99% specificity, 99% accuracy, and 99.98% precision, compared to 92% sensitivity, 58% specificity, 65% accuracy, and 37% precision). Once adapted for field deployment by training and validation with field data, this system has the potential to effectively detect head trauma in sports, military service, and other high-risk activities.

Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury (vol 43, pg 1918, 2015)

Roughly 5% of all collegiate and high school American football players suffer a concussion each season [1]. Concussions and repetitive sub-concussive trauma can have measurable effects on brain function and neurophysiological changes [2]. Several studies have suggested that a combination of linear and angular kinematic measures may be predictive of concussion [3, 4]. Presently, laboratory testing and analysis of purely linear kinematics is used to design and assess the safety of protective headgear. However, it is not known how well existing laboratory tests recapitulate angular kinematics. In this study, we analyze combinations of linear and angular head kinematics experienced by players on the field. This study sought to answer the question: how well do the twin-wire drop test apparatus and a spring-driven linear impactor reproduce the combination of linear and angular head impact kinematics experienced in vivo by players of American football?Copyright © 2013 by ASME