Validation of a helmet-based system to measure head impact biomechanics in ice hockey

Head Impacts in the Top 1% by Peak Linear Acceleration and/or Work Cause Immediate Concussion Signs and 'Check Engine' Responses in Military Service Members and Civilian Athletes

PURPOSE

Historically, head impact monitoring sensors have suffered from single impact measurement errors, leading to their data described by clinical experts as 'clinically irrelevant.' The purpose of this study was to use an accurate impact monitoring mouthguard system and (1) define head impact distributions for military service members and civilians and (2) determine if there was a dose-response relationship between accurately measured head impact magnitudes versus observations of concussion signs.

METHODS

A laboratory-calibrated commercial impact monitoring mouthguard system, along with video and hardware to confirm the sensor was on the teeth during impacts, was used to acquire 54,602 head acceleration events (HAE) in 973 military and civilian subjects over 3,449 subject days.

RESULTS

There were 17,551 head impacts (32% of HAE) measured with peak linear acceleration (PLA) > 10 g and 37,051 low-g events (68% of HAE) in the range of activities of daily living < 10 g PLA. The median of all HAE and of all head impacts was 8 g/15 g PLA and 1 J/4 J Work, respectively. The top 1% of head impacts were above 47 g and 32 J, respectively. There were fifty-six (56) head impacts where at least one clinical indicator of a concussion sign was observed. All the clinical indicator impacts were in the top 1% by magnitude of PLA, Work, or both. The median magnitude of these 'check engine' impacts was 58 g and 48 J. This median magnitude was substantially larger than the median of all HAE as well as the median of all head impacts.

CONCLUSION

This study shows a correlation between single head impacts in the top 1% by peak linear acceleration and/or Work and clinical indicators of concussion signs in civilians and military service members.

On-Field Evaluation of Mouthpiece-and-Helmet-Mounted Sensor Data from Head Kinematics in Football

PURPOSE

Wearable sensors are used to measure head impact exposure in sports. The Head Impact Telemetry (HIT) System is a helmet-mounted system that has been commonly utilized to measure head impacts in American football. Advancements in sensor technology have fueled the development of alternative sensor methods such as instrumented mouthguards. The objective of this study was to compare peak magnitude measured from high school football athletes dually instrumented with the HIT System and a mouthpiece-based sensor system.

METHODS

Data was collected at all contact practices and competitions over a single season of spring football. Recorded events were observed and identified on video and paired using event timestamps. Paired events were further stratified by removing mouthpiece events with peak resultant linear acceleration below 10 g and events with contact to the facemask or body of athletes.

RESULTS

A total of 133 paired events were analyzed in the results. There was a median difference (mouthpiece subtracted from HIT System) in peak resultant linear and rotational acceleration for concurrently measured events of 7.3 g and 189 rad/s2. Greater magnitude events resulted in larger kinematic differences between sensors and a Bland Altman analysis found a mean bias of 8.8 g and 104 rad/s2, respectively.

CONCLUSION

If the mouthpiece-based sensor is considered close to truth, the results of this study are consistent with previous HIT System validation studies indicating low error on average but high scatter across individual events. Future researchers should be mindful of sensor limitations when comparing results collected using varying sensor technologies.

Biofidelity and Limitations of Instrumented Mouthguard Systems for Assessment of Rigid Body Head Kinematics

Instrumented mouthguard systems (iMGs) are commonly used to study rigid body head kinematics across a variety of athletic environments. Previous work has found good fidelity for iMGs rigidly fixed to anthropomorphic test device (ATD) headforms when compared to reference systems, but few validation studies have focused on iMG performance in human cadaver heads. Here, we examine the performance of two boil-and-bite style iMGs in helmeted cadaver heads. Three unembalmed human cadaver heads were fitted with two instrumented boil-and-bite mouthguards [Prevent Biometrics and Diversified Technical Systems (DTS)] per manufacturer instructions. Reference sensors were rigidly fixed to each specimen. Specimens were fitted with a Riddell SpeedFlex American football helmet and impacted with a rigid impactor at three velocities and locations. All impact kinematics were compared at the head center of gravity. The Prevent iMG performed comparably to the reference system up to ~ 60 g in linear acceleration, but overall had poor correlation (CCC = 0.39). Prevent iMG angular velocity and BrIC generally well correlated with the reference, while underestimating HIC and overestimating HIC duration. The DTS iMG consistently overestimated the reference across all measures, with linear acceleration error ranging from 10 to 66%, and angular acceleration errors greater than 300%. Neither iMG demonstrated consistent agreement with the reference system. While iMG validation efforts have utilized ATD testing, this study highlights the need for cadaver testing and validation of devices intended for use in-vivo, particularly when considering realistic (non-idealized) sensor-skull coupling, when accounting for interactions with the mandible and when subject-specific anatomy may affect device performance.

Correlation between Malocclusion and Mandibular Fractures: An Experimental Study Comparing Dynamic Finite Element Models and Clinical Case Studies

Mandibular fractures are very common in maxillofacial trauma surgery. While previous studies have focused on possible risk factors related to post-operative complications, none have tried to identify pre-existing conditions that may increase the risk of mandibular fractures. We hypothesized, through clinical observation, that anatomical conditions involving poor dental contacts, such as malocclusions, may increase the risk of mandibular fractures. This work was subdivided into two parts. In the first part, Digital Imaging and Communications in Medicine (DICOM) data of four healthy patients characterized by different dentoskeletal occlusions (class I, class II, class III, and anterior open bite) have been used to develop four finite element models (FEMs) that accurately reproduce human bone structure. A vertical and lateral impact have been simulated at increasing speed on each model, analyzing the force distribution within the mandibular bone. Both vertical and lateral impact showed higher level of stress at the impact point and in the condylar area in models characterized by malocclusion. Specifically, the class III and the open bite models, at the same speed of impact, had higher values for a longer period, reaching critical stress levels that are correlated with mandibular fracture, while normal occlusion seems to be a protective condition. In the second part of this study, the engineering results were validated through the comparison with a sample of patients previously treated for mandibular fracture. Data from 223 mandibular fractures, due to low-energy injuries, were retrospectively collected to evaluate a possible correlation between pre-existing malocclusion and fracture patterns, considering grade of displacement, numbers of foci, and associated CFI score. Patients were classified, according to their occlusion, into Class I, Class II, Class III, and anterior open bite or poor occlusal contact (POC). Class I patients showed lower frequencies of fracture than class II, III, and open bite or POC patients. Class I was associated with displaced fractures in 16.1% of cases, class II in 47.1%, class III in 48.8% and open bite/POC in 65.2% of cases (p-value < 0.0001). In class I patients we observed a single non-displaced fracture in 51.6% of cases, compared to 12.9% of Class II, 19.5% of Class III and 22.7% of the open bite/POC group. Our analysis shows that class I appears to better dissipate forces applied on the mandible in low-energy injuries. A higher number of dental contacts showed a lower rate of multifocal and displaced fractures, mitigating the effect of direct forces onto the bone. The correlation between clinical data and virtual simulation on FEM models seems to point out that virtual simulation successfully predicts fracture patterns and risk of association with different type of occlusion. Better knowledge of biomechanics and force dissipation on the human body may lead to the development of more effective safety devices, and help select patients to plan medical, orthodontic/dental, and/or surgical intervention to prevent injuries.

Assessment of brain response in operators subject to recoil force from firing long-range rifles

Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. While it is important to protect against injurious head impacts, the repeated exposure of Canadian Armed Forces (CAF) service members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. This study investigates how the exposure to the recoil force of long-range rifles results in head motion and brain deformation. Direct measurements of head kinematics of a controlled population of military personnel during firing events were obtained using instrumented mouthguards. The experimentally measured head kinematics were then used as inputs to a finite element (FE) head model to quantify the brain strains observed during each firing event. The efficacy of a concept recoil mitigation system (RMS), designed to mitigate loads applied to the operators was quantified, and the RMS resulted in lower loading to the operators. The outcomes of this study provide valuable insights into the magnitudes of head kinematics observed when firing long-range rifles, and a methodology to quantify effects, which in turn will help craft exposure guidelines, guide training to mitigate the risk of injury, and improve the quality of lives of current and future CAF service members and veterans.

Associations between the circumstances and severity of head impacts in men's university ice hockey

Improved evidence on the most common and severe types of head impacts in ice hockey can guide efforts to preserve brain health through improvements in protective gear, rink design, player training, and rules of play. In this observational cohort study of men's university hockey, we compared video evidence on the circumstances of 234 head impacts to measures of head impact severity (peak linear accelerations and rotational velocities) from helmet-mounted sensors (GForceTracker). Videos were analyzed with a validated questionnaire, and paired with helmet sensor data. Shoulder-to-head impacts were more common than hand- or elbow-, but there were no differences in head impact severity between upper limb contact sites (p ≥ 0.2). Head-to-glass impacts were nearly four times more common, and just as severe as head-to-board impacts (p ≥ 0.4). Head impacts resulting in major penalties (versus no penalty), or visible signs of concussion (versus no signs), involved greater head rotational velocities (p = 0.038 and 0.049, respectively). Head impacts occurred most often to the side of the head, along the boards to players in their offensive zone without puck possession. Head impact severity did not differ between cases where the head was (versus was not) the primary site of contact (p ≥ 0.6). Furthermore, penalties were called in only 4% of cases where the head was the initial point of contact. Accordingly, rules that focus on primary targeting of the head, while important and in need of improved enforcement, offer a limited solution.

Development of a Head Acceleration Event Classification Algorithm for Female Rugby Union

Instrumented mouthguards have been used to detect head accelerations and record kinematic data in numerous sports. Each recording requires validation through time-consuming video verification. Classification algorithms have been posed to automatically categorise head acceleration events and spurious events. However, classification algorithms must be designed and/or validated for each combination of sport, sex and mouthguard system. This study provides the first algorithm to classify head acceleration data from exclusively female rugby union players. Mouthguards instrumented with kinematic sensors were given to 25 participants for six competitive rugby union matches in an inter-university league. Across all instrumented players, 214 impacts were recorded from 460 match-minutes. Matches were video recorded to enable retrospective labelling of genuine and spurious events. Four machine learning algorithms were trained on five matches to predict these labels, then tested on the sixth match. Of the four classifiers, the support vector machine achieved the best results, with area under the receiver operator curve (AUROC) and area under the precision recall curve (AUPRC) scores of 0.92 and 0.85 respectively, on the test data. These findings represent an important development for head impact telemetry in female sport, contributing to the safer participation and improving the reliability of head impact data collection within female contact sport.

A Review of Head Injury Metrics Used in Automotive Safety and Sports Protective Equipment

Despite advances in the understanding of human tolerances to brain injury, injury metrics used in automotive safety and protective equipment standards have changed little since they were first implemented nearly a half-century ago. Although numerous metrics have been proposed as improvements over the ones currently used, evaluating the predictive capability of these metrics is challenging. The purpose of this review is to summarize existing head injury metrics that have been proposed for both severe head injuries, such as skull fractures and traumatic brain injuries (TBI), and mild traumatic brain injuries (mTBI) including concussions. Metrics have been developed based on head kinematics or intracranial parameters such as brain tissue stress and strain. Kinematic metrics are either based on translational motion, rotational motion, or a combination of the two. Tissue-based metrics are based on finite element model simulations or in vitro experiments. This review concludes with a discussion of the limitations of current metrics and how improvements can be made in the future.

A neural network for the detection of soccer headers from wearable sensor data

To investigate the proposed association between soccer heading and deleterious brain changes, an accurate quantification of heading exposure is crucial. While wearable sensors constitute a popular means for this task, available systems typically overestimate the number of headers by poorly discriminating true impacts from spurious recordings. This study investigated the utility of a neural network for automatically detecting soccer headers from kinematic time series data obtained by wearable sensors. During 26 matches, 27 female soccer players wore head impacts sensors to register on-field impact events (> 8 g), which were labelled as valid headers (VH) or non-headers (NH) upon video review. Of these ground truth data, subsets of 49% and 21% each were used to train and validate a Long Short-Term Memory (LSTM) neural network in order to classify sensor recordings as either VH or NH based on their characteristic linear acceleration features. When tested on a balanced dataset comprising 271 VHs and NHs (which corresponds to 30% and 1.4% of ground truth VHs and NHs, respectively), the network showed very good overall classification performance by reaching scores of more than 90% across all metrics. When testing was performed on an unbalanced dataset comprising 271 VHs and 5743 NHs (i.e., 30% of ground truth VHs and NHs, respectively), as typically obtained in real-life settings, the model still achieved over 90% sensitivity and specificity, but only 42% precision, which would result in an overestimation of soccer players' true heading exposure. Although classification performance suffered from the considerable class imbalance between actual headers and non-headers, this study demonstrates the general ability of a data-driven deep learning network to automatically classify soccer headers based on their linear acceleration profiles.

Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Automated soccer head impact exposure tracking using video and deep learning

Head impacts are highly prevalent in sports and there is a pressing need to investigate the potential link between head impact exposure and brain injury risk. Wearable impact sensors and manual video analysis have been utilized to collect impact exposure data. However, wearable sensors suffer from high deployment cost and limited accuracy, while manual video analysis is a long and resource-intensive task. Here we develop and apply DeepImpact, a computer vision algorithm to automatically detect soccer headers using soccer game videos. Our data-driven pipeline uses two deep learning networks including an object detection algorithm and temporal shift module to extract visual and temporal features of video segments and classify the segments as header or nonheader events. The networks were trained and validated using a large-scale professional-level soccer video dataset, with labeled ground truth header events. The algorithm achieved 95.3% sensitivity and 96.0% precision in cross-validation, and 92.9% sensitivity and 21.1% precision in an independent test that included videos of five professional soccer games. Video segments identified as headers in the test data set correspond to 3.5 min of total film time, which can be reviewed through additional manual video verification to eliminate false positives. DeepImpact streamlines the process of manual video analysis and can help to collect large-scale soccer head impact exposure datasets for brain injury research. The fully video-based solution is a low-cost alternative for head impact exposure monitoring and may also be expanded to other sports in future work.

Characterizing Head Impact Exposure in Men and Women During Boxing and Mixed Martial Arts

Background:

The accumulation of subconcussive impacts has been implicated in permanent neurological impairment. A gap in understanding the relationship between head impacts and neurological function is the lack of precise characterization and quantification of forces that individuals experience during sports training and competition.

Purpose:

To characterize impact exposure during training and competition among male and female athletes participating in boxing and mixed martial arts (MMA) via an instrumented custom-fit Impact Monitoring Mouthguard (IMM).

Study Design:

Cross-sectional study; Level of evidence, 3.

Methods:

Twenty-three athletes (n = 4 women) were provided a custom-fit IMM. The IMM monitored impacts during sparring and competition. All training and competition sessions were videotaped. Video and IMM data were synchronized for post hoc data verification of true positives and substantiation of impact location. IMM data were collected from boxing and MMA athletes at a collaborating site. For each true-positive impact, peak linear acceleration and peak angular acceleration were calculated. Wilcoxon rank sum tests were used to evaluate potential differences in sport, activity type, and sex with respect to each outcome. Differences in impact location were assessed via Kruskal-Wallis tests.

Results:

IMM data were collected from 53 amateur training sessions and 6 competitions (session range, 5-20 minutes). A total of 896 head impacts (men, n = 786; women, n = 110) were identified using IMM data and video verification: 827 in practice and 69 during competition. MMA and boxers experienced a comparable number of impacts per practice session or competition. In general, MMA impacts produced significantly higher peak angular acceleration than did boxing impacts (P < .001) and were more varied in impact location on the head during competitions. In terms of sex, men experienced a greater number of impacts than women per practice session. However, there was no significant difference between men and women in terms of impact magnitude.

Conclusion:

Characteristic profiles of head impact exposure differed between boxing and MMA athletes; however, the impact magnitudes were not significantly different for male and female athletes.

Head Impact Sensor Triggering Bias Introduced by Linear Acceleration Thresholding

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.

Head Impact Research Using Inertial Sensors in Sport: A Systematic Review of Methods, Demographics, and Factors Contributing to Exposure

BACKGROUND

The number and magnitude of head impacts have been assessed in-vivo using inertial sensors to characterise the exposure in various sports and to help understand their potential relationship to concussion.

OBJECTIVES

We aimed to provide a comprehensive review of the field of in-vivo sensor acceleration event research in sports via the summary of data collection and processing methods, population demographics and factors contributing to an athlete's exposure to sensor acceleration events.

METHODS

The systematic search resulted in 185 cohort or cross-sectional studies that recorded sensor acceleration events in-vivo during sport participation.

RESULTS

Approximately 5800 participants were studied in 20 sports using 18 devices that included instrumented helmets, headbands, skin patches, mouthguards and earplugs. Female and youth participants were under-represented and ambiguous results were reported for these populations. The number and magnitude of sensor acceleration events were affected by a variety of contributing factors, suggesting sport-specific analyses are needed. For collision sports, being male, being older, and playing in a game (as opposed to a practice), all contributed to being exposed to more sensor acceleration events.

DISCUSSION

Several issues were identified across the various sensor technologies, and efforts should focus on harmonising research methods and improving the accuracy of kinematic measurements and impact classification. While the research is more mature for high-school and collegiate male American football players, it is still in its early stages in many other sports and for female and youth populations. The information reported in the summarised work has improved our understanding of the exposure to sport-related head impacts and has enabled the development of prevention strategies, such as rule changes.

CONCLUSIONS

Head impact research can help improve our understanding of the acute and chronic effects of head impacts on neurological impairments and brain injury. The field is still growing in many sports, but technological improvements and standardisation of processes are needed.

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

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

Potential Mechanisms of Acute Standing Balance Deficits After Concussions and Subconcussive Head Impacts: A Review

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.

Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review

Background: Wearable inertial sensor technology (WIST) systems provide feedback, aiming to modify aberrant postures and movements. The literature on the effects of feedback from WIST during work or work-related activities has not been previously summarised. This review examines the effectiveness of feedback on upper body kinematics during work or work-related activities, along with the wearability and a quantification of the kinematics of the related device. Methods: The Cinahl, Cochrane, Embase, Medline, Scopus, Sportdiscus and Google Scholar databases were searched, including reports from January 2005 to July 2021. The included studies were summarised descriptively and the evidence was assessed. Results: Fourteen included studies demonstrated a 'limited' level of evidence supporting posture and/or movement behaviour improvements using WIST feedback, with no improvements in pain. One study assessed wearability and another two investigated comfort. Studies used tri-axial accelerometers or IMU integration (n = 5 studies). Visual and/or vibrotactile feedback was mostly used. Most studies had a risk of bias, lacked detail for methodological reproducibility and displayed inconsistent reporting of sensor technology, with validation provided only in one study. Thus, we have proposed a minimum 'Technology and Design Checklist' for reporting. Conclusions: Our findings suggest that WIST may improve posture, though not pain; however, the quality of the studies limits the strength of this conclusion. Wearability evaluations are needed for the translation of WIST outcomes. Minimum reporting standards for WIST should be followed to ensure methodological reproducibility.

Head Impact Telemetry System's Video-based Impact Detection and Location Accuracy

PURPOSE

This study aimed to quantify the Head Impact Telemetry (HIT) System's impact detection and location measurement accuracy using an impact biomechanics data set paired with video of high school football special teams plays.

METHODS

The head impact biomechanics data set and video were collected from 22 high school football players, wearing HIT System instrumented helmets, competing in 218 special teams plays over a single high school football season. We used two separate video analysis approaches. To quantify the impact detection accuracy, we evaluated the video for head impacts independently of the impact data collection triggers collected by the HIT System. Video-observed impacts matched to valid and invalid head impacts by the HIT System algorithm were categorized as true positives, false positives, false negatives, and true negatives. To quantify impact location accuracy, we analyzed video-synchronized head impacts for impact location independent of the HIT System's impact location measurement and quantified the estimated percent agreement of impact location between the HIT System recorded impact location and the impact location observed on video.

RESULTS

The HIT System's impact-filtering algorithm had 69% sensitivity, 72% specificity, and 70% accuracy in categorizing true and non-head impact data collection triggers. The HIT System agreed with video-observed impact locations on 64% of the 129 impacts we analyzed (unweighted k = 0.43, 95% confidence interval = 0.31-0.54).

CONCLUSION

This work provides data on the HIT System's impact detection and location accuracy during high school football special teams plays using game video analysis that has not been previously published. Based on our data, we believe that the HIT System is useful for estimating population-based impact location distributions for special teams plays.

Biomechanics of Sport-Related Neurological Injury

As awareness on the short-term and long-term consequences of sports-related concussions and repetitive head impacts continues to grow, so too does the necessity to establish biomechanical measures of risk that inform public policy and risk mitigation strategies. A more precise exposure metric is central to establishing relationships among the traumatic experience, risk, and ultimately clinical outcomes. Accurate exposure metrics provide a means to support evidence-informed decisions accelerating public policy mandating brain trauma management through sport modification and safer play.

Machine Learning Classification of Verified Head Impact Exposure Strengthens Associations with Brain Changes

Cumulative exposure to head impacts during contact sports can elicit potentially deleterious brain white matter alterations in young athletes. Head impact exposure is commonly quantified using wearable sensors; however, these sensors tend to overestimate the number of true head impacts that occur and may obfuscate potential relationships with longitudinal brain changes. The purpose of this study was to examine whether data-driven filtering of head impact exposure using machine learning classification could produce more accurate quantification of exposure and whether this would reveal more pronounced relationships with longitudinal brain changes. Season-long head impact exposure was recorded for 22 female high school soccer athletes and filtered using three methods-threshold-based, heuristic filtering, and machine learning (ML) classification. The accuracy of each method was determined using simultaneous video recording of a subset of the sensor-recorded impacts, which was used to confirm which sensor-recorded impacts corresponded with true head impacts and the ability of each method to detect the true impacts. Each filtered dataset was then associated with the athletes' pre- and post-season MRI brain scans to reveal longitudinal white matter changes. The threshold-based, heuristic, and ML approaches achieved 22.0% accuracy, 44.6%, and 83.5% accuracy, respectively. ML classification also revealed significant longitudinal brain white matter changes, with negative relationships observed between head impact exposure and reductions in mean and axial diffusivity and a positive relationship observed between exposure and fractional anisotropy (all p < 0.05).

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

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

Describing headform pose and impact location for blunt impact testing

Reproduction of anthropomorphic test device (ATD) head impact test methods is a critical element needed to develop guidance and technologies that reduce the risk for brain injury in sport. However, there does not appear to be a consensus for reporting ATD pose and impact location for industry and researchers to follow. Thus, the purpose of this article is to explore the various methods used to report impact location and ATD head pose for sport-related head impact testing and provide recommendations for standardizing these descriptions. A database search and exclusion process identified 137 articles that met the review criteria. Only 4 of the 137 articles provided a description similar to the method we propose to describe ATD pose and impact location. We thus propose a method to unambiguously convey the impact location and pose of the ATD based on the sequence, quantifiable design, and articulation of ATD mount joints. This reporting method has been used to a limited extent in the literature, but we assert that adoption of this method will help to standardize the reporting of ATD headform pose and impact location as well as aid in the replication of impact test protocols across laboratories.

Comparison of head impact measurements via an instrumented mouthguard and an anthropometric testing device

The purpose of this study was to determine and compare the efficacy of head impact measurements via an electronic sensor framework, embedded within a mouthguard, against an anthropometric testing device. Development of the former is in response to the growing issue of head impacts and concussion in rugby union. Testing was conducted in a vehicle safety laboratory using a standard impact protocol utilising the headforms of anthropometric testing devices. The headforms were subjected to controlled front and side impacts. For each impact, the linear acceleration and rotational velocity was measured over a 104-ms interval at a frequency of 1 kHz. The magnitude of peak linear acceleration and peak rotational velocity was determined from the measured time-series traces and statistically compared. The peak linear acceleration and rotational velocity had intraclass correlation coefficients of 0.95 and 0.99, respectively. The root-mean-square error between the measurement systems was 4.3 g with a standard deviation of 3.5 g for peak linear acceleration and 0.7 rad/s with a standard deviation of 0.4 rad/s for rotational velocity. Bland and Altman analysis indicated a systematic bias of 2.5 g and − 0.5 rad/s and limits of agreement (1.96 × standard deviation) of ± 13.1 g and ± 1.25 rad/s for the instrumented mouthguard. These results provide the basis on which the instrumented mouthguard can be further developed for deployment and application within professional rugby, with a view to accurately and reliably quantify head collision dynamics.

Development, Validation and Pilot Field Deployment of a Custom Mouthpiece for Head Impact Measurement

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.

Feasibility of using a novel instrumented human head surrogate to measure helmet, head and brain kinematics and intracranial pressure during multidirectional impact tests

OBJECTIVES

Aim of the work is to present the feasibility of using an Instrumented Human Head Surrogate (IHHS-1) during multidirectional impacts while wearing a modern ski helmet. The IHHS-1 is intended to provide reliable and repeatable data for the experimental validation of FE models and for the experimental evaluation of modern helmets designed to enhance the degree of protection against multidirectional impacts.

DESIGN

The new IHHS-1 includes 9 triaxial MEMS accelerometers embedded in a silicone rubber brain, independently molded and presenting lobes separation and cerebellum, placed into an ABS skull filled with surrogate cerebrospinal fluid. A triaxial MEMS gyroscope is placed at the brain center of mass. Intracranial pressure can be detected by eight pressure sensors applied to the skull internal surface along a transversal plane located at the brain center of mass and two at the apex. Additional MEMS sensors positioned over the skull and the helmet allow comparison between outer and inner structure kinematics and surrogate CSF pressure behavior.

METHODS

The IHHS-1 was mounted through a Hybrid III neck on a force platform and impacted with a striker connected to a pendulum tower, with the impact energies reaching 24J. Impact locations were aligned with the brain center of mass and located in the back (sagittal axis), right (90° from sagittal axis), back/right (45°), and front right (135°) locations. Following dynamic data were collected: values of the linear accelerations and angular velocities of the brain, skull and helmet; intracranial pressures inside the skull.

RESULTS

Despite the relatively low intensity of impacts (HIC at skull max value 46), the skull rotational actions reached BrIC values of 0.33 and angular accelerations of 5216rad/s², whereas brain angular acceleration resulted between 1,44 and 2,1 times lower with similar values of BrIC.

CONCLUSIONS

The IHHS-1 is a physical head surrogate that can produce repeatable data for the interpretation of inner structures behavior during multidirectional impacts with or without helmets of different characteristics.

Verifying Head Impacts Recorded by a Wearable Sensor using Video Footage in Rugby League: a Preliminary Study

BACKGROUND

Rugby league is a full-contact collision sport with an inherent risk of concussion. Wearable instrumented technology was used to observe and characterize the level of exposure to head impacts during game play.

PURPOSE

To verify the impacts recorded by the x-patch™ with video analysis.

STUDY DESIGN

Observational case series.

METHODS

The x-patch™ was used on eight men's semi-professional rugby league players during the 2016 Newcastle Rugby League competition (five forwards and three backs). Game day footage was recorded by a trained videographer using a single camera located at the highest midfield location to verify the impact recorded by the x-patch™. Videographic and accelerometer data were time synchronized.

RESULTS

The x-patch™ sensors recorded a total of 779 impacts ≥ 20 g during the games, of which 732 (94.0%) were verified on video. In addition, 817 impacts were identified on video that did not record an impact on the sensors. The number of video-verified impacts ≥ 20 g, per playing hour, was 7.8 for forwards and 4.8 for backs (range = 3.9-19.0). Impacts resulting in a diagnosed concussion had much greater peak linear acceleration (M = 76.1 g, SD = 17.0) than impacts that did not result in a concussion (M = 34.2g, SD = 18.0; Cohen's d = 2.4).

CONCLUSIONS

The vast majority (94%) of impacts ≥ 20 g captured by the x-patch™ sensor were video verified in semi-professional rugby league games. The use of a secondary source of information to verify impact events recorded by wearable sensors is beneficial in clarifying game events and exposure levels.

A review of impact testing methods for headgear in sports: Considerations for improved prevention of head injury through research and standards

Standards for sports headgear were introduced as far back as the 1960s and many have remained substantially unchanged to present day. Since this time, headgear has virtually eliminated catastrophic head injuries such as skull fractures and changed the landscape of head injuries in sports. Mild traumatic brain injury (mTBI) is now a prevalent concern and the effectiveness of headgear in mitigating mTBI is inconclusive for most sports. Given that most current headgear standards are confined to attenuating linear head mechanics and recent brain injury studies have underscored the importance of angular mechanics in the genesis of mTBI, new or expanded standards are needed to foster headgear development and assess headgear performance that addresses all types of sport-related head and brain injuries. The aim of this review is to provide a basis for developing new sports headgear impact tests for standards by summarizing and critiquing: 1) impact testing procedures currently codified in published headgear standards for sports and 2) new or proposed headgear impact test procedures in published literature and/or relevant conferences. Research areas identified as needing further knowledge to support standards test development include defining sports-specific head impact conditions, establishing injury and age appropriate headgear assessment criteria, and the development of headgear specific head and neck surrogates for at-risk populations.

Relative Motion between the Helmet and Head in Football Impact Test

Approximately 1.6-3.8 million sports-related traumatic brain injuries occur each year in the US. Researchers track the head motion using a variety of techniques to study the head injury biomechanics. To understand how helmets provide head protection, quantification of the relative motion between the head and the helmet is necessary. The purpose of this study was to compare helmet and head kinematics and quantify the relative motion of helmet with respect to head during experimental representations of on-field American football impact scenarios. Seven helmet-to-helmet impact configurations were simulated by propelling helmeted crash test dummies into each other. Head and helmet kinematics were measured with instrumentation and an optical motion capture system. The analysis of results showed that, the helmets translated 12 - 41 mm and rotated up to 37 degrees with respect to the head. The peak resultant linear acceleration of the helmet was about 2 - 5 times higher than the head. The peak resultant angular velocity of the helmet ranged from 37% less to 71% more than the head, depending on the impact conditions. The results of this study demonstrate that the kinematics of the head and helmet are noticeably different and that the helmet rotates significantly with respect to the head during impacts. Therefore, capturing the helmet kinematics using a video motion tracking methodology is not sufficient to study the biomechanics of the head. Head motion must be measured independently of the helmet.

Evaluation of a novel bicycle helmet concept in oblique impact testing

BACKGROUND

A novel bicycle helmet concept has been developed to mitigate rotational head acceleration, which is a predominant mechanism of traumatic brain injury (TBI). This WAVECEL concept employs a collapsible cellular structure that is recessed within the helmet to provide a rotational suspension. This cellular concept differs from other bicycle helmet technologies for mitigation of rotational head acceleration, such as the commercially available Multi-Directional Impact Protection System (MIPS) technology which employs a slip liner to permit sliding between the helmet and the head during impact. This study quantified the efficacy of both, the WAVECEL cellular concept, and a MIPS helmet, in direct comparison to a traditional bicycle helmet made of rigid expanded polystyrene (EPS).

METHODS

Three bicycle helmet types were subjected to oblique impacts in guided vertical drop tests onto an angled anvil: traditional EPS helmets (CONTROL group); helmets with a MIPS slip liner (SLIP group); and helmets with a WAVECEL cellular structure (CELL group). Helmet performance was evaluated using 4.8 m/s impacts onto anvils angled at 30°, 45°, and 60° from the horizontal plane. In addition, helmet performance was tested at a faster speed of 6.2 m/s onto the 45° anvil. Five helmets were tested under each of the four impact conditions for each of the three groups, requiring a total of 60 helmets. Headform kinematics were acquired and used to calculate an injury risk criterion for Abbreviated Injury Score (AIS) 2 brain injury.

RESULTS

Linear acceleration of the headform remained below 90 g and was not associated with the risk of skull fracture in any impact scenario and helmet type. Headform rotational acceleration in the CONTROL group was highest for 6.2 m/s impacts onto the 45° anvil (7.2 ± 0.6 krad/s²). In this impact scenario, SLIP helmets and CELL helmets reduced rotational acceleration by 22% (p = 0003) and 73% (p < 0.001), respectively, compared to CONTROL helmets. The CONTROL group had the highest AIS 2 brain injury risk of 59 ± 8% for 6.2 m/s impacts onto the 45° anvil. In this impact scenario, SLIP helmets and CELL helmets reduced the AIS 2 brain injury risk to 34.2% (p = 0.001) and 1.2% (p < 0.001), respectively, compared to CONTROL helmets.

DISCUSSION

Results of this study are limited to a narrow range of impact conditions, but demonstrated the potential that rotational acceleration and the associated brain injury risk can be significantly reduced by the cellular WAVECEL concept or a MIPS slip liner. Results obtained under specific impact angles and impact velocities indicated performance differences between these mechanisms. These differences emphasize the need for continued research and development efforts toward helmet technologies that further improve protection from brain injury over a wide range a realistic impact parameters.

Preliminary evidence from a prospective DTI study suggests a posterior-to-anterior pattern of recovery in college athletes with sports-related concussion

OBJECTIVES

We compared the integrity of white matter (WM) microstructure to the course of recovery in athletes who sustained one sports-related concussion (SRC), assessing individual longitudinal changes in WM fiber tracts following SRC using pre- and post-injury measurements.

MATERIALS AND METHODS

Baseline diffusion tensor imaging (DTI) scans and neuropsychological tests were collected on 53 varsity contact-sport college athletes. Participants (n = 13) who subsequently sustained an SRC underwent DTI scans and neuropsychological testing at 2 days, 2 weeks, and 2 months following injury.

RESULTS

Relying on tract-based spatial statistics (TBSS) analyses, we found that radial diffusivity (RD) and mean diffusivity (MD) were significantly increased at 2 days post-injury compared to the same-subject baseline (corrected p < 0.02). These alterations were visible in anterior/posterior WM regions spanning both hemispheres, demonstrating a diffuse pattern of injury after concussion. Implicated WM fiber tracts at 2 days include the following: right superior/inferior longitudinal fasciculus; right/left inferior fronto-occipital fasciculus; right corticospinal tract; right acoustic radiation; right/left anterior thalamic radiations; right/left uncinate fasciculus; and forceps major/minor. At 2 weeks post-injury, persistently elevated RD and MD were observed solely in prefrontal portions of WM fiber tracts (using same-subject contrasts). No significant differences were found for FA in any of the post-injury comparisons to baseline. Plots of individual subject RD and MD in prefrontal WM demonstrated homogenous increases from baseline to just after SRC; thereafter, trajectories became more variable. Most subjects' diffusivity values remained elevated at 2 months post-injury relative to their own baseline. Over the 2-month period after SRC, recovery of WM fiber tracts appeared to follow a posterior-to-anterior trend, paralleling the posterior-anterior pattern of WM maturation previously identified in the normal population.

CONCLUSION

These results suggest greater vulnerability of prefrontal regions to SRC, underline the importance of an individualized approach to concussion management, and show promise for using RD and MD for imaging-based diagnosis of SRC.

In-Season Variations in Head Impact Exposure among Youth Football Players

Head impact exposure (HIE) is often summarized by the total exposure measured during the season and does not indicate how the exposure was accumulated, or how it varied during the season. Therefore, the objective of this study was to compare HIE during pre-season, the first and second halves of the regular season, and playoffs in a sample of youth football players (n = 119, aged 9-13 years). Athletes were divided into one of four exposure groups based on quartiles computed from the distribution of risk-weighted cumulative exposure (RWE_CP). Mean impacts per session and mean 95th percentile linear and rotational acceleration in practices and games were compared across the four exposure groups and time frames using mixed effects models. Within games, the mean 95th percentile accelerations for the entire sample ranged from 47.2g and 2331.3 rad/sec² during pre-season to 52.1g and 2533.4 rad/sec² during the second half of regular season. Mean impacts per practice increased from pre-season to the second half of regular season and declined into playoffs among all exposure groups; however, the variation between time frames was not greater than two impacts per practice. Time of season had a significant relationship with mean 95th percentile linear and rotational acceleration in games (both, p = 0.01) but not with practice accelerations or impacts per session. The in-practice mean levels of 95th percentile linear and rotational acceleration remained fairly constant across the four time frames, but in games these changed over time depending on exposure group (interactions, p ≤ 0.05). The results of this study improve our understanding of in-season variations in HIE in youth football and may inform important opportunities for future interventions.

The Influence of Heavier Football Helmet Faceguards on Head Impact Location and Severity

OBJECTIVE

To determine whether players with heavier faceguards have increased odds of sustaining top of the head impacts and head impacts of higher severity.

DESIGN

Cohort study.

SETTING

On-field.

PARTICIPANTS

Thirty-five division I collegiate football players.

INTERVENTIONS

Faceguard mass was measured. Head impact location and severity (linear acceleration [gravity], rotational acceleration [radian per square second], and Head Impact Technology severity profile [unitless]) were captured for 19 379 total head impacts at practices using the Head Impact Telemetry System.

MAIN OUTCOME MEASURES

Players' faceguards were categorized as either heavier (>480 g) or lighter (≤480 g) using a median split. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed for sustaining top of the head impacts between faceguard groups using a random intercepts generalized logit model. We compared head impact severity between groups using random intercepts general linear models (α = 0.05). Player position was included in all models.

RESULTS

Overall, the 4 head impact locations were equally distributed across faceguard groups (F(3,26) = 2.16, P = 0.117). Football players with heavier faceguards sustained a higher proportion impacts to the top of the head (24.7% vs 17.5%) and had slightly increased odds of sustaining top (OR, 1.72; 95% CI, 1.01-2.94) head impacts rather than front of the head impacts.

CONCLUSIONS

Football players wearing heavier faceguards might be slightly more prone to sustaining a higher proportion of top of the head impacts, suggesting that greater faceguard mass may make players more likely to lower their head before collision. Individuals involved with equipment selection should consider the potential influence of faceguard design on head impact biomechanics when recommending the use of a heavier faceguard.

Sensing Passive Eye Response to Impact Induced Head Acceleration Using MEMS IMUs

The eye may act as a surrogate for the brain in response to head acceleration during an impact. Passive eye movements in a dynamic system are sensed by microelectromechanical systems (MEMS) inertial measurement units (IMU) in this paper. The technique is validated using a three-dimensional printed scaled human skull model and on human volunteers by performing drop-and-impact experiments with ribbon-style flexible printed circuit board IMUs inserted in the eyes and reference IMUs on the heads. Data are captured by a microcontroller unit and processed using data fusion. Displacements are thus estimated and match the measured parameters. Relative accelerations and displacements of the eye to the head are computed indicating the influence of the concussion causing impacts.

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification

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.

Evaluating the "Threshold Theory": Can Head Impact Indicators Help?

PURPOSE

This study aimed to determine the clinical utility of biomechanical head impact indicators by measuring the sensitivity, specificity, positive predictive value (PV+), and negative predictive value (PV-) of multiple thresholds.

METHODS

Head impact biomechanics (n = 283,348) from 185 football players in one Division I program were collected. A multidisciplinary clinical team independently made concussion diagnoses (n = 24). We dichotomized each impact using diagnosis (yes = 24, no = 283,324) and across a range of plausible impact indicator thresholds (10g increments beginning with a resultant linear head acceleration of 50g and ending with 120g).

RESULTS

Some thresholds had adequate sensitivity, specificity, and PV-. All thresholds had low PV+, with the best recorded PV+ less than 0.4% when accounting for all head impacts sustained by our sample. Even when conservatively adjusting the frequency of diagnosed concussions by a factor of 5 to account for unreported/undiagnosed injuries, the PV+ of head impact indicators at any threshold was no greater than 1.94%.

CONCLUSIONS

Although specificity and PV- appear high, the low PV+ would generate many unnecessary evaluations if these indicators were the sole diagnostic criteria. The clinical diagnostic value of head impact indicators is considerably questioned by these data. Notwithstanding, valid sensor technologies continue to offer objective data that have been used to improve player safety and reduce injury risk.

Performance Evaluation of a Pre-computed Brain Response Atlas in Dummy Head Impacts

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.

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model-based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in superior longitudinal fasciculus (SLF; 0.867-0.879, 84.4-85.2, and 84.1-84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.

Video Analysis Verification of Head Impact Events Measured by Wearable Sensors

BACKGROUND

Wearable sensors are increasingly used to quantify the frequency and magnitude of head impact events in multiple sports. There is a paucity of evidence that verifies head impact events recorded by wearable sensors.

PURPOSE

To utilize video analysis to verify head impact events recorded by wearable sensors and describe the respective frequency and magnitude.

STUDY DESIGN

Cohort study (diagnosis); Level of evidence, 2.

METHODS

Thirty male (mean age, 16.6 ± 1.2 years; mean height, 1.77 ± 0.06 m; mean weight, 73.4 ± 12.2 kg) and 35 female (mean age, 16.2 ± 1.3 years; mean height, 1.66 ± 0.05 m; mean weight, 61.2 ± 6.4 kg) players volunteered to participate in this study during the 2014 and 2015 lacrosse seasons. Participants were instrumented with GForceTracker (GFT; boys) and X-Patch sensors (girls). Simultaneous game video was recorded by a trained videographer using a single camera located at the highest midfield location. One-third of the field was framed and panned to follow the ball during games. Videographic and accelerometer data were time synchronized. Head impact counts were compared with video recordings and were deemed valid if (1) the linear acceleration was ≥20 g, (2) the player was identified on the field, (3) the player was in camera view, and (4) the head impact mechanism could be clearly identified. Descriptive statistics of peak linear acceleration (PLA) and peak rotational velocity (PRV) for all verified head impacts ≥20 g were calculated.

RESULTS

For the boys, a total recorded 1063 impacts (2014: n = 545; 2015: n = 518) were logged by the GFT between game start and end times (mean PLA, 46 ± 31 g; mean PRV, 1093 ± 661 deg/s) during 368 player-games. Of these impacts, 690 were verified via video analysis (65%; mean PLA, 48 ± 34 g; mean PRV, 1242 ± 617 deg/s). The X-Patch sensors, worn by the girls, recorded a total 180 impacts during the course of the games, and 58 (2014: n = 33; 2015: n = 25) were verified via video analysis (32%; mean PLA, 39 ± 21 g; mean PRV, 1664 ± 619 rad/s).

CONCLUSION

The current data indicate that existing wearable sensor technologies may substantially overestimate head impact events. Further, while the wearable sensors always estimated a head impact location, only 48% of the impacts were a result of direct contact to the head as characterized on video. Using wearable sensors and video to verify head impacts may decrease the inclusion of false-positive impacts during game activity in the analysis.

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

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

Head-Impact-Measurement Devices: A Systematic Review

CONTEXT

With an estimated 3.8 million sport- and recreation-related concussions occurring annually, targeted prevention and diagnostic methods are needed. Biomechanical analysis of head impacts may provide quantitative information that can inform both prevention and diagnostic strategies.

OBJECTIVE

To assess available head-impact devices and their clinical utility.

DATA SOURCES

We performed a systematic search of the electronic database PubMed for peer-reviewed publications, using the following phrases: accelerometer and concussion, head impact telemetry, head impacts and concussion and sensor, head impacts and sensor, impact sensor and concussion, linear acceleration and concussion, rotational acceleration and concussion, and xpatch concussion. In addition to the literature review, a Google search for head impact monitor and concussion monitor yielded 15 more devices.

STUDY SELECTION

Included studies were performed in vivo, used commercially available devices, and focused on sport-related concussion.

DATA EXTRACTION

One author reviewed the title and abstract of each study for inclusion and exclusion criteria and then reviewed each full-text article to confirm inclusion criteria. Controversial articles were reviewed by all authors to reach consensus.

DATA SYNTHESIS

In total, 61 peer-reviewed articles involving 4 head-impact devices were included. Participants in boxing, football, ice hockey, soccer, or snow sports ranged in age from 6 to 24 years; 18% (n = 11) of the studies included female athletes. The Head Impact Telemetry System was the most widely used device (n = 53). Fourteen additional commercially available devices were presented.

CONCLUSIONS

Measurements collected by impact monitors provided real-time data to estimate player exposure but did not have the requisite sensitivity to concussion. Proper interpretation of previously reported head-impact kinematics across age, sport, and position may inform future research and enable staff clinicians working on the sidelines to monitor athletes. However, head-impact-monitoring systems have limited clinical utility due to error rates, designs, and low specificity in predicting concussive injury.

Brain strain uncertainty due to shape variation in and simplification of head angular velocity profiles

Head angular velocity, instead of acceleration, is more predictive of brain strains. Surprisingly, no study exists that investigates how shape variation in angular velocity profiles affects brain strains, beyond characteristics such as peak magnitude and impulse duration. In this study, we evaluated brain strain uncertainty due to variation in angular velocity profiles and further compared with that resulting from simplifying the profiles into idealized shapes. To do so, we used reconstructed head impacts from American National Football League for shape extraction and simulated head uniaxial coronal rotations from onset to full stop. The velocity profiles were scaled to maintain an identical peak velocity magnitude and duration in order to isolate the shape for investigation. Element-wise peak maximum principal strains from 44 selected impacts were obtained. We found that the shape of angular velocity profile could significantly affect brain strain magnitude (e.g., percentage difference of 4.29-17.89 % in the whole brain relative to the group average, with cumulative strain damage measure (CSDM) uncertainty range of 23.9 %) but not pattern (correlation coefficient of 0.94-0.99). Strain differences resulting from simplifying angular velocity profiles into idealized shapes were largely within the range due to shape variation, in both percentage difference and CSDM (signed difference of 3.91 % on average, with a typical range of 0-6 %). These findings provide important insight into the uncertainty or confidence in the performance of kinematics-based injury metrics. More importantly, they suggest the feasibility to simplify head angular velocity profiles into idealized shapes, at least within the confinements of the profiles evaluated, to enable real-time strain estimation via pre-computation in the future.

A Review of Instrumented Equipment to Investigate Head Impacts in Sport

Contact, collision, and combat sports have more head impacts as compared to noncontact sports; therefore, such sports are uniquely suited to the investigation of head impact biomechanics. Recent advances in technology have enabled the development of instrumented equipment, which can estimate the head impact kinematics of human subjects in vivo. Literature pertaining to head impact measurement devices was reviewed and usage, in terms of validation and field studies, of such devices was discussed. Over the past decade, instrumented equipment has recorded millions of impacts in the laboratory, on the field, in the ring, and on the ice. Instrumented equipment is not without limitations; however, in vivo head impact data is crucial to investigate head injury mechanisms and further the understanding of concussion.

Head Impact Magnitude in American High School Football

OBJECTIVES

To describe determinants of head impact magnitudes between various play aspects in high school football.

METHODS

Thirty-two high school American football players wore Head Impact Telemetry System instrumented helmets to capture head impact magnitude (linear acceleration, rotational acceleration, and Head Impact Technology severity profile [HITsp]). We captured and analyzed video from 13 games (n = 3888 viewable head impacts) to determine the following play aspects: quarter, impact cause, play type, closing distance, double head impact, player's stance, player's action, direction of gaze, athletic readiness, level of anticipation, player stationary, ball possession, receiving ball, and snapping ball. We conducted random intercepts general linear mixed models to assess the differences in head impact magnitude between play aspects (α = 0.05).

RESULTS

The following aspects resulted in greater head impact magnitude: impacts during the second quarter (HITsp: P = .03); contact with another player (linear, rotational, HITsp: P < .001); initial head impact when the head is struck twice (linear, rotational, HITsp: P < .001); longer closing distances, especially when combined with a 3-point stance or when being struck in the head (linear: P = .03); the 2-point stance (linear, rotational, HITsp: P < .001); and offensive linemen not snapping the ball compared with those snapping the ball (rotational: P = .02, HITsp: P = .02).

CONCLUSIONS

Preventing head impacts caused by contact with another player may reduce head impact magnitude in high school football. Rule or coaching changes that reduce collisions after long closing distances, especially when combined with the 3-point stance or when a player is being struck in the head, should be considered.