Relative Motion between the Helmet and Head in Football Impact Test

An Assessment of Sikh Turban's Head Protection in Bicycle Incident Scenarios

Due to religious tenets, Sikh population wear turbans and are exempted from wearing helmets in several countries. However, the extent of protection provided by turbans against head injuries during head impacts remains untested. One aim of this study was to provide the first-series data of turbans' protective performance under impact conditions that are representative of real-world bicycle incidents and compare it with the performance of bicycle helmets. Another aim was to suggest potential ways for improving turban's protective performance. We tested five different turbans, distinguished by two wrapping styles and two fabric materials with a size variation in one of the styles. A Hybrid III headform fitted with the turban was dropped onto a 45 degrees anvil at 6.3 m/s and head accelerations were measured. We found large difference in the performance of different turbans, with up to 59% difference in peak translational acceleration, 85% in peak rotational acceleration, and 45% in peak rotational velocity between the best and worst performing turbans. For the same turban, impact on the left and right sides of the head produced very different head kinematics, showing the effects of turban layering. Compared to unprotected head impacts, turbans considerably reduce head injury metrics. However, turbans produced higher values of peak linear and rotational accelerations in front and left impacts than bicycle helmets, except from one turban which produced lower peak head kinematics values in left impacts. In addition, turbans produced peak rotational velocities comparable with bicycle helmets, except from one turban which produced higher values. The impact locations tested here were covered with thick layers of turbans and they were impacted against flat anvils. Turbans may not provide much protection if impacts occur at regions covered with limited amount of fabric or if the impact is against non-flat anvils, which remain untested. Our analysis shows that turbans can be easily compressed and bottom out creating spikes in the headform's translational acceleration. In addition, the high friction between the turban and anvil surface leads to higher tangential force generating more rotational motion. Hence, in addition to improving the coverage of the head, particularly in the crown and rear locations, we propose two directions for turban improvement: (i) adding deformable materials within the turban layers to increase the impact duration and reduce the risk of bottoming out; (ii) reducing the friction between turban layers to reduce the transmission of rotational motion to the head. Overall, the study assessed Turbans' protection in cyclist head collisions, with a vision that the results of this study can guide further necessary improvements for advanced head protection for the Sikh community.

Capturing Head Impacts in Boxing: A Video-Based Comparison of Three Wearable Sensors

Wearable sensors are used to quantify head impacts in athletes, but recent work has shown that the number of events recorded may not be accurate. This study aimed to compare the number of head acceleration events recorded by three wearable sensors during boxing and assess how impact type and location affect the triggering of acceleration events. Seven boxers were equipped with an instrumented mouthguard, a skin patch, and a headgear patch. Contacts to participants' heads were identified via three video cameras over 115 sparring rounds. The resulting 5168 video-identified events were used as reference to quantify the sensitivity, specificity, and positive predictive value (PPV) of the sensors. The mouthguard, skin patch, and headgear patch recorded 695, 1579, and 1690 events, respectively, yielding sensitivities of 35%, 86%, and 78%, respectively, and specificities of 90%, 76%, and 75%, respectively. The mouthguard, skin patch, and headgear patch yielded 693, 1571, and 1681 true-positive events, respectively, leading to PPVs for head impacts over 96%. All three sensors were more likely to be triggered by punches landing near the sensor and cleanly on the head, although the mouthguard's sensitivity to impact location varied less than the patches. While the use of head impact sensors for assessing injury risks remains uncertain, this study provides valuable insights into the capabilities and limitations of these sensors in capturing video-verified head impact events.

Differences in head impact biomechanics between playing positions in Canadian high school football players

The objective of this study was to compare head impact magnitudes and time between impacts among positions in Canadian high-school football. Thirty nine players from two high-school football teams were recruited and assigned a position profile: Profile 1 (quarterback, receiver, defensive back, kicker), Profile 2 (linebacker, running back), and Profile 3 (linemen). Players wore instrumented mouthguards to measure peak magnitudes of linear and angular acceleration and velocity for each head impact throughout the season. A principal component analysis reduced the dimensionality of biomechanical variables, resulting in one principal component (PC1) score assigned to every impact. Time between impacts was calculated by subtracting the timestamps of subsequent head impacts within a session. Significant differences in PC1 scores and time between impacts occurred between playing position profiles (ps<0.001). Post-hoc comparisons determined that PC1 was greatest in Profile 2, followed by Profiles 1 and 3. Time between impacts was lowest in Profile 3, followed by Profiles 2 and 1. This study delivers a new method of reducing the multidimensionality of head impact magnitudes and suggests different Canadian high-school football playing positions experience different head impact magnitudes and frequencies, which is important for monitoring concussion and repetitive head impact exposure.

Evaluation of the Fall Protection of Type I Industrial Helmets

The performance of Type I industrial helmets for fall protection is not required to be tested in standardized tests. The current study analyzed the fall protection performance of Type I industrial helmets and evaluated if the use of a chin strap and the suspension system tightness have any effect on protection performance. Head impact tests were performed using an instrumented manikin. There were 12 combinations of test conditions: with or without chin strap usage, three levels of suspension system tightness, and two impact surfaces. Four representative helmet models (two basic and two advanced models) were selected for the study. Impact tests without a helmet under all other applicable test conditions were used as a control group. There were four replicates for each test condition-a total of 192 impact tests with helmets and eight impact tests for the control group. The peak acceleration and the calculated head impact criteria (HIC) were used to evaluate shock absorption performance of the helmets. The results showed that all four helmet models demonstrated excellent performance for fall protection compared to the barehead control group. The fall protection performance of the advanced helmet models was substantially better than the basic helmet models. However, the effects of the use of chin straps and suspension system tightness on the helmets' fall protection performance were statistically not significant.

Estimation of Head Accelerations in Crashes Using Neural Networks and Sensors Embedded in the Protective Helmet

Traumatic Brain Injuries (TBIs) are one of the most frequent and severe outcomes of a Powered Two-Wheeler (PTW) crash. Early diagnosis and treatment can greatly reduce permanent consequences. Despite the fact that devices to track head kinematics have been developed for sports applications, they all have limitations, which hamper their use in everyday road applications. In this study, a new technical solution based on accelerometers integrated in a motorcycle helmet is presented, and the related methodology to estimate linear and rotational acceleration of the head with deep Artificial Neural Networks (dANNs) is developed. A finite element model of helmet coupled with a Hybrid III head model was used to generate data needed for the neural network training. Input data to the dANN model were time signals of (virtual) accelerometers placed on the inner surface of the helmet shell, while the output data were the components of linear and rotational head accelerations. The network was capable of estimating, with good accuracy, time patterns of the acceleration components in all impact conditions that require medical treatment. The correlation between the reference and estimated values was high for all parameters and for both linear and rotational acceleration, with coefficients of determination (R2) ranging from 0.91 to 0.97.

Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis.

Influence of Friction at the Head-Helmet Interface on Advanced Combat Helmet (ACH) Blunt Impact Kinematic Performance

INTRODUCTION

The purpose of this study was to compare the rotational blunt impact performance of an anthropomorphic test device (ATD: male 50% Hybrid III head and neck) headform donning an Advanced Combat Helmet (ACH) between conditions in which the coefficient of static friction (μs) at the head-to-helmet pad interface varied.

MATERIALS AND METHODS

Two ACHs (size large) were used in this study and friction was varied using polytetrafluoroethylene (PTFE), human hair, skullcap, and the native vinyl skin of the ATD. A condition in which hook and loop material adhered the headform to the liner system was also tested, resulting in a total of five conditions: PTFE, Human Hair, Skullcap, Vinyl, and Hook. Blunt impact tests with each helmet in each of the five conditions were conducted on a pneumatic linear impactor at 4.3 m/s. The ATD donning the ACH was impacted in seven locations (Crown, Front, Rear, Left Side, Right Side, Left Nape, and Right Nape). The peak resultant angular acceleration (PAA), velocity (PAV), and the Diffuse Axonal Multi-Axis, General Evaluation (DAMAGE) metric were compared between conditions.

RESULTS

No pairwise differences were observed between conditions for PAA. A positive correlation was observed between mean μs and PAA at the Front (τ = 0.28; P = .044) and Rear (τ = 0.31; P = .024) impact locations. The Hook condition had a mean PAV value that was often less than the other conditions (P ≤ .024). A positive correlation was observed between mean μs and PAV at the Front (τ = 0.32; P = .019) and Right Side (τ = 0.57; P < .001) locations. The Hook condition tended to have the lowest DAMAGE value compared to the other conditions (P ≤ .032). A positive correlation was observed between the mean μs and DAMAGE at the Rear (τ = 0.60; P < .001) location. A negative correlation was observed at the Left Side (τ = -0.28; P = .040), Right Side (τ = -0.58; P < .001) and Left Nape (τ = -0.56; P < .001) locations.

CONCLUSIONS

The results of this study indicate that at some impact locations kinematic responses can vary as a function of the friction at the head-to-helmet pad interface. However, a reduction in the coupling of the head-helmet pad interface did not consistently reduce head angular kinematics or measures of brain strain across impact locations. Thus, for the ACH during collision-type impacts, impact location as opposed to μs seems to have a greater influence on head kinematics and rotational-based measures of brain strain.

Concussion biomechanics, head acceleration exposure and brain injury criteria in sport: a review

There are mounting concerns surrounding the risk of neurodegenerative diseases and complications associated with concussion incidence and repetitive head acceleration events (HAE) in sport. The aim of this review is to provide an overview of concussion biomechanics, head acceleration exposure and brain injury criteria in sport. Rotational head motion appears to be the primary contributor to brain injury risk due to the unique mechanical properties of the brain and its location within the body. There is a growing evidence base of different biomechanical brain injury mechanisms, including those involving repetitive HAE. Historically, many studies on concussion biomechanics, head acceleration exposure and brain injury criteria in sport have been limited by validity of the biomechanical approaches undertaken. Biomechanical approaches such as instrumented mouthguards and subject-specific finite element (FE) brain models provide a unique opportunity to develop greater brain injury criteria and aid in on-field athlete removal. Implementing these approaches on a large-scale can gain insight into potential risk factors within sports and certain athletes/cohorts who sustain a greater number and/or severity of HAE throughout their playing career. These findings could play a key role in the development of concussion prevention strategies and techniques that mitigate the severity of HAE in sport.

Sex differences in neck strength and head impact kinematics in university rugby union players

Globally, over three million women participate in rugby union, yet injury prevention and training strategies are predominantly based on androcentric data. These strategies may have limited generalisability to females, given the cervical spine is more susceptible to whiplash and less adept at resisting inertial loading. A total of 53 university rugby union players (25 female, 28 male, 20.7 ± 1.8 years) had their isometric neck strength measured. Bespoke instrumented mouthguards were used to record the magnitude of head impact events in six female and seven male competitive matches. Mean female maximal isometric neck strength was 47% lower than male. Independent samples Mann-Whitney U tests showed no significant differences for peak linear head acceleration (female: median 11.7 g, IQR 7.9 g; male: median 12.5 g, IQR 7.0 g p=.23) or peak rotational head acceleration (female: median 800.2 rad·s^-2, IQR 677.7 rad·s^-2; male: median 849.4 rad·s^-2, IQR 479.8 rad·s^-2; p=.76), despite the mean male body mass being 24% greater than female. Coded video analysis revealed substantial differences in head-impact mechanisms; uncontrolled whiplash dominated >50% of all recorded female impact events and <0.5% in males. Direct head-to-ground impacts comprised 26.1% of female and 9.7% of male impacts, with whiplash occurring in 78.0% and 0.5%, respectively. Overall, the data provided in this study do not support the generalisation of male-derived training and injury-prevention data to female rugby athletes. These results suggest a considerable research effort is required to identify specific weakness of female rugby players and derive appropriate training, injury prevention and return to play protocols.

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.

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.

A Review of On-Field Investigations into the Biomechanics of Concussion in Football and Translation to Head Injury Mitigation Strategies

This review paper summarizes the scientific advancements in the field of concussion biomechanics in American football throughout the past five decades. The focus is on-field biomechanical data collection, and the translation of that data to injury metrics and helmet evaluation. On-field data has been collected with video analysis for laboratory reconstructions or wearable head impact sensors. Concussion biomechanics have been studied across all levels of play, from youth to professional, which has allowed for comparison of head impact exposure and injury tolerance between different age groups. In general, head impact exposure and injury tolerance increase with increasing age. Average values for concussive head impact kinematics are lower for youth players in both linear and rotational acceleration. Head impact data from concussive and non-concussive events have been used to develop injury metrics and risk functions for use in protective equipment evaluation. These risk functions have been used to evaluate helmet performance for each level of play, showing substantial differences in the ability of different helmet models to reduce concussion risk. New advances in head impact sensor technology allow for biomechanical measurements in helmeted and non-helmeted sports for a more complete understanding of concussion tolerance in different demographics. These sensors along with advances in finite element modeling will lead to a better understanding of the mechanisms of injury and human tolerance to head impact.

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.

Finite Element Model of a Deformable American Football Helmet Under Impact

Despite the use of helmets in American football, brain injuries are still prevalent. To reduce the burden of these injuries, novel impact mitigation systems are needed. The Vicis Zero1 (VZ1) American football helmet is unique in its use of multi-directional buckling structures sandwiched between a deformable outer shell and a stiff inner shell. The objective of this study was to develop a model of the VZ1 and to assess this unique characteristic for its role in mitigating head kinematics. The VZ1 model was developed using a bottom-up framework that emphasized material testing, constitutive model calibration, and component-level validation. Over 50 experimental tests were simulated to validate the VZ1 model. CORrelation and Analysis (CORA) was used to quantify the similarity between experimental and model head kinematics, neck forces, and impactor accelerations and forces. The VZ1 model demonstrated good correlation with an overall mean CORA score of 0.86. A parametric analysis on helmet compliance revealed that the outer shell and column stiffness influenced translational head kinematics more than rotational. For the material parameters investigated, head linear acceleration ranged from 80 to 220 g, whereas angular velocity ranged from 37 to 40 rad/s. This helmet model is open-source and serves as an in silico design platform for helmet innovation.