Development and Multi-Scale Validation of a Finite Element Football Helmet Model

Effect of Structure and Wearing Modes on the Protective Performance of Industrial Safety Helmet

This study aims to explore the effects of helmet structure designs and wearing modes on the protective performance of safety helmets under the impact of falling objects. Four helmet types (no helmet, V-shaped, dome-shaped, and motorcycle helmets) and five wearing modes (left and right tilt by 5 deg, backward tilt by 15 deg, 0 deg without chin strap, 0 deg with chin strap) were included in this study. The axial impact of a concrete block under various impact velocities was simulated. The results indicate that the energy absorption and shock mitigation effects of the foam cushion are superior to those of the suspension system in traditional industrial safety helmets. The structure of the top of V-shaped helmets is designed to withstand greater impact. Regarding the wearing mode, the helmet strap's deflection angle increases stress in the brain tissue and skull, heightens intracranial pressure, and causes pressure diffusion toward the forehead.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts

Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.

A Review of Image-Based Simulation Applications in High-Value Manufacturing

Image-Based Simulation (IBSim) is the process by which a digital representation of a real geometry is generated from image data for the purpose of performing a simulation with greater accuracy than with idealised Computer Aided Design (CAD) based simulations. Whilst IBSim originates in the biomedical field, the wider adoption of imaging for non-destructive testing and evaluation (NDT/NDE) within the High-Value Manufacturing (HVM) sector has allowed wider use of IBSim in recent years. IBSim is invaluable in scenarios where there exists a non-negligible variation between the 'as designed' and 'as manufactured' state of parts. It has also been used for characterisation of geometries too complex to accurately draw with CAD. IBSim simulations are unique to the geometry being imaged, therefore it is possible to perform part-specific virtual testing within batches of manufactured parts. This novel review presents the applications of IBSim within HVM, whereby HVM is the value provided by a manufactured part (or conversely the potential cost should the part fail) rather than the actual cost of manufacturing the part itself. Examples include fibre and aggregate composite materials, additive manufacturing, foams, and interface bonding such as welding. This review is divided into the following sections: Material Characterisation; Characterisation of Manufacturing Techniques; Impact of Deviations from Idealised Design Geometry on Product Design and Performance; Customisation and Personalisation of Products; IBSim in Biomimicry. Finally, conclusions are drawn, and observations made on future trends based on the current state of the literature.

Whitewater Helmet STAR: Evaluation of the Biomechanical Performance and Risk of Head Injury for Whitewater Helmets

More than six million people participate in whitewater kayaking and rafting in the United States each year. Unfortunately, with these six million whitewater participants come 50 deaths annually, making it one of the highest fatality rates of all sports. As the popularity in whitewater activities grows, the number of injuries, including concussions, also increases. The objective of this study was to create a new rating system for whitewater helmets by evaluating the biomechanical performance and risk of head injury of whitewater helmets using the Summation of Tests for the Analysis of Risk (STAR) system. All watersport helmets that passed the EN: 1385: 2012 standard and that were clearly marketed for whitewater use were selected for this study. Two samples of each helmet model were tested on a custom pendulum impactor under conditions known to be associated with the highest risk of head injury and death. A 50th percentile male NOCSAE headform instrumented with three linear accelerometers and a triaxial angular rate sensor coupled with a Hybrid III 50th percentile neck were used for data collection. A total of 126 tests were performed using six different configurations. These included impacts to the front, side, and rear using two speeds of 3.1 and 4.9 m/s that modeled whitewater river flow rates. Each helmet's STAR score was calculated using the combination of exposure and injury risk that was determined from the linear and rotational head accelerations. The resulting head impact accelerations predicted a very high risk of concussion for all impact locations at the 4.9 m/s speed. The STAR score varied between helmets indicating that some helmets provide better protection than others. Overall, these results show a clear need for improvement in whitewater helmets, and the methodologies developed in this research project should provide manufacturers a design tool for improving these products.

APPLICATION OF PHYSICAL TRAINING IN CONJUNCTION WITH REHABILITATION IN MALE SOCCER INJURY

ABSTRACT Introduction: Soccer is one of the sports with the highest incidence of injuries, generated both by the high performance required on the field and by physical conflicts between players. A fast rehabilitation is essential for the player’s performance. It has been empirically observed that an early recovery in patients’ rehabilitation is associated with physical training compared to players who received only the traditional rehabilitation. Objective: Verify the physical training influence on rehabilitation sports injuries in male soccer players. Method: 180 cases of male adolescents with sports injuries admitted to a Taiyuan hospital were selected. A division into two groups was randomly computerized to avoid statistical differences in the intensity of the injuries. The control group (14.3±2.45 years old) was treated with the standard protocol, while the experimental group (15.3±2.95 years old) received an intervention with physical training. Interviews and questionnaires were conducted involving analysis of time and severity of the injury, laterality, and location. The control group received treatment based on this information. The recovery rate and intervention satisfaction rate were collected. SPSS22.0 Statistical Software was used for student’s t-test and chi-square test. Results: Treatment efficiency was 82 (91.11%) in the control group versus 88 (97.78%) in the experimental group. The degree of dissatisfaction was 1 (2.11%) versus 8 (10%). The perceived overall satisfaction was 80 (89%) versus 87 (96.67%), (p <0.05). Conclusion: Rehabilitation associated with physical training intervention improved satisfaction and treatment efficiency. Evidence Level II; Therapeutic Studies – Investigating the results.

Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios

As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.

Physics-Informed Machine Learning Improves Detection of Head Impacts

In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F₁ score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

Effect of body size and enhanced helmet systems on risk for motorsport drivers

OBJECTIVE

Computational modeling has been shown to be a useful tool for simulating representative motorsport impacts and analyzing data for relative injury risk assessment. Previous studies have used computational modeling to analyze the probability of injury in specific regions of a 50th percentile male driver. However, NASCAR drivers can represent a large range in terms of size and female drivers are becoming increasingly more common in the sport. Additionally, motorsport helmets can be outfitted with external attachments, or enhanced helmet systems (EHS), whose effect is unknown relative to head and neck kinematics. The current study expands on this previous work by incorporating the F05-OS and M95-OS into the motorsport environment in order to determine correlations between metrics and factors such as PDOF, resultant ΔV occupant size, and EHS.

METHODS

A multi-step computational process was used to integrate the Global Human Body Models Consortium family of simplified occupant models into a motorsport environment. This family included the 5th percentile female (F05-OS), 50th percentile male (M50-OS), and 95th percentile male (M95-OS), which provide a representative range for the size and sex of drivers seen in NASCAR's racing series'. A series of 45 representative impacts, developed from real-world crash data, and set of observed on-track severe impacts were conducted with these models. These impacts were run in triplicate for three helmet configurations: bare helmet, helmet with visor, helmet with visor and camera. This resulted in 450 total simulations. A paired t-test was initially performed as an exploratory analysis to study the effect of helmet configuration on 10 head and neck injury metrics. A mixed-effects model with unstructured covariance matrix was then utilized to correlate the effect between five independent variables (resultant ΔV, body size, helmet configuration, impact quadrant, and steering wheel position) and a selection of 25 metrics. All simulations were conducted in LS-Dyna R. 9.1.

RESULTS

Risk estimates from the M50-OS with bare helmet were used as reference values to determine the effect of body size and helmet configuration. The paired t-test found significance for helmet configuration in select head-neck metrics, but the relative increase in these metrics was low and not likely to increase injury risk. The mixed-effects model analyzed statistical relationships across multiple types of variables. Within the mixed-effects model, no significance was found between helmet configuration and metrics. The greatest effect was found from resultant ΔV, body size, and impact quadrant.

CONCLUSIONS

Overall, smaller drivers showed statistically significant reductions in injury metrics, while larger drivers showed statistically significant increases. Lateral impacts showed the greatest effect on neck metrics and, on average, showed decreases for head metrics related to linear acceleration and increases for head metrics related to angular velocity. HBM parametric studies such as this may provide an avenue to assist injury detection for motorsport incidents, improve triage effectiveness, and assist in the development of safety standards.

Application of polyethylene air-bubble cushions to improve the shock absorption performance of Type I construction helmets for repeated impacts

BACKGROUND

The use of helmets was considered to be one of the important prevention strategies employed on construction sites. The shock absorption performance of a construction (or industrial) helmet is its most important performance parameter. Industrial helmets will experience cumulative structural damage when being impacted repeatedly with impact magnitudes greater than its endurance limit.

OBJECTIVE

The current study is to test if the shock absorption performance of Type I construction helmets subjected to repeated impacts can be improved by applying polyethylene air-bubble cushions to the helmet suspension system.

METHODS

Drop impact tests were performed using a commercial drop tower test machine following the ANSI Z89.1 Type I drop impact protocol. Typical off-the-shelf Type I construction helmets were evaluated in the study. A 5 mm thick air-bubble cushioning liner was placed between the headform and the helmet to be tested. Helmets were impacted ten times at different drop heights from 0.61 to 1.73 m. The effects of the air-bubble cushioning liner on the helmets' shock absorption performance were evaluated by comparing the peak transmitted forces collected from the original off-the-shelf helmet samples to the helmets equipped with air-bubble cushioning liners.

RESULTS

Our results showed that a typical Type I construction helmet can be subjected to repeated impacts with a magnitude less than 22 J (corresponding to a drop height 0.61 m) without compromising its shock absorption performance. In comparison, the same construction helmet, when equipped with an air-bubble cushioning liner, can be subjected to repeated impacts of a magnitude of 54 J (corresponding to a drop height 1.52 m) without compromising its shock absorption performance.

CONCLUSIONS

The results indicate that the helmet's shock absorbing endurance limit has been increased by 145% with addition of an air-bubble cushioning liner.

Evaluation and Validation of Thorax Model Responses: A Hierarchical Approach to Achieve High Biofidelity for Thoracic Musculoskeletal System

As one of the most frequently occurring injuries, thoracic trauma is a significant public health burden occurring in road traffic crashes, sports accidents, and military events. The biomechanics of the human thorax under impact loading can be investigated by computational finite element (FE) models, which are capable of predicting complex thoracic responses and injury outcomes quantitatively. One of the key challenges for developing a biofidelic FE model involves model evaluation and validation. In this work, the biofidelity of a mid-sized male thorax model has been evaluated and enhanced by a multi-level, hierarchical strategy of validation, focusing on injury characteristics, and model improvement of the thoracic musculoskeletal system. At the component level, the biomechanical responses of several major thoracic load-bearing structures were validated against different relevant experimental cases in the literature, including the thoracic intervertebral joints, costovertebral joints, clavicle, sternum, and costal cartilages. As an example, the thoracic spine was improved by accurate representation of the components, material properties, and ligament failure features at tissue level then validated based on the quasi-static response at the segment level, flexion bending response at the functional spinal unit level, and extension angle of the whole thoracic spine. At ribcage and full thorax levels, the thorax model with validated bony components was evaluated by a series of experimental testing cases. The validation responses were rated above 0.76, as assessed by the CORA evaluation system, indicating the model exhibited overall good biofidelity. At both component and full thorax levels, the model showed good computational stability, and reasonable agreement with the experimental data both qualitatively and quantitatively. It is expected that our validated thorax model can predict thorax behavior with high biofidelity to assess injury risk and investigate injury mechanisms of the thoracic musculoskeletal system in various impact scenarios. The relevant validation cases established in this study shall be directly used for future evaluation of other thorax models, and the validation approach and process presented here may provide an insightful framework toward multi-level validating of human body models.

An Experimental Platform Generating Simulated Blunt Impacts to the Head Due to Rearward Falls

Impacts to the back of the head due to rearward falls, also referred to as "backfall" events, represent a common source of TBI for athletes and soldiers. A new experimental apparatus is described for replicating the linear and rotational kinematics of the head during backfall events. An anthropomorphic test device (ATD) with a head-borne sensor suite was configured to fall backwards from a standing height, inducing contact between the rear of the head and a ground surface simulant. A pivoting swing arm and release strap were used to generate consistent and realistic head kinematics. Backfall experiments were performed with the ATD fitted with an American football helmet and the resulting linear and rotational head kinematics, as well as calculated injury metrics, compared favorably with those of football players undergoing similar impacts during games or play reconstructions. This test method complements existing blunt impact helmet performance experiments, such as drop tower and pneumatic ram test methods, which may not be able to fully reproduce head-neck-torso kinematics during a backfall event.

A Systematic Review of the Orthopedic Literature Involving National Hockey League Players

Background

Orthopedic injuries of National Hockey League (NHL) players are common and may significantly affect players’ health and careers. Evidence-based injury management is important in guiding players’ timely return to sport and their ability to play at their pre-injury levels of competition.

Purpose

To summarize all data published between January 1980 and March 2020 on orthopedic injuries experienced by professional ice hockey players competing in the NHL.

Study Design

Systematic review.

Methods

A literature review of studies examining orthopedic injuries in the NHL was performed using the Embase, PubMed, and CINAHL databases. The review included studies focusing on NHL players and players attending the NHL Combine and preseason NHL team camps. Studies pertaining to non-orthopedic injuries and case reports were excluded.

Results

A total of 39 articles met the inclusion criteria and were analyzed. The articles were divided by anatomic site of injury for further analysis: hip and pelvis (24%), general/other (14%), ankle (10%), knee (10%), foot (7%), shoulder (7%), thigh (7%), trunk (7%), spine (6%), elbow (4%), and hand and wrist (4%). The majority of articles were Level IV Evidence (51.3%), followed by Level III Evidence (38.5%). Most studies obtained data from publicly available internet resources (24.7%), player medical records (19.5%) or surveys of team physicians and athletic trainers (15.5%). A much smaller number of studies utilized the NHL Injury Surveillance System (NHLISS) (6.5%) or the Athlete Health Management System (AHMS) (2.6%).

Conclusion

This systematic review provides NHL team physicians with a single source of the current literature regarding orthopedic injuries in NHL players. Most research was published on hip and pelvis (24%) injuries, did not utilize the NHLISS and consisted of Level IV Evidence.

Simulation-based assessment of injury risk for an average male motorsport driver

OBJECTIVE

While well-protected through a variety of safety countermeasures, motorsports drivers can be exposed to a large variety of crash modes and severities. Computational human body models (HBMs) are currently used to assess occupant safety for the general driving public in production vehicles. The purpose of this study was to incorporate a HBM into a motorsport environment using a simulation-based approach and provide quantitative data on relative risk for on-track motorsport crashes.

METHODS

Unlike a traditional automotive seat, the NASCAR driver environment is driver-customized and form-fitting. A multi-step process was developed to integrate the Global Human Body Models Consortium (GHBMC) 50th percentile male simplified occupant into a representative motorsport environment which includes a donned helmet, a 7-point safety belt system, head and neck restraint (HNR), poured-foam seat, steering wheel, and leg enclosure. A series of 45 representative impacts, developed from real-world crash data, of varying severity (10 kph ≤ ΔV ≤ 100 kph) and impact direction (∼290° ≤ PDOF ≤ 20°) were conducted with the GHBMC 50th percentile male simplified occupant (M50-OS v2.2). Kinematic and kinetic data, and a variety of injury criteria, were output from each of the simulations and used to calculate AIS 1+, 2+, and 3+ injury risk. All simulations were conducted in LS-Dyna R. 9.1.

RESULTS

Injury risk of the occupant using the previously mentioned injury criteria was calculated for the head, neck, thorax, and lower extremity, and the probability of injury for each region was plotted. Of the simulated impacts, five had a maximum AIS 1+ injury risk >20%, six had a maximum AIS 2+ injury risk >10%, and no cases had a maximum AIS 3+ injury >1%. Overall, injury risk estimates were reasonable compared to on-track data reported from Patalak et al. (2020).

CONCLUSIONS

Beyond injury risk, the study is the first of its kind to provide mechanical loading values likely experienced during motorsports crash incidents with crash pulses developed from real-world data. Given the severity of the crash pulses, the simulated environments reinforce the need for the robust safety environment implemented by NASCAR.

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.