A Computational Study of Liquid Shock Absorption for Prevention of Traumatic Brain Injury

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types

OBJECTIVE

The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM.

METHODS

To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).

RESULTS

The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models.

CONCLUSION

The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate.

SIGNIFICANCE

This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Improving outcome after paediatric concussion: challenges and possibilities

The term concussion has permeated mainstream media and household vocabulary mainly due to awareness regarding the risks of concussion in professional contact sports, yet it occurs across a variety of settings and ages. Concussion is prevalent in infants, preschoolers, children, and adolescents, and is a common presentation or reason for referral to primary care providers, emergency departments, and specialised trauma clinics. Its broad range of symptoms and sequelae vary according to multiple individual, environmental, and clinical factors and can lead to health and economic burden. More than 20 years of research into risk factors and consequences of paediatric concussion has revealed as many questions as answers, and scientific work and clinical cases continue to expose its complexity and heterogeneity. In this Review, we present empirical evidence for improving outcome after paediatric concussion. We consider work pertaining to both sports and other injury mechanisms to provide a perspective that should be viewed as complementary to publications focused specifically on sports concussion. Contemporary challenges in prevention, diagnosis, prognosis, and intervention are discussed alongside pathways and future directions for improving outcome.

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.

Impact Performance of 3D Printed Spatially Varying Elastomeric Lattices

Additive manufacturing is catalyzing a new class of volumetrically varying lattice structures in which the dynamic mechanical response can be tailored for a specific application. Simultaneously, a diversity of materials is now available as feedstock including elastomers, which provide high viscoelasticity and increased durability. The combined benefits of complex lattices coupled with elastomers is particularly appealing for anatomy-specific wearable applications such as in athletic or safety equipment. In this study, Siemens' DARPA TRADES-funded design and geometry-generation software, Mithril, was leveraged to design vertically-graded and uniform lattices, the configurations of which offer varying degrees of stiffness. The designed lattices were fabricated in two elastomers using different additive manufacturing processes: (a) vat photopolymerization (with compliant SIL30 elastomer from Carbon) and (b) thermoplastic material extrusion (with Ultimaker™ TPU filament providing increased stiffness). Both materials provided unique benefits with the SIL30 material offering compliance suitable for lower energy impacts and the Ultimaker™ TPU offering improved protection against higher impact energies. Moreover, a hybrid lattice combination of both materials was evaluated and demonstrated the simultaneous benefits of each, with good performance across a wider range of impact energies. This study explores the design, material, and process space for manufacturing a new class of comfortable, energy-absorbing protective equipment to protect athletes, consumers, soldiers, first responders, and packaged goods.

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.

Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards

Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain's response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0's validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s² for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.