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Because of the relatively rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration (mean relative error, MRE < 13%), the peak angular velocity (MRE < 8%), brain injury criteria values (MRE < 13%) and brain deformation (described as maximum principal strain and fiber strain, calculated by a convolutional neural network based brain model, MRE < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.
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Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude, and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics in three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.
Wearable devices have been shown to effectively measure the head movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain, which is used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed head impacts collected by the Stanford Instrumented Mouthguard. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 20 ms and a post-trigger time of 70 ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200 ms time window recorded by the mouthguard.
Concussion and repeated exposure to mild traumatic brain injury are risks for athletes in many sports. While direct head impacts are analyzed to improve the detection and awareness of head acceleration events so that an athletes brain health can be appropriately monitored and treated. However, head accelerations can also be induced by impacts with little or no head involvement. In this work we evaluated if impacts that do not involve direct head contact, such as being pushed in the torso, can be sufficient in collegiate American football to induce head accelerations comparable to direct head impacts. Datasets of impacts with and without direct head contact were collected and compared. These datasets were gathered using a state-of-the-art impact detection algorithm embedded in an instrumented mouthguard to record head kinematics. Video analysis was used to differentiate between impact types. In total, 15 impacts of each type were used in comparison, with clear video screenshots available to distinguish each impact type. Analysis of the kinematics showed that the impacts without direct head contact achieved similar levels of linear and angular accelerations during impact compared to those from direct head impacts. Finite element analyses using the median and peak kinematic signals were used to calculate maximum principal strain of the brain. Statistical analysis revealed that no significant difference was found between the two datasets based on a Bonferroni-adjusted p-value threshold of 0.017 , with the exception of peak linear acceleration. Impacts without direct head contact showed higher mean values of peak linear acceleration values of 17.6 g compared to the direct-head impact mean value of 6.1g. These results indicated that impacts other than direct head impacts could still produce meaningful kinematic loads in the head and as such should be included in athlete health monitoring.
Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (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 different types of head impacts has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BICs 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 each of 18 BIC respectively. The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit 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.
Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are less accurate across the variety of impacts that patients may undergo. We investigated the spectral characteristics of different head impact types with kinematics classification. Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football), reaching a median accuracy of 96% over 1,000 random partitions of training and test sets. To test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96% accuracy. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range). Finally, with the classifier, type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile). This showed a generally higher R2-value than baseline models. The classifier enables a better understanding of the impact kinematics in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation. Key words: traumatic brain injury, head impacts, classification, impact kinematics
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