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Traumatic brain injury (TBI) is a complex injury that is hard to predict and diagnose, with many studies focused on associating head kinematics to brain injury risk. Recently, there has been a push towards using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the Brain Angle Metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of a FE brain model simulated with live human impact data. We show it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations. On our dataset, the simplified model highly correlates with peak principal FE strain (R 2 =0.80). Further, coronal and axial model displacement correlated with fiber-oriented peak strain in the corpus callosum (R 2 =0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model, and is compared against a number of existing rotational and translational kinematic injury metrics on a dataset of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, linear acceleration, and angular velocity in classifying injury and non-injury events. Metrics which separated time traces into their directional components had improved model deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work both as a computationally efficient alternative to FE models and for classifying injuries over a wide range of loading conditions. Key words:Brain injury, injury criterion, injury prediction, concussion 159.32, 131.53, and 132.02 respectively. Further, metrics such as HIC and SI which analyze acceleration magnitudes performed with lower sensitivity to those which treated each direction separately. Similarly, the VTCP, which takes into account peak linear and angular acceleration magnitude, had lower model deviance and higher AUCPR and AUCROC than metrics which treated each anatomical direction separately. Peak linear acceleration ( ⃗) had lower deviance, AUCPR, and AUCROC to peak angular kinematics and BAM but still outperformed many other metrics. While many previous studies suggest rotation is a primary cause of brain injury 16,17,56 , the results shown here indicate that linear acceleration still has predictive value in classifying brain injuries. This could be because in our dataset, with the majority of injuries taken from laboratory reconstruction data, the linear and angular acceleration values may be coupled more so than in-vivo data.However, single variate injury criteria, based on linear acceleration, had extremely low sensitivity in our dataset. We see that both HIC15 and SI predict only a single event with >50% risk of injury on our dataset due to a few non-injury events with high HIC15 and SI values. Surprisingly, many existing metrics had hig...

Concern about the consequences of head impacts in US football has motivated researchers to investigate and develop instrumentation to measure the severity of these impacts. However, the severity of head impacts in unhelmeted sports is largely unknown as miniaturised sensor technology has only recently made it possible to measure these impacts in vivo. The objective of this study was to measure the linear and angular head accelerations in impacts in mixed martial arts, and correlate these with concussive injuries. Thirteen mixed martial arts fighters were fitted with the Stanford instrumented mouthguard (MiG2.0) participated in this study. The mouthguard recorded linear acceleration and angular velocity in 6 degrees of freedom. Angular acceleration was calculated by differentiation. All events were video recorded, time stamped and reported impacts confirmed. A total of 451 verified head impacts above 10g were recorded during 19 sparring events (n = 298) and 11 competitive events (n = 153). The average resultant linear acceleration was 38.0624.3g while the average resultant angular acceleration was 256761739 rad/s2. The competitive bouts resulted in five concussions being diagnosed by a medical doctor. The average resultant acceleration (of the impact with the highest angular acceleration) in these bouts was 86.7618.7g and 756163438 rad/s2. The average maximum Head Impact Power was 20.6kW in the case of concussion and 7.15kW for the uninjured athletes. In conclusion, the study recorded novel data for sub-concussive and concussive impacts. Events that resulted in a concussion had an average maximum angular acceleration that was 24.7% higher and an average maximum Head Impact Power that was 189% higher than events where there was no injury. The findings are significant in understanding the human tolerance to short-duration, high linear and angular accelerations.

The requirement to measure the number and severity of head impacts in sports has led to the development of many wearable sensors. The objective of this study was to determine the reliability and accuracy of a wearable head impact sensor: xPatch, X2Biosystems, Inc. The skin-mounted sensor, xPatch, was fixed onto a Hybrid III headform and dropped using an impact test rig. A total of 400 impacts were performed, ranging from 20g to 200g linear acceleration, and impact velocities of 1.2-3.9 m/s. During each impact, the peak linear acceleration, angular velocity and angular acceleration were recorded and compared to the reference calibrated data. Impacts were also recorded using a high-speed video camera. The results show that the linear acceleration recorded by the xPatch during frontal and side impacts had errors of up to 24% when compared to the referenced data. The angular velocity and angular acceleration had substantially larger errors of up to 47.5% and 57%, respectively. The location of the impact had a significant effect on the results: if the impact was to the side of the head, the device on that side may have an error of up to 71%, thus highlighting the importance of device location. All impacts were recorded using two separate xPatches and, in certain cases, the difference in angular velocity between the devices was 43%. In conclusion, the xPatch can be useful for identifying impacts and recording linear accelerations during front and side impacts, but the rotational velocity and acceleration data need to be interpreted with caution.

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