  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. In this study, we investigated the spectral
characteristics of different head impact types with kinematics classification.
Data was analyzed from 3262 head impacts from head model simulations, on-field
data from American football and mixed martial arts (MMA) using our instrumented
mouthguard, and publicly available car crash data. A random forest classifier
with spectral densities of linear acceleration and angular velocity was built
to classify different types of head impacts (e.g., football, MMA), reaching a
median accuracy of 96% over 1000 random partitions of training and test sets.
Furthermore, 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 from these devices.
The most important features in classification included both low-frequency and
high-frequency features, both linear acceleration features and angular velocity
features. It was found that 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 head impact
classification, 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 R^2-value than baseline models without
classification.
