  Objective: Traumatic brain injury can be caused by head impacts, but many
brain injury risk estimation models are not equally accurate across the variety
of impacts that patients may undergo and the characteristics of different types
of impacts are not well studied. We investigated the spectral characteristics
of different head impact types with kinematics classification. Methods: 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, car crash,
mixed martial arts). To test the classifier robustness, another 271
lab-reconstructed impacts were obtained from 5 other instrumented mouthguards.
Finally, with the classifier, type-specific, nearest-neighbor regression models
were built for brain strain. Results: The classifier reached a median accuracy
of 96% over 1,000 random partitions of training and test sets. 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). The type-specific regression showed a generally higher
R^2-value than baseline models without classification. Conclusion: The
machine-learning-based classifier enables a better understanding of the impact
kinematics spectral density in different sports, and it can be applied to
evaluate the quality of impact-simulation systems and on-field data
augmentation.
