  Traumatic brain injury can be caused by various types of head impacts.
However, due to different kinematic characteristics, many brain injury risk
estimation models are not generalizable across the variety of impacts that
humans may sustain. The current definitions of head impact subtypes are based
on impact sources (e.g., football, traffic accident), which may not reflect the
intrinsic kinematic similarities of impacts across the impact sources. To
investigate the potential new definitions of impact subtypes based on
kinematics, 3,161 head impacts from various sources including simulation,
college football, mixed martial arts, and car racing were collected. We applied
the K-means clustering to cluster the impacts on 16 standardized temporal
features from head rotation kinematics. Then, we developed subtype-specific
ridge regression models for cumulative strain damage (using the threshold of
15%), which significantly improved the estimation accuracy compared with the
baseline method which mixed impacts from different sources and developed one
model (R^2 from 0.7 to 0.9). To investigate the effect of kinematic features,
we presented the top three critical features (maximum resultant angular
acceleration, maximum angular acceleration along the z-axis, maximum linear
acceleration along the y-axis) based on regression accuracy and used logistic
regression to find the critical points for each feature that partitioned the
subtypes. This study enables researchers to define head impact subtypes in a
data-driven manner, which leads to more generalizable brain injury risk
estimation.

Applications;Machine Learning;Quantitative Methods;Tissues and Organs