Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

ABSTRACT: Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting

SUBMITTER: Arrue P 

PROVIDER: S-EPMC7546353 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature