Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-rank representation of head impact kinematics: A data-driven
  emulator   Head motion induced by impacts has been deemed as one of the most important
measures in brain injury prediction, given that the 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, those
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, consisting of 6 degrees of freedom measurements, we found that only a
few modes, e.g. 15 in the case of angular velocity, is sufficient for accurate
reconstruction of the entire data set. Furthermore, these modes are
predominantly low frequency since over 70% to 90% of the angular velocity
response can be captured by modes that have frequencies under 40Hz. We compared
our proposed method against existing impact parametrization methods and showed
significantly better performance in injury prediction using a range of
kinematic-based metrics -- such as head injury criterion and rotational injury
criterion (RIC) -- and brain tissue deformation-metrics -- such as brain angle
metric, maximum principal strain (MPS) and axonal fiber strains (FS). In all
cases, our approach reproduced injury metrics similar to the ground truth
measurements with no significant difference, whereas the existing methods
obtained significantly different (p<0.01) values as well as poor injury
classification sensitivity and specificity. This emulator will enable us to
provide the necessary data augmentation to build a head impact kinematic data
set of any size.

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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: cs
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci