Development of a Metric for Predicting Brain Strain Responses
Using Head Kinematics
, and M
Department of Mechanical and Aerospace Engineering, University of Virginia, Center for Applied Biomechanics, 4040 Lewis and
Clark Drive, Charlottesville, VA 22911, USA
(Received 2 October 2017; accepted 22 March 2018; published online 28 March 2018)
Associate Editor Stefan M Duma oversaw the review of this article.
Abstract—Diffuse brain injuries are caused by excessive
brain deformation generated primarily by rapid rotational
head motion. Metrics that describe the severity of brain
injury based on head motion often do not represent the
governing physics of brain deformation, rendering them
ineffective over a broad range of head impact conditions.
This study develops a brain injury metric based on the
response of a second-order mechanical system, and relates
rotational head kinematics to strain-based brain injury
metrics: maximum principal strain (MPS) and cumulative
strain damage measure (CSDM). This new metric, universal
brain injury criterion (UBrIC), is applicable over a broad
range of kinematics encountered in automotive crash and
sports. Efﬁcacy of UBrIC was demonstrated by comparing it
to MPS and CSDM predicted in 1600 head impacts using
two different ﬁnite element (FE) brain models. Relative to
existing metrics, UBrIC had the highest correlation with the
FE models, and performed better in most impact conditions.
While UBrIC provides a reliable measurement for brain
injury assessment in a broad range of head impact condi-
tions, and can inform helmet and countermeasure design, an
injury risk function was not incorporated into its current
formulation until validated strain-based risk functions can be
developed and veriﬁed against human injury data.
Keywords—Brain deformation, Finite element modeling,
Rotational, Second-order system.
Each year in the United States (US), traumatic brain
injury (TBI) is a primary or secondary diagnosis in 16%
of injury-related hospitalizations, and contributes to
nearly one-third of injury-related deaths.
estimates vary, falls remain the leading cause of TBI
While MVCs are the third highest source of
TBI (14%), they contribute to the largest number of
TBI-related deaths among people aged (5–24) years.
These ﬁgures only include civilian estimates, and do not
account for those receiving care at a federal facility (e.g.,
military personnel) nor do they include sport-related
concussions, which are vastly underreported.
head impacts have been identiﬁed as the largest cause of
Thus, reliable TBI risk assess-
ment models are needed to inform the design of safety
systems that are effective at protecting against this
mechanism of brain injury during head impact.
TBI risk assessments are made using criteria which
consist of a biomechanical metric and an injury risk
function. The metric summarizes head impact severity,
and is a mathematical function of one or more biome-
chanical response variables. Metrics used for brain injury
criteria can be categorized into two types: kinematic-
based and tissue-level-based. Kinematic metrics are based
on rigid-body motion parameters of the head, while tis-
sue-level metrics are based on mechanics of the par-
enchyma. The risk function is a probabilistic model that
relates the metric to brain injury likelihood. While the risk
function is necessary for brain injury prediction, the
underlying metric is responsible for representing the in-
jury mechanism and relative severity.
Head impact kinematics have been the basis for most
head injury metrics. This is likely due to the feasibility of
measuring and summarizing head kinematic response,
either on a dummy or a volunteer, relative to measuring
brain tissue response. Head kinematics can be separated
into two diﬀerent types of motion: translational and
rotational. Early metrics were formulated using only
translational parameters of head motion, and existing
safety standards used in helmet and crash testing have
been based on linear head acceleration.
Address correspondence to Matthew B. Panzer, Department of
Mechanical and Aerospace Engineering, University of Virginia, Cen-
ter for Applied Biomechanics, 4040 Lewis and Clark Drive, Char-
lottesville, VA 22911, USA. Electronic mail: email@example.com
Annals of Biomedical Engineering, Vol. 46, No. 7, July 2018 (
2018) pp. 972–985
2018 Biomedical Engineering Society