Head impact research using inertial sensors in sport: a systematic review of methods, demographics, and factors contributing to exposure
Background The number and magnitude of head impacts have been assessed in-vivo using
inertial sensors to characterise the exposure in various sports and to help understand their …
inertial sensors to characterise the exposure in various sports and to help understand their …
Prevalence and mechanisms of injuries in water polo: a systematic review
F Croteau, H Brown, D Pearsall… - BMJ Open Sport & …, 2021 - bmjopensem.bmj.com
Objective To summarise the information available in the literature on the prevalence of
injuries in water polo and injury risk factors. Methods Protocol was registered on Open …
injuries in water polo and injury risk factors. Methods Protocol was registered on Open …
Translational models of mild traumatic brain injury tissue biomechanics
Traumatic brain injury (TBI) is a global health concern. Mild TBI (mTBI) which accounts for
the majority of TBI cases, is hard to detect since often the imaging is normal but can still …
the majority of TBI cases, is hard to detect since often the imaging is normal but can still …
Brain deformation estimation with transfer learning for head impact datasets across impact types
Objective: The machine-learning head model (MLHM) to accelerate the calculation of brain
strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model …
strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model …
Salivary S100 calcium-binding protein beta (S100B) and neurofilament light (NfL) after acute exposure to repeated head impacts in collegiate water polo players
Blood-based biomarkers of brain injury may be useful for monitoring brain health in athletes
at risk for concussions. Two putative biomarkers of sport-related concussion, neurofilament …
at risk for concussions. Two putative biomarkers of sport-related concussion, neurofilament …
Adaptive machine learning head model across different head impact types using unsupervised domain adaptation and generative adversarial networks
Machine learning head models (MLHMs) are developed to estimate brain deformation from
sensor-based kinematics for early detection of traumatic brain injury (TBI). However, the …
sensor-based kinematics for early detection of traumatic brain injury (TBI). However, the …
Finding the spatial co-variation of brain deformation with principal component analysis
Objective: Strain and strain rate are effective traumatic brain injury metrics. In finite element
(FE) head model, thousands of elements were used to represent the spatial distribution of …
(FE) head model, thousands of elements were used to represent the spatial distribution of …
Injuries affecting intercollegiate water polo athletes: a descriptive epidemiologic study
GG Schroeder, DJ McClintick… - … journal of sports …, 2022 - journals.sagepub.com
Background: There are few data on injuries suffered by collegiate water polo athletes.
Purpose: To describe the epidemiology of injuries suffered by National College Athletic …
Purpose: To describe the epidemiology of injuries suffered by National College Athletic …
[HTML][HTML] One season of head-to-ball impact exposure alters functional connectivity in a central autonomic network
Repetitive head impacts represent a risk factor for neurological impairment in team-sport
athletes. In the absence of symptoms, a physiological basis for acute injury has not been …
athletes. In the absence of symptoms, a physiological basis for acute injury has not been …
Piecewise multivariate linearity between kinematic features and cumulative strain damage measure (csdm) across different types of head impacts
In a previous study, we found that the relationship between brain strain and kinematic
features cannot be described by a generalized linear model across different types of head …
features cannot be described by a generalized linear model across different types of head …