Head acceleration measurement sensors are now widely deployed in the field to monitor
head kinematic exposure in contact sports. The wealth of impact kinematics data provides …

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of
high dimension and resolution. In this study, we establish a transformer neural network …

Brain strain is increasingly being used in helmet design and safety performance evaluation
as it is generally considered as the primary mechanism of concussion. In this study, we …

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 …

The brain injury modeling community has recommended improving model subject specificity
and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural …

Abstract Background and Objectives Driven by the risk of repetitive head trauma, sensors
have been integrated into mouthguards to measure head impacts in contact sports and …

Impact-induced brain strains are spatially rich and intrinsically dynamic. However, the
dynamic information of brain strain is not typically used in any injury investigation. Here, we …

The large amount of training samples required to develop a deep learning brain injury
model demands enormous computational resources. Here, we study how a transformer …

Objective: Impact kinematics are widely employed to investigate mechanisms of traumatic
brain injury (TBI). However, they are susceptible to noise and artefacts; thus, require data …

Despite decades of research into neurotrauma prevention and treatment, the underlying
mechanisms responsible for Traumatic Brain Injury (TBI) are not well understood. This is a …