245 citations · 759 across the 24 of their papers we have counts for
9 papers · 1 filter
Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi +4
Strain and strain rate are effective traumatic brain injury predictors. Kinematics-based models estimating these metrics suffer from significant different distributions of both kin…
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi +4
Brain strain and strain rate are effective in predicting traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands consid…
Physics-informed machine learning improves detection of head impacts
Samuel J. Raymond, Nicholas J. Cecchi, Hossein Vahid Alizadeh +6
In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Mon…
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
Xianghao Zhan, Yiheng Li, Yuzhe Liu +5
Traumatic brain injury can be caused by various types of head impacts. However, due to different kinematic characteristics, many brain injury risk estimation models are not general…
A comparison of sports-related head accelerations with and without direct head impacts
Samuel J. Raymond, Yuzhe Liu, Nicholas J. Cecchi +9
Concussion and repeated exposure to mild traumatic brain injury are risks for athletes in many sports. While direct head impacts are analyzed to improve the detection and awareness…
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems
Samuel J. Raymond, David B. Camarillo
Physics-Informed Machine Learning (PIML) has gained momentum in the last 5 years with scientists and researchers aiming to utilize the benefits afforded by advances in machine lear…
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
Xianghao Zhan, Yiheng Li, Yuzhe Liu +11
Objective: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients m…
Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football
Yuzhe Liu, August G. Domel, Nicholas J. Cecchi +8
Wearable devices have been shown to effectively measure the head movement during impacts in sports like American football. When a head impact occurs, the device is triggered to col…
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
Xianghao Zhan, Yiheng Li, Yuzhe Liu +12
Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of…