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Found 7 papers, 0 papers with code
Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI).
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body.
Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
