Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.

ABSTRACT: 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 overfitting to simulated impacts and the decreasing accuracy caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose a new MLHM configuration that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on target head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based m

SUBMITTER: Zhan X 

PROVIDER: S-EPMC11781752 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature