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Brain strain and strain rate are effective in predicting traumatic braininjury (TBI) caused by head impacts. However, state-of-the-art finite elementmodeling (FEM) demands considerable computational time in the computation,limiting its application in real-time TBI risk monitoring. To accelerate,machine learning head models (MLHMs) were developed, and the model accuracy wasfound to decrease when the training/test datasets were from different headimpacts types. However, the size of dataset for specific impact types may notbe enough for model training. To address the computational cost of FEM, thelimited strain rate prediction, and the generalizability of MLHMs to on-fielddatasets, we propose data fusion and transfer learning to develop a series ofMLHMs to predict the maximum principal strain (MPS) and maximum principalstrain rate (MPSR). We trained and tested the MLHMs on 13,623 head impacts fromsimulations, American football, mixed martial arts, car crash, and comparedagainst the models trained on only simulations or only on-field impacts. TheMLHMs developed with transfer learning are significantly more accurate inestimating MPS and MPSR than other models, with a mean absolute error (MAE)smaller than 0.03 in predicting MPS and smaller than 7 (1/s) in predicting MPSRon all impact datasets. The MLHMs can be applied to various head impact typesfor rapidly and accurately calculating brain strain and strain rate. Besidesthe clinical applications in real-time brain strain and strain rate monitoring,this model helps researchers estimate the brain strain and strain rate causedby head impacts more efficiently than FEM.

Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion