  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 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 was
found to decrease when the training/test datasets were from different head
impacts types. However, the size of dataset for specific impact types may not
be enough for model training. 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). We trained and tested the MLHMs on 13,623 head impacts from
simulations, American football, mixed martial arts, car crash, and compared
against the models trained on only simulations or only on-field impacts. The
MLHMs developed with transfer learning are significantly more accurate in
estimating 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 MPSR
on all impact datasets. The MLHMs can be applied to various head impact types
for rapidly and accurately calculating brain strain and strain rate. Besides
the clinical applications in real-time brain strain and strain rate monitoring,
this model helps researchers estimate the brain strain and strain rate caused
by head impacts more efficiently than FEM.
