Physics-Informed Machine Learning Improves Detection of Head Impacts
Author:
Funder
National Institutes of Health
PAC-12 Conference’s Student-Athlete Health and Well-Being Initiative
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s10439-022-02911-6.pdf
Reference45 articles.
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2. Beckwith, J. G., R. M. Greenwald, J. J. Chu, et al. Head impact exposure sustained by football players on days of diagnosed concussion. Medicine and Science in Sports and Exercise. 2013. https://doi.org/10.1249/MSS.0b013e3182792ed7
3. Camarillo, D. B., P. B. Shull, J. Mattson, R. Shultz, and D. Garza. An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Annals of Biomedical Engineering. 2013. https://doi.org/10.1007/s10439-013-0801-y
4. Cecchi, N. J., A. G. Domel, Y. Liu, et al. Identifying factors associated with head impact kinematics and brain strain in high school American football via instrumented mouthguards. Annals of Biomedical Engineering. 2021. https://doi.org/10.1007/s10439-021-02853-5
5. Collins, D., S. Raymond, Y. Ai, J. Willams, R. O’Rorke, and M. Tayebi. Acoustic field design in microfluidic geometries via Huygens-Fresnel diffraction and deep neural networks. The Journal of the Acoustical Society of America. 148(4):2707, 2020.
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