Predicting Neurological Effects Associated with Traumatic Brain Injuries Using Video Analysis and Finite Element Modeling

Bianca Acot, Branko Glisic, Annegret Dettwiler, Michael D. Gilchrist

Research output: Chapter in Book/Report/Conference proceedingChapter


The topic of concussion has become controversial due to the recent discoveries of long-term neurodegenerative diseases in former football players related to concussions they sustained earlier in life. In order to prevent concussion, the mechanism of concussive head impacts in football must be understood. However, studying concussion is difficult because of the ethical issues related to studies involving living subjects. Finite element simulations enable researchers to study the relation between predicted injury in the brain and head impact mechanics as surrogates to live subjects. To improve the accuracy of these simulations, this paper developed a methodology to cyclically analyze and improve the process of finite element modelling of head impacts. Case studies of two college football players were analyzed through the following steps: video analysis of representative concussive hits to elucidate head impact mechanics, finite element simulation of head impact to garner biomechanical metrics in the brain, and correlation of biomechanical metrics with neuroimaging metrics. A relationship was found between these metrics while areas of improvement within the accident reconstruction and finite element simulation process were also found. Through acknowledging areas of further work, researchers can continue to develop the process of head impact simulations to eventually use this tool for diagnostic purposes.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computational Vision and Biomechanics
PublisherSpringer Science and Business Media B.V.
Number of pages11
StatePublished - 2023

Publication series

NameLecture Notes in Computational Vision and Biomechanics
ISSN (Print)2212-9391
ISSN (Electronic)2212-9413

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


  • Accident Reconstruction
  • Concussion
  • Diffusion Tensor Imaging
  • Finite element modeling


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