Developing an instrumented mouthguard to study human mild traumatic brain injury
- Mild traumatic brain injury has been recognized as a silent epidemic, with seemingly transient acute symptoms but potential long-term irreversible brain changes. In more recent years, with increasing awareness of the dangers of concussive and subconcussive trauma, the condition is becoming less and less silent, but still remains invisible. It is known that external forces on the head cause injury, yet their exact effects on the brain are unknown. Although animal studies have helped uncover potential biomechanical mechanisms of injury, there is a lack of human data to confirm any hypotheses. In the current thesis work, an instrumented mouthguard was developed to optimize for kinematic accuracy and impact detection accuracy for the collection of high quality human data in contact sports. It was found that a mouthguard form factor provided tighter skull-coupling than soft tissue-mounted or headgear-mounted sensors. In addition, we identified sensor bandwidth requirements to ensure head impact kinematics are sufficiently captured for computing injury risk criteria. We also designed and validated a head impact detection system in both laboratory and field scenarios to accurately detect helmet contacts from other field activities. Applying the instrumented mouthguard in field studies helped to provide the first evidence of directional dependence of injury from human data, and linked subconcussive head impact exposure with neurological deficit. In the near future, human data gathered using the instrumented mouthguard can help better understand the mechanisms of mild traumatic brain injury, enable on-field real-time injury screening, and aid in the design of better preventative equipment.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Bioengineering.
|Fan, Jonathan Albert
|Fan, Jonathan Albert
|Statement of responsibility
|Lyndia Chun Wu.
|Submitted to the Department of Bioengineering.
|Thesis (Ph.D.)--Stanford University, 2017.
- © 2017 by Chun Wu
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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