Measurement and modeling of head impact kinematics
Abstract/Contents
- Abstract
- Mild traumatic brain injury was once considered a silent epidemic due to difficulties in detecting and diagnosing its subtle neurological symptoms. However, increased public awareness of mild traumatic brain injuries coupled with recent advances in wearable sensing, sideline assessments, and basic understanding of underlying mechanisms has placed a large spotlight on the dangers of brain injuries to short-term and long-term neurological health. Today, researchers are approaching a consensus that head angular rotations are particularly dangerous as it results in shearing of the brain tissue. Studies on animals, cadavers, and surrogates were instrumental in developing hypotheses linking head angular rotations with injury, while studies on human subject populations at high risk of brain injury were instrumental in confirming these hypotheses. Yet, despite all that has been discovered regarding mild traumatic brain injuries, they are still difficult to predict and prevent. This is because we lack an understanding of how impact forces cause dangerous head rotations, and we lack methods to accurately measure the head rotations that result in brain injuries. In this thesis, I will discuss my work in the development of head and neck models to understand how the head is set in motion by impact forces, and the development of wearable sensors to measure head impact rotations in the field. In the future, my head and neck models can serve to determine susceptibilities of the head to impact forces and inform improvement for preventative equipment. Furthermore, the wearable sensor technologies and algorithms I developed in my thesis can be used as accurate, real-time diagnostic tools to detect severe head impacts likely to result in brain injuries. Together, these can be both be used to reduce brain injury incidence and identify injured individuals for proper treatment and rest.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kuo, Calvin |
|---|---|
| Degree supervisor | Camarillo, David |
| Degree supervisor | Delp, Scott |
| Thesis advisor | Camarillo, David |
| Thesis advisor | Delp, Scott |
| Thesis advisor | Kuhl, Ellen, 1971- |
| Degree committee member | Kuhl, Ellen, 1971- |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Calvin Kuo. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Calvin Kuo
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...