Musculoskeletal simulation and optimization for predicting human movement
Abstract/Contents
- Abstract
- Human movement requires complex coordination between the muscular, skeletal, and neural systems. When these systems are impaired, gait pathologies can occur. Previous research has studied how neuromuscular and skeletal deficits result in abnormal, inefficient, gait patterns. However, while these studies have suggested relationships between musculoskeletal parameters and observed gait, they have been limited in understanding the cause-effect relationship between these variables. Other previous work has focused on developing devices to augment human performance, both for individuals with and without impairments, but results have been mixed, likely due to the complex human dynamics and human-device interactions. This thesis describes two musculoskeletal simulation and optimization frameworks that were developed to explore if simulations can be used to 1) probe the cause-effect relationship between muscle deficits and commonly observed pathological gait patterns and 2) help design assistive devices. For each study, the frameworks generated predictive simulations, or simulations in which movement trajectories were created without tracking any experimental data. In the first study, the framework could generate realistic simulations of walking. Plantarflexor muscle weakness or contracture, commonly observed in individuals with stroke or cerebral palsy, was then added to the model, and the model adopted gait patterns that are seen in pathologic gait. In the second study, the framework could generate realistic simulations of a standing long jump. Potential active and passive assistive devices were then added to the model, and the framework tuned the devices to increase jump distance. This work shows how simulation frameworks can be used to predict how movement would change under various conditions, leading to a deeper understanding of mechanisms behind gait pathologies and a framework to aid in designing devices to augment human performance.
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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ong, Carmichael Filbert |
|---|---|
| Degree supervisor | Delp, Scott |
| Thesis advisor | Delp, Scott |
| Thesis advisor | Mitiguy, Paul |
| Thesis advisor | Okamura, Allison |
| Degree committee member | Mitiguy, Paul |
| Degree committee member | Okamura, Allison |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Carmichael F. Ong. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Carmichael Filbert Ong
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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