Simulation and statistical tools for clinical biomechanics
Abstract/Contents
- Abstract
- Healthy gait requires complex coordination between the neuromuscular and skeletal systems. In patients with movement disorders such as cerebral palsy, these coordination patterns may be disrupted and their walking is inefficient and unstable. Understanding the interactions between the neuromuscular and skeletal systems is necessary for designing treatments to improve gait for individuals with impaired walking. This dissertation describes two classes of tools—physics-based dynamic simulation and statistical modeling—for understanding and improving human gait. First, we developed and validated a computer model of the full body that can be used to generate dynamic simulations of gait. We demonstrated how computer simulations of gait can be used to study muscle coordination in walking and running, as well as provide clinically-relevant metrics that can be used to make treatment recommendations. Second, we developed and tested statistical models that used biomechanical and mathematical features extracted from clinical gait analysis data to predict surgical outcomes in children with cerebral palsy. These statistical models were able to identify patients that are likely to have clinically-meaningful short-term and long-term improvements in gait following orthopedic surgery. This work demonstrates the utility of combining biomechanical modeling and statistical learning to improve clinical care for patients with gait pathology. Our musculoskeletal and statistical models are shared openly with the research and clinical community to allow others to adopt and build upon our work.
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 | Rajagopal, Apoorva | |
|---|---|---|
| Degree supervisor | Delp, Scott | |
| Thesis advisor | Delp, Scott | |
| Thesis advisor | Boyd, Stephen P | |
| Thesis advisor | Mitiguy, Paul | |
| Degree committee member | Boyd, Stephen P | |
| Degree committee member | Mitiguy, Paul | |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Apoorva Rajagopal. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Apoorva Rajagopal
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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