Quantitative Motion Analysis of the Upper Limb: Establishment of Normative Kinematic Datasets and Systematic Comparison of Motion Analysis Systems
| dc.contributor.advisor | Kontson, Kimberly L | en_US |
| dc.contributor.advisor | White, Ian | en_US |
| dc.contributor.author | Wang, Sophie Linyi | en_US |
| dc.contributor.department | Bioengineering | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2023-02-01T06:35:13Z | |
| dc.date.available | 2023-02-01T06:35:13Z | |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | Upper limb prosthetic devices with advanced capabilities are currently in development. With these advancements brings to light the importance of objectively and quantitatively measuring effectiveness and benefit of these devices. Recently, the application of motion capture (i.e., digital tracking of upper body movements in space) to performance-based outcome measures has gained traction as a possible tool for human movement assessment that could facilitate optimal device selection, track rehabilitative progress, and inform device regulation and review. While motion capture shows promise, the clinical, regulatory, and industry communities would benefit from access to large clinical and normative datasets from different motion capture systems and a better understanding of advantages and limitations of different motion capture approaches. The first objective of this dissertation is to establish kinematic datasets of normative and upper-limb prosthesis user motion. The normative kinematic distributions of many performance-based outcome measures are not established, and it is difficult to determine departures from normative patterns without relevant clinical expertise. In Specific Aim 1, normative and clinically relevant datasets were created using a gold standard motion capture system to record participants performing standardized tasks from outcome measures. Without kinematic data, it is also difficult to identify informative kinematic features and tasks that exhibit characteristic differences from normative motion. The second objective is to identify salient kinematic characteristics associated with departures from normative motion. In Specific Aim 2, an unsupervised K-means machine learning algorithm was applied to the previously collected data to determine motions and tasks that distinguish between normative and prosthesis user movement. The third objective is to compare three commonly used motion capture systems that vary in motion tracking mechanisms. The most informative tasks and kinematic characteristics previously identified will be used to evaluate the detection of these differences for several motion capture systems with varying tracking methods in Specific Aim 3. | en_US |
| dc.identifier | https://doi.org/10.13016/i9jq-e4qu | |
| dc.identifier.uri | http://hdl.handle.net/1903/29565 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Biomechanics | en_US |
| dc.subject.pqcontrolled | Artificial intelligence | en_US |
| dc.subject.pquncontrolled | K-means | en_US |
| dc.subject.pquncontrolled | Kinematic datasets | en_US |
| dc.subject.pquncontrolled | Machine learning | en_US |
| dc.subject.pquncontrolled | Motion capture | en_US |
| dc.subject.pquncontrolled | Outcome measures | en_US |
| dc.subject.pquncontrolled | Upper-limb prostheses | en_US |
| dc.title | Quantitative Motion Analysis of the Upper Limb: Establishment of Normative Kinematic Datasets and Systematic Comparison of Motion Analysis Systems | en_US |
| dc.type | Dissertation | en_US |
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