This dissertation focuses on taking a human-centric approach to utilize human intelligence best to inform machine learning models. More specifically, the complex relationship between the changes in movement functionality to movement quality. I designed and evaluated the Tacit Computable Empowering methodology across two domains: in-home rehabilitation and clinical assessment. My methodology has three main objectives: first, to transform tacit expert knowledge into explicit knowledge. Second, to transform explicit knowledge into a computable framework that machine learning can understand and replicate. Third, synergize human intelligence with computational machine learning to empower, not replace, the human. Finally, my methodology uses assistive interfaces to allow clinicians and machine learning models to draw parallels between movement functionality and movement quality. The results from my dissertation inform researchers and clinicians on how best to create a standardized framework to capture and assess human movement data for embodied learning scenarios / Doctor of Philosophy / Artificial intelligence (AI) is increasingly considered an important computational design material in the development of innovative products, systems, and services. Recent research emphasizes the potential for computational designers to create new tools, methods, and design processes to more adeptly handle AI and machine learning as fundamental but not exclusive materials within the design process. This talk adopts a human-centric approach to utilize human intelligence to inform machine learning models within a healthcare context. I describe the novel tacit computable empowering (TCE) methodology used and evaluated across two healthcare domains: in-home rehabilitation and clinic-based assessment. The TCE methodology comprises three main objectives: 1) to transform tacit expert knowledge into explicit knowledge; 2) to transform explicit knowledge into a computable framework that machine learning can understand and replicate and 3) to synergize human intelligence with computational machine learning to empower (and not replace) the human. This methodology uses assistive interfaces to allow clinicians and machine learning models to draw parallels between movement functionality and movement quality. Outcomes from this work inform researchers and clinicians as to how to best create a standardized framework to capture and assess human movement data for embodied learning scenarios.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/109982 |
| Date | 09 May 2022 |
| Creators | Zilevu, Kobla Setor |
| Contributors | Computer Science, Kelliher, Aisling, Rikakis, Thanassis, Anglin, Deana, Lee, Sang Won, Bowman, Douglas A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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