Abstract
dc:description.abstractThis 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
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy
- Level thesis:degree_level
- doctoral
- Discipline thesis:degree_discipline
- Computer Science and Applications
- Department dc:contributor.department
- Computer Science
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2022
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Zilevu, Kobla Setor
- Chairs dc:contributor.committeechair
-
- Kelliher, Aisling
- Rikakis, Thanassis
- Committee members dc:contributor.committeemember
-
- Anglin, Deana
- Lee, Sang Won
- Bowman, Douglas A.
Subjects
dc:subject × 3Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:33975
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/109982