{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/109982"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/109982","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Human Computer Interaction for Complex Machine Learning","abstract":"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","abstract_html":"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","abstract_has_math":false,"creators":["Zilevu, Kobla Setor"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Kelliher, Aisling","Rikakis, Thanassis"],"committee_members":["Anglin, Deana","Lee, Sang Won","Bowman, Douglas A."],"year":2022,"date_issued":"2022-05-09","date_published":"2022-05-09","updated_at":"2026-07-22T22:19:42Z","subjects":["Human Computer Interaction","Artificial Intelligence","Healthcare"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:33975"],"render_values":[{"text":"vt_gsexam:33975","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/109982","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Kelliher, Aisling","Rikakis, Thanassis"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Anglin, Deana","Lee, Sang Won","Bowman, Douglas A."]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Zilevu, Kobla Setor"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-05-10T08:00:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-10T08:00:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-05-09"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Human Computer Interaction","Artificial Intelligence","Healthcare"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:33975"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/109982"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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"]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Human Computer Interaction for Complex Machine Learning"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Kelliher, Aisling","Rikakis, Thanassis"],"dc:contributor.committeemember":["Anglin, Deana","Lee, Sang Won","Bowman, Douglas A."],"dc:contributor.department":["Computer Science"],"dc:creator":["Zilevu, Kobla Setor"],"dc:date.accessioned":["2022-05-10T08:00:14Z"],"dc:date.available":["2022-05-10T08:00:14Z"],"dc:date.issued":["2022-05-09"],"dc:description.abstract":["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"],"dc:description.abstractgeneral":["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."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:33975"],"dc:identifier.uri":["http://hdl.handle.net/10919/109982"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Human Computer Interaction","Artificial Intelligence","Healthcare"],"dc:title":["Human Computer Interaction for Complex Machine Learning"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:42Z"}