{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/8237"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/8237","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"American Sign Language Gesture Recognition using Motion Tracking Gloves in VR","abstract":"Gesture recognition has become an topic of great interest as it continues to advance the capabilities of human computer interaction. Research has shown that related technologies have the potential to facilitate highly accessible user interfaces, enabling users with various limitations to use different applications in a more intuitive way. This thesis presents a new contribution to this research by introducing a novel approach to performing gesture recognition on American sign language (ASL) hand gestures through virtual reality (VR) using motion tracking gloves. As a proof of concept, an application was developed using this approach which is capable of recognizing 34 ASL hand gestures performed by a user as they navigate a turorial-based environment. This application was evaluated through a user study to determine the effectiveness of the approach and any possible improvements that could be made. The hope is that the approach presented in this thesis could be expanded into a number of different applications aimed at propagating the use of ASL and improving the lives of those who use it regularly.","abstract_html":"Gesture recognition has become an topic of great interest as it continues to advance the capabilities of human computer interaction. Research has shown that related technologies have the potential to facilitate highly accessible user interfaces, enabling users with various limitations to use different applications in a more intuitive way. This thesis presents a new contribution to this research by introducing a novel approach to performing gesture recognition on American sign language (ASL) hand gestures through virtual reality (VR) using motion tracking gloves. As a proof of concept, an application was developed using this approach which is capable of recognizing 34 ASL hand gestures performed by a user as they navigate a turorial-based environment. This application was evaluated through a user study to determine the effectiveness of the approach and any possible improvements that could be made. The hope is that the approach presented in this thesis could be expanded into a number of different applications aimed at propagating the use of ASL and improving the lives of those who use it regularly.","abstract_has_math":false,"creators":["Colby, Justice Steven"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Harris, Frederick C"],"committee_chairs":[],"committee_members":["Dascalu, Sergiu M","Galek, Kristine E"],"year":2022,"date_issued":"2022","date_published":"2022","updated_at":"2026-07-27T21:46:08Z","subjects":["ASL","Gesture Recognition","HCI","Machine Learning","Sign Language","VR"],"languages":[],"rights":["Creative Commons Attribution-NonCommercial-ShareAlike 4.0 United States"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/8237","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Harris, Frederick C"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Dascalu, Sergiu M","Galek, Kristine E"]},{"key":"dc:creator","label":"Author","values":["Colby, Justice Steven"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-06-28T01:24:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-28T01:24:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2022"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ASL","Gesture Recognition","HCI","Machine Learning","Sign Language","VR"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-NonCommercial-ShareAlike 4.0 United States"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/8237"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Gesture recognition has become an topic of great interest as it continues to advance the capabilities of human computer interaction. Research has shown that related technologies have the potential to facilitate highly accessible user interfaces, enabling users with various limitations to use different applications in a more intuitive way. This thesis presents a new contribution to this research by introducing a novel approach to performing gesture recognition on American sign language (ASL) hand gestures through virtual reality (VR) using motion tracking gloves. As a proof of concept, an application was developed using this approach which is capable of recognizing 34 ASL hand gestures performed by a user as they navigate a turorial-based environment. This application was evaluated through a user study to determine the effectiveness of the approach and any possible improvements that could be made. 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