{"id":{"repo_id":"ohiolink","oai_identifier":"oai:etd.ohiolink.edu:case1355349305"},"canonical_url":"https://search.dev.ndltd.org/etd/ohiolink/oai:etd.ohiolink.edu:case1355349305","repository":{"repo_id":"ohiolink","name":"OhioLINK","base_url":"https://etd.ohiolink.edu/acprod/odb_etd/ws/oai/oai"},"display":{"title":"USING THE XBOX KINECT TO DETECT FEATURES OF THE FLOOR SURFACE","abstract":"This thesis examines the effectiveness of the Xbox Kinect as a sensor to identify features of the navigation surface in front of a smart wheelchair robot. Recent advances in mobile robotics have brought about the development of smart wheelchairs to assist disabled people, allowing them to be more independent. Because these robots have a human occupant, safety is the highest priority, and the robot must be able to detect hazards like holes, stairs, or obstacles. Furthermore, to ensure safe navigation, wheelchairs often need to locate and navigate on ramps. The results demonstrate how data from the Kinect can be processed to effectively identify these features, increasing occupant safety and allowing for a smoother ride.","abstract_html":"This thesis examines the effectiveness of the Xbox Kinect as a sensor to identify features of the navigation surface in front of a smart wheelchair robot. Recent advances in mobile robotics have brought about the development of smart wheelchairs to assist disabled people, allowing them to be more independent. Because these robots have a human occupant, safety is the highest priority, and the robot must be able to detect hazards like holes, stairs, or obstacles. Furthermore, to ensure safe navigation, wheelchairs often need to locate and navigate on ramps. The results demonstrate how data from the Kinect can be processed to effectively identify these features, increasing occupant safety and allowing for a smoother ride.","abstract_has_math":false,"creators":["Cockrell, Stephanie"],"institution":"Case Western Reserve University School of Graduate Studies","degree_name":"Master of Sciences (Engineering)","degree_level":"masters","degree_discipline":"EECS - Electrical Engineering","degree_department":null,"school":null,"contributors":["Lee, Greg"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08-16","date_published":"2013-08-16","updated_at":"2026-07-24T03:35:52Z","subjects":["Electrical Engineering","Robotics","Kinect","obstacle detection","drivable surfaces","ramps","smart wheelchairs","mobile robots"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=case1355349305","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lee, Greg"]},{"key":"dc:creator","label":"Author","values":["Cockrell, Stephanie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-08-16"]},{"key":"dc:publisher","label":"Institution","values":["Case Western Reserve University School of Graduate Studies / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["EECS - Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Sciences (Engineering)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Case Western Reserve University School of Graduate Studies"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering","Robotics","Kinect","obstacle detection","drivable surfaces","ramps","smart wheelchairs","mobile robots"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=case1355349305"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis examines the effectiveness of the Xbox Kinect as a sensor to identify features of the navigation surface in front of a smart wheelchair robot. Recent advances in mobile robotics have brought about the development of smart wheelchairs to assist disabled people, allowing them to be more independent. Because these robots have a human occupant, safety is the highest priority, and the robot must be able to detect hazards like holes, stairs, or obstacles. Furthermore, to ensure safe navigation, wheelchairs often need to locate and navigate on ramps. The results demonstrate how data from the Kinect can be processed to effectively identify these features, increasing occupant safety and allowing for a smoother ride."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.96","2.76 MB"]},{"key":"dc:title","label":"Title","values":["USING THE XBOX KINECT TO DETECT FEATURES OF THE FLOOR SURFACE"]}]}],"canonical_facts":{"dc:contributor":["Lee, Greg"],"dc:creator":["Cockrell, Stephanie"],"dc:date":["2013-08-16"],"dc:description":["This thesis examines the effectiveness of the Xbox Kinect as a sensor to identify features of the navigation surface in front of a smart wheelchair robot. Recent advances in mobile robotics have brought about the development of smart wheelchairs to assist disabled people, allowing them to be more independent. Because these robots have a human occupant, safety is the highest priority, and the robot must be able to detect hazards like holes, stairs, or obstacles. Furthermore, to ensure safe navigation, wheelchairs often need to locate and navigate on ramps. The results demonstrate how data from the Kinect can be processed to effectively identify these features, increasing occupant safety and allowing for a smoother ride."],"dc:format":["application/pdf","p.96","2.76 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=case1355349305"],"dc:language":["English"],"dc:publisher":["Case Western Reserve University School of Graduate Studies / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Electrical Engineering","Robotics","Kinect","obstacle detection","drivable surfaces","ramps","smart wheelchairs","mobile robots"],"dc:title":["USING THE XBOX KINECT TO DETECT FEATURES OF THE FLOOR SURFACE"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["EECS - Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Sciences (Engineering)"],"thesis:institution_name":["Case Western Reserve University School of Graduate Studies"]},"updated_at":"2026-07-24T03:35:52Z"}