• About
  • Policies
  • What is openaccess
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      •   BUIR Home
      • University Library
      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Recognition of occupational therapy exercises for cerebral palsy

      Thumbnail
      View / Download
      1.9 Mb
      Author
      Ongun, Mehmet Faruk
      Advisor
      Güdükbay, Uğur
      Date
      2018-09
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
      153
      views
      88
      downloads
      Abstract
      Depth camera-based virtual rehabilitation systems are gaining traction in occupational therapy for approaching patients with cerebral palsy. When developing such a system, a domain speci c exercise recognition method is vital. In order to design a successful gesture recognition solution for this speci c purpose, some obstacles needs to be overcome, namely; detection of gestures that are not related to the de ned exercise set and recognition of incorrect exercises that are performed by the patients to compensate for their lack of ability. A combination of solutions, that are based on hidden Markov models, targeting aforementioned obstacles are proposed and elaborated on. The proposed solution works for upper extremity functional exercises and critical compensation mistakes together with restrictions for classifying these mistakes are determined with the help of occupational therapists. Afterwards, we rst aim to eliminate the unde ned gestures by designing two models that produce adaptive threshold values. Then, we utilize speci c negative models based on an approach named feature thresholding and train them speci cally for each exercise to distinguish the compensation mistakes. We conducted various tests using our method in a laboratory environment under the supervision of occupational therapists and presented the results of our proposed approach.
      Keywords
      Gesture Recognition
      Cerebral Palsy
      Occupational Therapy
      Hidden Markov Model
      Exercise Recognition
      Depth Camera
      Virtual Rehabilitation
      Permalink
      http://hdl.handle.net/11693/47897
      Collections
      • Dept. of Computer Engineering - Master's degree 511
      Show full item record

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      Copyright © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy