Ongun, Mehmet Faruk2018-09-212018-09-212018-092018-092018-09-20http://hdl.handle.net/11693/47897Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.Includes bibliographical references (leaves 54-59).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.xii, 59 leaves : illustrations, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessGesture RecognitionCerebral PalsyOccupational TherapyHidden Markov ModelExercise RecognitionDepth CameraVirtual RehabilitationThesisB159021