Serebral palsi hastalığına yönelik ergoterapi egzersizlerinin tanınması

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 specific exercise recognition method is vital. In order to design a successful gesture recognition solution for this specific purpose, some obstacles needs to be overcome, namely; detection of gestures that are not related to the defined 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 first aim to eliminate the undefined gestures by designing two models that produce adaptive threshold values. Then, we utilize specific negative models based on an approach named feature thresholding and train them specifically 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.