Depth camera-based virtual rehabilitation systems are gaining attention in occupational therapy for cerebral palsy patients. When developing such a system, domain-specific exercise recognition is vital. To design such a gesture recognition method, some obstacles need to be overcome: detection of gestures not related to the defined exercise set and recognition of incorrect exercises performed by the patients to compensate for their lack of ability. We propose a framework based on hidden Markov models for the recognition of upper extremity functional exercises. We determine critical compensation mistakes together with restrictions for classifying these mistakes with the help of occupational therapists. We first eliminate undefined gestures by evaluating two models that produce adaptive threshold values. Then we utilize specific negative models based on feature thresholding and train them for each exercise to detect compensation mistakes. We perform various tests using our method in a laboratory environment under the supervision of occupational therapists.

基于深度摄像头的虚拟康复系统在脑瘫患者的职业治疗中受到关注。在开发这样的系统时，特定领域的运动识别至关重要。为了设计这样的手势识别方法，需要克服一些障碍：检测与所定义的运动集无关的手势，以及识别患者为弥补其能力不足而进行的不正确的运动。我们提出了一个基于隐马尔可夫模型的框架，用于识别上肢功能锻炼。我们确定严重的赔偿错误以及在职业治疗师的帮助下对这些错误进行分类的限制。我们首先通过评估产生自适应阈值的两个模型来消除未定义的手势。然后，我们基于特征阈值使用特定的负面模型，并在每次练习中对其进行训练，以检测补偿错误。我们在职业治疗师的监督下，在实验室环境中使用我们的方法执行各种测试。