Evaluation Of Physical Therapy Through Analysis Of Depth Images The support through robots in orthopaedic rehabilitation is an opportunity to relieve physiotherapists. However, to be able to provide a control in the robotpatient cooperation in the therapy process a certain standard in interpreting the exercise has to be established. In this paper we present an evaluation approach of the health subject performance in a tibiofemoral rehabilitation on the example of squat exercises. The proposed method utilizes only depth images for the performance evaluation and any humanrobot interaction system for the performance correction. Thus, this method can be easily applied to a mobile service robot in the robotaided physical therapy. The patient is observed while performing the exercise and the motion is evaluated and segmented using Motion History Images. Concrete, depth images are used to monitor local points of interest on the performer during the exercise. The proposed approach was evaluated on custom image sequences with a multitude of varying subjects and shows the suitable performance for assisting in the correctness of the exercise execution.

RealTime Rehabilitation and Fitness System using Depth Sensor This paper presents an action verification system for rehabilitation of patients with limited mobility. In this regard, a kinect sensor is used to capture the imagesequences of patients for identification of different actions in real time during rehabilitation exercisers at home. Then, a set of most discriminating features is extracted from a human skeleton. These features are fed to the classifier for action recognition. In order to evaluate the performance of the proposed system, we build a data set for patients with frozen shoulders and Overhead shoulder issues. The results confirm an excellent performance of the proposed system. In this paper, our contribution is three fold. 1) we have constructed a data set of overhead shoulder exercise performed by 8 different actors. 2) A novel set of features has been proposed for verification of these two exercises in real time. 3) we have employed 5 different classifiers for classification and presented a comparative analysis.