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.

Depth SensorBased InHome Daily Activity Recognition and Assessment System for Stroke Rehabilitation Stroke is a leading cause of longterm adult disability. Many stroke patients participate in rehabilitation programs prescribed by an occupational therapist to aid in recovery; however, occupational therapists rely on inclinic assessments and oftenunreliable selfassessments at home to track a patientu0027s progress, limiting their ability to monitor how patients perform outside of a clinical setting. Our Daily Activity Recognition and Assessment System collects depth and skeletal data passively from within the patientu0027s home to assess longterm recovery and provide metrics to an occupational therapist to allow for more individualized rehabilitation plans. Using data from a wallmounted depth sensor, we adapt a hierarchical cooccurrence network to identify actions from presegmented skeletal data. We then perform assessments on the classified actions to track key recovery metrics: normalized jerk, speed of motions, and extent of reach. We also introduce novel filters to identify high quality data for analysis. Our sensor was installed in a stroke patientu0027s kitchen for seven days, generating the first action recognition data set from a stroke patient in a naturalistic environment. We use this data in conjunction with the NTURGBD data set to validate our recognition and assessment algorithms. We achieved 90.1% accuracy by replicating the results of the NTURGBD data set and a maximum of 59.6% accuracy on our kitchen data set.