Analysis of Joints for Tracking Fitness and Monitoring Progress in Physiotherapy This paper proposes a framework to track the progress in angles and range of motion of joints in physiotherapy. Using a sensor, the 3D skeletal information of a subject undergoing the therapy is extracted. Using timefrequency features of the skeletal profile based on the approximation coefficients of the Discrete Wavelet Transform (DWT), the exercise the subject is engaged in is identified by a recurrent neural network in conjunction with longandshort term memory. Subsequently, each instance of the exercise is segmented. The best of these is used as a reference and various instances of the exercise are compared against the reference for repeatability, fidelity, etc., to study muscle and or joint fatigue and progress. Finally, Joint performance analysis is carried out using metrics evaluated at the end of each engaging session. Experimental results demonstrate that with such progressive analysis, it is possible to quantify the performance through the course of the regimen.

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.