This thesis presents a real-time human activity analysis system, where a user’s activity can be quantitatively evaluated with respect to a ground truth recording. Multiple Kinects are used to solve the problem of self-occlusion while performing an activity. The Kinects are placed in locations with different perspectives to extract the optimal joint positions of a user using Singular Value Decomposition (SVD) and Sequential Quadratic Programming (SQP). The extracted joint positions are then fed through our Incremental Dynamic Time Warping (IDTW) algorithm so that an incomplete sequence of an user can be optimally compared against the complete sequence from an expert (ground truth). Furthermore, the user’s performance is communicated through a novel visual feedback system, where colors on the skeleton present the user’s level of performance. Experimental results demonstrate the impact of our system, where through elaborate user testing we show that our IDTW algorithm combined with visual feedback improves the user’s performance quantitatively.