  We propose a real-time human activity analysis system, where a user's
activity can be quantiatively evaluated with respect to a ground truth
recording. We use two Kinects to solve the ptorblem of self-occlusion through
extraction optimal joint positions using Singular Value Decomposition (SVD) and
Sequential Quadratic Programming (SQP). Incremental Dynamic Time Warping (IDTW)
is used to compare the user and expert (ground truth) to quantiatively score
the user's performance. Furthermore, the user's performance is displayed
through a visual feedback system, where colors on the skeleton represent the
user's score. Our experiements use a motion capture suit as ground truth to
compare our dual Kinect setup to a single Kinect. We also show that with out
visual feedback method, users gain statistically significant boost to learning
as opposed to watching a simple video.
