Real-Time System for Human Activity Analysis
Randy Tan, Naimul Khan, Ling Guan · 2017
We propose a real-time human activity analysis system, where a user's activity can be quantitatively evaluated with respect to a ground truth recording. We use two Kinects to solve the problem of self-occlusion through extracting 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 quantitatively 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 experiments use a motion capture suit as ground truth to compare our dual Kinect setup to a single Kinect. We also show that with our visual feedback method, users gain a statistically significant boost to learning as opposed to watching a simple video.