Real-Time System for Human Activity Analysis
This work addresses the problem of real-time activity analysis for users needing performance feedback, though it is incremental as it builds on existing methods like SVD and DTW.
The authors tackled real-time human activity analysis by developing a system that quantitatively evaluates user performance against ground truth recordings, using dual Kinects to reduce self-occlusion and visual feedback to enhance learning, with experiments showing a statistically significant boost in user learning compared to watching a simple video.
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.