Training for Physical Tasks in Virtual Environments: Tai Chi
VR '03 Proceedings of the IEEE Virtual Reality 2003
Real-time classification of dance gestures from skeleton animation
SCA '11 Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Evaluating a dancer's performance using kinect-based skeleton tracking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Automatic imitation assessment in interaction
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Real-Time Gesture Recognition from Depth Data through Key Poses Learning and Decision Forests
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
International Journal of Computer Vision
Motionlets: Mid-level 3D Parts for Human Motion Recognition
CVPR '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition
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In this paper, we develop a graph-based method to align two dynamic skeleton sequences, and apply it to both action recognition tasks as well as to the objective quantification of the goodness of the action performance. The automated measurement of "action quality" has potential to be used to monitor action imitations, for example, during a physical therapy. We seek matches between a query sequence and model sequences selected with graph mining. The best matches are obtained through minimizing an energy function that jointly measures space and time domain deformations. This measure has been used for recognizing actions, for separating acceptable and unacceptable action performances, or as a continuous quantification of the action performance goodness. Experimental evaluation demonstrates the improved results of our scheme vis-à-vis its nearest competitors. Furthermore, a plausible relationship has been obtained between action perturbation, given by the joint noise variances, and quality measure, given by matching energies averaged over a sequence.