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Dance performance evaluation using hidden Markov models
Author(s) -
Laraba Sohaib,
Tilmanne Joëlle
Publication year - 2016
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1715
Subject(s) - computer science , gesture , hidden markov model , gesture recognition , dance , artificial intelligence , context (archaeology) , set (abstract data type) , adaptation (eye) , motion capture , machine learning , markov model , motion (physics) , speech recognition , computer vision , markov chain , programming language , art , paleontology , physics , literature , optics , biology
We present in this paper a hidden Markov model‐based system for real‐time gesture recognition and performance evaluation. The system decodes performed gestures and outputs at the end of a recognized gesture, a likelihood value that is transformed into a score. This score is used to evaluate a performance comparing to a reference one. For the learning procedure, a set of relational features has been extracted from high‐precision motion capture system and used to train hidden Markov models. At runtime, a low‐cost sensor (Microsoft Kinect) is used to capture a learner's movements. An intermediate step of model adaptation was hence requested to allow recognizing gestures captured by this low‐cost sensor. We present one application of this gesture evaluation system in the context of traditional dance basics learning. The estimation of the log‐likelihood allows giving a feedback to the learner as a score related to his performance. Copyright © 2016 John Wiley & Sons, Ltd.