Dance performance evaluation using hidden Markov models

AbstractWe 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.