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2016-05-03 - Article/Dans un journal avec peer-review - Anglais - 9 page(s)

Laraba Sohaib , Tilmanne Joëlle , "Dance performance evaluation using hidden Markov models" in Computer Animation and Virtual Worlds

  • Edition : John Wiley & Sons, Hoboken (NJ)
  • Codes CREF : Sciences de l'ingénieur (DI2000), Intelligence artificielle (DI1180), Informatique mathématique (DI1160)
  • Unités de recherche UMONS : Information, Signal et Intelligence artificielle (F105)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech), Institut NUMEDIART pour les Technologies des Arts Numériques (Numédiart)
Texte intégral :

Abstract(s) :

(Anglais) 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.