HMM-based model for dance motions with pose representation

This paper presents a model for human dance motions based on hidden Markov model. The whole dance is defined as sequences of several finite distinct gestures. Dance gestures are cast as hidden discrete states and phrase of dance as a sequence of gestures. In order to map the skeleton motion data to a smaller set of features, an angular skeleton representation of the human pose is also designed, for recognition robustness under noisy input of 3D sensor. A pose of dance is defined by this angular skeleton representation which can be quantified based on range of movement for discrete hidden Markov model.