A Machine Intelligence Approach to Virtual Ballet Training

This article presents a framework for real-time analysis and visualization of ballet dance movements performed within a Cave Virtual Reality Environment (CAVE). A Kinect sensor captures and extracts dance-based movement features, from which a topology preserved 'posture space" is constructed using a spherical self-organizing map (SSOM). Recordings of dance movements are parsed into gestural elements by projection onto the SSOM to form unique trajectories in posture space. Dependencies between postures in a trajectory are modeled using a Markovian empirical transition matrix, which is then used to recognize attempted movements. This allows for quantitative assessment and feedback of a student's performance, delivered using concurrent, localized visualizations together with a performance score based on incremental dynamic time warping (IDTW).