A Machine Intelligence Approach to Virtual Ballet Training


This text presents a framework for real-time analysis and visualization of ballet dance movements performed inside a Cave Virtual Reality Environment (CAVE). A Kinect sensor captures and extracts dance-based movement features, from that a topology preserved * pair of;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 make distinctive trajectories in posture space. Dependencies between postures in a very trajectory are modeled using a Markovian empirical transition matrix, that is then used to acknowledge attempted movements. This allows for quantitative assessment and feedback of a student's performance, delivered using concurrent, localized visualizations along with a performance score based mostly on incremental dynamic time warping (IDTW).

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