An Advanced Computational Intelligence System for Training of Ballet Dance in a Cave Virtual Reality Environment

Abstract

This paper presents a computer-based system for assessment and training of ballet dance in a CAVE virtual reality environment. The system utilizes Kinect sensor to capture student's dance and extracts features from skeleton joints. This system depends on a structured posture space, which comprises a set of dance elements that represent key moments -- "postures", that typically will be so briefly held as to experience as a fleeting moment in a flux -- in the dance movements whose performance we are attempting to assess. The recording captured from the Kinect allows the parsing of dance movement into a structured posture space using the spherical self-organizing map (SSOM). From this, a unique descriptor can be obtained by following gesture trajectories through posture space on the SSOM, which appropriately reflects the subtleties of ballet dance movements. Consequently, the system can recognize the category of movement the student is attempting, and this allows us make a quantitative assessment of individual movements. Based on the experimental results, the proposed system appears to be very effective for recognition and offering generalization across instances of movement. Thus, it is possible for the construction of assessment and visualization of ballet dance movements performed by the student in an instructional, virtual reality setting.