A virtual reality dance self-learning framework using Laban movement analysis

Abstract

The capabilities of general motion evaluation algorithms are significantly limited in analyzing the stylistic qualities and expressions of dance movement. This study proposes a novel dance self-learning framework on the basis of the principles of Laban movement analysis (LMA) to facilitate trainees in automatically analyzing dance movements and correcting dance techniques without an expert. First, a "shape-effort" feature description model was presented in this framework to reflect the subtleties of dance movement. The evaluation of body-shape performance was obtained via open-end dynamic time warping algorithm. Next, rhythm was qualitatively assessed by curve fitting, whereas effort was measured by using standard deviation. Finally, constructive instructions were generated in this framework on basis of the assessment scores of the movement of the trainees. The framework was implemented in cave automatic virtual environment, and its effectiveness and feasibility were verified through experiments. Results demonstrate that the feature description model with 23 LMA parameters can be used in describing dance movements. Multi-mode feedback with direct instructions for the problems in question satisfies the learning habits of the trainee. The quality of the trainees' movements achieves an average of 10% overall improvement by using the framework. Body-shape performance acquires the most improvement of 18%, followed by effort. This study provides a new research method for evaluation and training of dance movements.