Evaluation System for Dancing Enlightenment Posture Training Using the Skeleton Tracking of Microsoft Common Objects in Context

Dance enlightenment education is of great significance to children's physical health. Therefore, we developed an evaluation system to correct the posture of the beginners for their dancing training. The pre-train network is based on the Open Neural Network Exchange. The pattern of human pose is the skeleton in the Microsoft Common Objects in Context dataset. We designed a quantitative presentation to calculate the similarity of the postures between the skeletons of the dancing beginner and target image of the training, and obtained objective evaluation indicators based on the recorded of the angle differences of limbs to calculate the score. 8 angles of the joints have been computed and presented in the evaluation system. The results show that the dancing beginner can correct her postures to approach the target image of the training. She improved the score from 94 to 96. Now, parents pay more and more attention to the quality education of their children. The AI-aided dancing training will make the beginners to learn the performances of the postures much easier in any time and any location. Therefore, the learning of dancing will become more interesting for the beginners.