DancÆR: Efficient and Accurate Dance Choreography Learning by Feedback Through Pose Classification

Abstract

Educational philosophies have slowly focused on differentiated and self-learning systems that respectively emphasize tailoring instruction to meet individual needs and gathering, processing, and retaining knowledge without the help of another person in recent years. In this regard, creating opportunities to further self-learning resources has become increasingly important. Some people started to prefer these over traditional learning practices for various reasons, such as the difficulty of transportation in metropolises or fitting their timetables to in-person lessons. The creation of such platforms or opportunities for physical education, however, proves to be more difficult as individuals require continuous and precise feedback regarding the usage of their bodies. Accordingly, we have developed an augmented reality application that presents a platform for dance that focuses on differentiated and self-learning principles with accurate feedback. We built the augmented reality (AR) app prototype using the Swift programming language and used the MoveNet pose detection model along with our own neural network to capture the body position. Our proposal could prove a valuable addition to learning physical activities assisted by AI systems since the application of AI technologies to dance and physical education could be improved by further investigation and research.