AR Dance Learning App with a Feedback Feature Through Pose Estimation: DancÆR

In recent years, educational philosophies have slowly begun shifting to focus on differentiated and self-learning systems. In this regard, creating opportunities to further self-learning resources has become increasingly important. 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 AR app using the Swift programming language and used the Core ML action classification model to capture the body position. We also recognize the importance of social interaction in learning, such as its benefits to motivation or peer-to-peer support. As a result, we designed the app to allow its users to connect with their friends globally and dance together. We do this by converting the captured poses of various users from different locations during a live session and instantaneously converting them into AR avatars. This way, the users can dance with their friends live, emphasizing the undeniably social component of dance and learning while going through the learning process at their own pace.