Ethnic dance movement instruction guided by artificial intelligence and 3D convolutional neural networks

This study aims to explore the potential application of artificial intelligence in ethnic dance action instruction and achieve movement recognition by utilizing the three-dimensional convolutional neural networks (3D-CNNs). In this study, the 3D-CNNs is introduced and combined with a residual network (ResNet), resulting in a proposed 3D-ResNet-based ethnic dance movement recognition model. The model operates in three stages. First, it collects data and constructs a dataset featuring movements from six specific ethnic dances, namely Miao, Dai, Tibetan, Uygur, Mongolian, and Yi. Second, 3D-ResNet is used to identify and classify these ethnic dance movements. Lastly, the model's performance is evaluated. Experiments on the self-built dataset and NTU-RGBD60 database show that the proposed 3D-ResNet-based model's accuracy is above 95%. This model performs well in movement recognition tasks, showing remarkable advantages in different dance types. It exhibits good versatility and adaptability to various cultural contexts, providing advanced technical support for ethnic dance instruction. The main contribution of this study is to identify and analyze six specific ethnic dances, verify the universality and adaptability of the proposed 3D-ResNet-based model, and offer reference and support for cross-cultural dance instruction.