Zhen Ni, Keun Park Jae
Dance department, Sangmyung University, Seoul, South Korea.
Sci Rep. 2025 May 15;15(1):16856. doi: 10.1038/s41598-025-01879-2.
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.
本研究旨在探索人工智能在民族舞蹈动作教学中的潜在应用，并利用三维卷积神经网络（3D-CNN）实现动作识别。在本研究中，引入了3D-CNN并将其与残差网络（ResNet）相结合，从而提出了一种基于3D-ResNet的民族舞蹈动作识别模型。该模型分三个阶段运行。首先，它收集数据并构建一个包含来自苗族、傣族、藏族、维吾尔族、蒙古族和彝族六种特定民族舞蹈动作的数据集。其次，使用3D-ResNet对这些民族舞蹈动作进行识别和分类。最后，评估该模型的性能。在自建数据集和NTU-RGBD60数据库上进行的实验表明，所提出的基于3D-ResNet的模型的准确率在95%以上。该模型在动作识别任务中表现良好，在不同舞蹈类型中显示出显著优势。它对各种文化背景具有良好的通用性和适应性，为民族舞蹈教学提供了先进的技术支持。本研究的主要贡献在于识别和分析六种特定民族舞蹈，验证所提出的基于3D-ResNet的模型的通用性和适应性，并为跨文化舞蹈教学提供参考和支持。
