## 6 Conclusions  
This study aimed to implement an AI-based prototype capable of automatically recognizing movements in Korean traditional dance. A systematic classification framework reflecting the characteristics of traditional dance was established, and video recordings of movements were collected to create a metadata-enhanced dataset. Using the open-source BlazePose model for real-time skeletal key point detection and a customized Bi-LSTM architecture for sequential analysis, the study successfully adapted and optimized existing AI techniques to recognize five fundamental dance movements.  
Unlike previous research that focused on general pose recognition, the key contribution of this study lies in the domain-specific adaptation of existing AI tools. By combining BlazePose with a tailored metadata-enhanced dataset, this research demonstrated how ex  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Inference Time (ms)** & **FPS** \\ \hline Bi-LSTM (Proposed) & 99.35 & 0.0804 & 12,440 \\ \hline DD-Net & 100 & 0.5417 & 1846 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Performance comparison between Bi-LSTM and DD-Net.  
isting models can be effectively applied to recognize the subtle and complex movements unique to Korean traditional dance.  
The findings highlight the potential of AI for the digitalization and preservation of cultural heritage, offering a new methodology for the analysis and reinterpretation of intangible cultural heritage. The systematic creation of a metadata-enhanced dataset and the adaptation of AI models to cultural contexts mark significant progress in applying modern technology to traditional arts.  
The proposed method is specialized for Korean traditional dance movements. However, with the establishment of an appropriate dataset and classification system, it can be extended to other dance movements. Since it is based on the general-purpose pose estimation model BlazePose, it is capable of learning different types of dance movements. However, to accurately recognize other dance movements, it is necessary to newly design metadata sets and classification systems that reflect the unique characteristics and movements of each dance. Through this process, the model can learn the patterns and continuity of dance movements, making it effectively applicable to various dance styles.  
While this study achieved promising results with a prototype model, it does not involve the development of a new recognition algorithm. Instead, the research demonstrates how existing tools can be adapted and optimized for culturally specific tasks.  
Ultimately, this study lays a critical foundation for the preservation, analysis, and modern reinterpretation of Korean traditional dance. By leveraging existing technologies and tailoring them to cultural heritage, this research fosters new opportunities for the global exchange and appreciation of traditional dances in contemporary digital contexts.  
This study was conducted with the support of the 'Asian Community Heritage Culture Platform Construction Project' (2022) of the Ministry of Culture, Sports and Tourism (MCST) and Gwangju Metropolitan City (GMC).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflict of interest.