## 4 Result  
Summarizing the entire development process in Figure 11, the trained dataset consists of quantified values of joint coordinates and angles for each traditional dance movement. These were used to create a traditional dance classification-based labeled dataset, which compares real-time input data along the time axis to predict the most similar training data for specific movements. A new AI prototype model specialized in traditional dance pose recognition was developed. By integrating BlazePose with the unique traditional dance classification system and templates, it became possible to create an executable file specialized in recognizing traditional dance movements. The executable file uses BlazePose as the foundational technology for extracting and processing data, and it is fused with the Traditional Dance PoseNetmodel, a dataset of traditional dance movements categorized by the classification system. This integration resulted in the creation of a new AI model specialized in recognizing traditional dance \begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Description** & **Key Roles/Actions** & **Related Libraries/Modules** \\ \hline \multirow{4}{*}{Feature Calculation} & \multirow{4}{*}{Calculate relative vectors and angles using landmark data} & - Compute relative vectors (v) & \multirow{4}{*}{numpy} \\  & & between landmarks. & \\  & & -Calculate angles between vectors (using & \\  & & np.arcosomal np.einsum). & \\  & & -Generate feature vectors including angles & \\  & & (in radians). & \\ \hline \multirow{2}{*}{Preparing LSTM Input} & Store specific vector sequences & - Create sequences composed of 40 frames. & \multirow{2}{*}{tensorflow.keras.models} \\  & (seq) and input them to the LSTM model & - Adjust input dimensions for the model & \\  & & using np.expand\_dims. & \\ \hline \multirow{2}{*}{Comparison Analysis} & Compare BlazePose results with traditional classifications and templates & - Use a Bi-LSTM model to analyze real-time & \\  & & - traditional classifications & data sequences and compare them with & train.ipynb \\ \hline \multirow{4}{*}{Motion Prediction} & \multirow{4}{*}{Predict motion classes for input sequences using the LSTM model} & - Identify the class with the highest & \multirow{4}{*}{tensorflow} \\  & & probability from model output (y\_pred). & \\ \cline{1-1}  & & - Exclude results with low confidence based & \\ \cline{1-1}  & & on the confidence threshold. & \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont_. Figure 11: Development process of a traditional dance-specialized AI prototype. movements. By applying traditional dance pose labels to the general-purpose pose estimation AI model BlazePose, the new model was termed a 'Traditional Dance Specialized AI Learning Model'. Unlike the original BlazePose, this model was trained specifically on the postures and movements of Korean traditional dance. Therefore, referring to it as a 'new traditional dance specialized AI learning model' signifies that this model has been newly defined beyond simple pose estimation to recognize traditional dance. While BlazePose is a general-purpose pose estimation model, it has been expanded into an AI model specialized for Korean dance by learning the unique movement elements of Korean traditional dance. The additional traditional dance dataset customized for BlazePose indicates that this model has been tailored to recognize Korean traditional dance movements. Thus, it emphasizes that this is a'specialized' AI learning model, distinct from existing pose estimation models. As shown in Table 8, BlazePose was trained using metadata for five basic Korean traditional dance movements. Through this, an algorithm was developed to recognize three upper-body dance movements and two lower-body dance movements, leading to the development of a functional AI model prototype.