## 5 Experimental Results
### Learning and Results of the Evaluation Model  
The encoder models described in Sections 4.4 and 4.5 can perform feature extraction on motion data, respectively. The feature vectors obtained from these encoders were combined to train the evaluation model. Specifically, in the combined model (CL + AE), feature vectors obtained from each encoder are combined and used as input to the evaluation model. LMA and random forest (RF) were used as baseline models, and then a model using encoders with contrastive learning for feature extraction (CL), a model using autoencoders for feature extraction (AE), and a model using both contrastive learning and autoencoders (CL + AE) were compared for performance. In addition to the kappa score and quadratic weighted kappa described in Section 4.6, correlation coefficients and mean absolute error (MAE) were used as evaluation measures. The results are shown in Tables 13-18 for each evaluation item. The results show that the model (CL + AE) that combines contrastive learning and an autoencoder achieved an average accuracy improvement of more than 10 points for the quadratic weighted kappa. Compared with the other evaluation metrics, (CL + AE) \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Quadratic** & **Correlation** & **MAE** \\  & **Weighted Kappa** & **Kappa** & **Coefficient** & **MAE** \\ \hline LMA + RF & 0.575 & 0.035 & 0.562 & 1.829 \\ CL & 0.785 & 0.117 & 0.793 & 1.238 \\ AE & 0.752 & 0.048 & 0.767 & 1.361 \\ CL + AE & **0.882** & **0.192** & **0.846** & **1.027** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation model learning results (dynamics). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Search Range** & **Value** \\ \hline Vector combining method & Sub, Concat, Mul & Sub \\ \hline Number of dense layers & 1–3 & 2 \\ \hline Number of units in dense layers & 8–1024 & (256, 64) \\ \hline Dropout rate & 0.0–0.5 & 0.2 \\ \hline Learning rate & 0.00001–1 & 0.0005 \\ \hline Batch size & 8–256 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Parameters of evaluation model (reference-guided). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Dynamics** & **Sharpness** & **Scalability** & **Timing** & **Accuracy** & **Stability** \\ \hline LMA + RF & 0.575 & 0.548 & 0.450 & 0.562 & 0.585 & 0.539 \\ CL (Euclidean) & **0.785** & **0.762** & **0.666** & **0.747** & **0.759** & **0.797** \\ CL (Manhattan) & 0.658 & 0.635 & 0.593 & 0.660 & 0.571 & 0.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation model learning results (contrastive learning). These results indicate that the automatic evaluation model can be applied to unknown dancers and can produce reliable evaluations. Next, we compared the reference-guided model, which compares the model with a reference example. In this study, we proposed a reference-guided model that enables relative evaluation through comparison with a model. To test the robustness of the reference-guided model to unknown choreography, the first 32 counts of each dancer's dance were used for training and the remaining eight counts were used for testing. The classification accuracies of the evaluation model without a model (no reference) and the reference-guided model with a model are shown in Table 19.  
The training results showed that the reference-guided model had the best accuracy in all items. In particular, the kappa score improved by more than 5 points in the evaluation of \"sharpness\", \"accuracy\", and \"stability.\" This is thought to be due to the fact that more specific performance evaluation became possible by referring to the model motion data. Figures 21 and 22 show the prediction results for the test data. Figure 21 shows the prediction results for \"dynamics\" for all 20 dancers in confusion matrices when the last count in the test data was used as the input. As before, the left side of this figure shows the baseline (LMA + RF) results and the right side shows the results for the proposed method (CL + AE).