## 4 Experiments
### Experimental Results  
To evaluate the effectiveness of the proposed data augmentation strategy, we first trained two representative baseline models CNN3D and Transformer on both the raw dataset and the augmented dataset for 500 epochs. The training and validation accuracy curves are shown in Figure 4.  
The CNN3D model trained on the raw data exhibits signs of overfitting, where the training accuracy continues to increase while the validation accuracy fluctuates and fails to steadily improve. In contrast, as shown on the right, the CNN3D model trained on the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Batch Size** & **Learning Rate** & **Weight Decay** & **Dropout** & **Hidden Dim** & **Notes** \\ \hline CNN3D & 16 & \(3.15\times 10^{-4}\) & \(1.79\times 10^{-5}\) & 0.395 & 320 & \\ TCN & 32 & \(1.48\times 10^{-4}\) & \(5.21\times 10^{-5}\) & 0.266 & 256 & Kernel size = 3, 6 layers \\ Transformer & 32 & \(4.60\times 10^{-4}\) & \(8.55\times 10^{-5}\) & 0.102 & 32 & 2 layers, 8 heads \\ Informer & 32 & \(9.83\times 10^{-5}\) & \(7.92\times 10^{-3}\) & 0.422 & 256 & Kernel size = 0, 4 layers \\ Sparse Transformer & 16 & \(7.79\times 10^{-4}\) & \(2.97\times 10^{-4}\) & 0.458 & 384 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model -specific training hyperparameters used in all experiments. augmented data achieves consistently higher validation accuracy with reduced fluctuation, indicating better generalization and more stable convergence. For the Transformer model, the benefit of augmentation is even more pronounced. When trained on the raw dataset, the Transformer fails to reach satisfactory accuracy within 500 epochs and suffers from underfitting. However, when trained on the augmented dataset, the model quickly converges to a high level of both training and validation accuracy, demonstrating that the augmented data not only improves overall performance but also enables more effective model fitting within the same training budget. These results confirm that the proposed augmentation approach enhances both convergence speed and final accuracy, especially for deeper architectures such as Transformers. To provide a comprehensive evaluation of model performance on the augmented gesture dataset, we trained five representative architectures CNN3D, TCN, Transformer, Informer, and Sparse Transformer for 1000 epochs. The training trajectories and final accuracy outcomes are presented in Figure 5, respectively. As illustrated in Figure 5, all models demonstrate effective learning and converge successfully on the augmented dataset. CNN3D and TCN exhibit fast initial convergence, reaching high accuracy in the early training phase. Transformer-based models, including Informer and Sparse Transformer, present a more progressive and stable learning process, characterized by consistent improvement across epochs and smoother validation curves. Figure 5 complements these findings by comparing the final training and validation accuracy. All models achieve high overall accuracy, confirming the suitability of the augmented data across architectural paradigms. Notably, Transformer-based models attain a particularly strong balance between training and validation performance, with Informer and Sparse Transformer both reaching 94.1% validation accuracy. In addition, CNN3D achieves the highest validation accuracy of 96.5%, further validating the effectiveness of the augmentation strategy on convolutional architectures. Figure 4: Training versus validation accuracy across different models and data settings over 500 epochs. (**a,b**): CNN3D model trained on raw and augmented datasets, respectively; (**c,d**): same comparison for the Transformer model. Augmented data consistently leads to improved validation accuracy and faster convergence, particularly mitigating overfitting in deep architectures. Overall, these results highlight the versatility and strength of the augmented dataset in supporting both classical and modern architectures.