## 4 Experiment and Results  
### Evaluation of the Proposed Network  
Our proposed method's classification accuracy and F1-score on the Ninapro DB4 and DB5 and the BioPatRec DB1, DB2, and DB3 datasets are presented in Table 3. To comprehensively assess the model's performance, we conducted tests with window sizes of 50 ms, 150 ms, and 250 ms. For Ninapro DB4, the classification accuracy and F1-score were 83.0% and 83.1%, respectively, with a window size of 250 ms. In the case of Ninapro DB5, a window size of 250 ms resulted in a classification accuracy of 87.6% and an F1-score of 87.4%. For BioPatRec DB1, DB2, and DB3, the best results were obtained with a window size of 250 ms, with identical classification accuracies and F1-scores of 91.4%, 91.3%, and 91.3%, respectively. This consistency demonstrates the stability of the model. Our experimental results suggest that larger window sizes generally lead to higher accuracy but introduce increased latency. Striking a balance between accuracy and latency is crucial. The results indicate that a window size of 150 ms represents a favorable choice, offering performance close to the 250 ms window with minimal performance loss while reducing real-time recognition latency, and it outperforms the 50 ms window in terms of accuracy. Figure 3 illustrates the confusion matrices for BioPatRec DB1 at 50 ms, 150 ms, \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Block** & **Type** & **Output Shape** & \
\begin{tabular}{l} **Kernel** \\ **Size** \\ \end{tabular} &
\begin{tabular}{l} **Activation** \\ **Unit** \\ \end{tabular} & **Parameters** \\ \hline Channel & 1D Convolution & [1, 64, 16] & 3 & Sigmoid & 24,640 \\ selection block & 1D Convolution & [1, 64, 16] & 3 & - & 24,640 \\ \hline \multirow{6}{*}{Classification head} & AdaptiveAvgPool & [1, 64, 1] & & - & - \\  & FC layer & [1, 256] & - & GELU & 16,640 \\ \cline{1-1}  & LayerNorm & [1, 256] & - & - & 512 \\ \cline{1-1}  & Dropout(0.2) & [1, 256] & - & - & - \\ \cline{1-1}  & FC layer & [1, 256] & - & GELU & 65,792 \\ \cline{1-1}  & LayerNorm & [1, 256] & - & - & 512 \\ \cline{1-1}  & Dropout(0.2) & [1, 256] & - & - & - \\ \cline{1-1}  & FC layer & [1, 53] & - & - & 13,621 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._and 250 ms. It is apparent from the figures that the model can correctly predict most classes, achieving single-class recognition accuracies of 94%, 95%, and 96% for the three different window sizes, respectively. To further analyze the performance of the model and attempt to visualize it, the machine learning method t-SNE (t-distributed Stochastic Neighbor Embedding) [35] was employed to perform dimensionality reduction analysis on the model's output. This was done to measure the Euclidean distance between different categories and samples of the same category, thereby facilitating an analysis of the model's performance. t-SNE is a nonlinear dimensionality reduction technique designed to map high-dimensional data into two or three-dimensional space for visualization. By applying t-SNE for nonlinear dimensionality reduction and data visualization techniques, we conducted an in-depth visual analysis of the model's classification performance. It effectively projected complex high-dimensional feature information into a two-dimensional space. As illustrated in Figure 4, as the window segment length increased from 50 ms to 250 ms, the model's performance in recognizing different hand gesture actions progressively improved. This improvement was evident in the increased clustering of similar gestures and the more pronounced distinction between different gestures. Notably, when the window lengths were set at 150 ms and 250 ms, there was no significant difference in the model's performance in classifying various hand gestures. This finding suggests that within a certain range of window lengths, further increasing the length has a limited effect in terms of enhancing the model's classification capabilities.