## 3 Experimental Setup
### Network Settings and Evaluation Indicators  
The proposed architecture was implemented within the PyTorch 1.11 deep learning framework and trained using the AdamW optimizer, which demonstrated faster empirical convergence and achieved higher accuracy in this work. Model training parameters were carefully selected through a series of experiments. The dropout rate was set to 0.2, and the model was trained for 200 epochs with a batch size of 1024. To ensure reproducibility, the parameters were initialized using a fixed random seed, and the initial learning rate was set to \(5\times 10^{-3}\). All experiments are conducted on a computer with an Intel CPU i5-13600 KF 3.50 GHz and an Nvidia RTX 4090 GPU, which are sourced from Intel Corporation and Nvidia Corporation, respectively, in Santa Clara, USA. The detailed structure and parameters of the proposed model are listed in the Table 2.  
\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 \multirow{4}{*}{\
\begin{tabular}{l} Patch \\ embedding \\ \end{tabular} } & 1D Convolution & [1; 128; 16] & 5 & GELU & 10,368 \\  & Layernorm & [1; 128; 16] & - & - & - \\  & 1D Convolution & [1; 128; 16] & 3 & GELU & 49,280 \\  & Dropout(0.2) & [1; 128; 16] & - & - & - \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Partial block \\ \end{tabular} } & 1D Convolution & [1; 32; 16] & 3 & - & 4128 \\  & Softmax & [1; 32; 16] & - & - & - \\  & 1D Convolution & [1; 32; 16] & 1 & - & 3104 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed parameters of each block used in the proposed model.  
Figure 2: Process of segmentation. The red box indicates the preceding sliding window, the yellow box indicates the subsequent sliding window. The stride represents the overlapping region between the two sliding windows.  
During training, all the data are randomly shuffled. To address potential distribution differences between the training and test data, a total trial is used for the training and testing [26]. Specifically, NinaPro DB4 trials numbered 1, 3, 4, and 6 of all 10 subjects were used for training, while trials numbered 2 and 5 were used for testing.  
In this work, the performance of methods is evaluated by classification accuracy and F1-score. Accuracy is the most commonly used classification evaluation metric. The formula is defined by Equation (7).  
\[Accuracy=\frac{Correctedly\ predicted\ samples}{All\ samples}\times 100 \tag{7}\]  
The F1-score is selected as another evaluation index due to the presence of a large number of similar actions in the data, which, together with accuracy, forms the evaluation index system of the method. Its formula is defined by Equation (8).  
\[F1\text{ score }=\frac{Precision\times Recall}{Precision+Recall}\times 2 \tag{8}\]  
in which precision and recall are determined by the following equation:  
\[Precision=\frac{TP}{TP+FP}\ \ \ Recall=\frac{TP}{TP+FN} \tag{9}\]  
where the operators \(TP\), \(FP\), and \(FN\) are true positives, false positives, and false negatives, respectively. The results for each label are weighted by the number of samples in each class to calculate the F1-score.