## 2 Materials and Methods
### The Structure and Parameters of the Models with Attention Mechanism  
To satisfy the need to accurately monitor emotional stress, which is based on building the framework (CNN-BiLSTM) with deep learning tools, we attempted to improve CNN-BiLSTM using the attention mechanism. For the CNN layer in the CNN-BiLSTM, we used CBAM, a non-local neural network, and DA-NET separately to make the CNN-BiLSTM focus on efficient features and filter out any invalid features. For the BiLSTM layer in the CNN-BiLSTM, attention-based BiLSTM was used instead of normal BiLSTM to increase the attention of the CNN-BiLSTM. As different attention mechanisms make the framework focus on different positions of the feature map, to find the most effective  
Figure 10: Attention-based bidirectional long short-term memory networks. attention mechanism to use with CNN-BiLSTM for this particular issue, we separately constructed the CNN-BiLSTM with the CBAM framework, the CNN-BiLSTM with the non-local neural network, the CNN-BiLSTM with DA-NET, the CNN-attention-based BiLSTM, the CNN-attention-based BiLSTM with CBAM, and the CNN-attention-based BiLSTM with the non-local neural network for verification. The specific parameters of the network were obtained through repeated experiments. For the CNN-BiLSTM with CBAM as shown in Table 2, we added the CBAM module after the CNN. In the CBAM, we set the drop out coefficient of the fully connected layer (K) to 0.3 and the kernel size of the convolution layer to 28. For the CNN-BiLSTM with the non-local neural network shown in Table 3, we added the non-local neural network after the CNN. In the non-local neural network, the convolutional layer was used to generate \(\theta\), \(\phi\), and \(g\). By considering the training time and performance of the framework, we set the number of filters in the convolutional layer (F) to 16. For the CNN-BiLSTM with DA-NET in Table 4, the convolutional layer in the DA-NET was used to generate the feature maps B and C. Considering the training time and performance of the framework, we set the number of filters in the convolutional layer (F) to 8. \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max-pooling & Pool size = 8, stride = 8 \\ Dual Attention Network & The number of filters in the conv layer to \\  & generate B and C in the position module (F) = 8 \\ First BiLSTM & \\ Second BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) with DA-NET (Dual Attention Network). \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Batch normalization + dropout (0.70) & \\ Max-pooling & Pool size = 8, stride = 8 \\ CBAM & K = 0.3, kernel size = 200 \\ First BiLSTM & \\ Second BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The CNN (Convolutional Neural Networks)–BiLSTM (Bi-directional Long Short-Term Memory) with CBAM (convolutional block attention module). \begin{table}
\begin{tabular}{l l} \hline \hline
**CNN** & \\ \hline Convolutional layer & Filter = 32, kernel size = 200, stride = 8 \\ Rectified Linear Unit & \\ Max-pooling & F = 16 (1/2*Filter) \\ First BiLSTM & \\ Second BiLSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) with non-local neural network. For the CNN-attention-based BiLSTM shown in Table 5, we used attention-BiLSTM instead of the first and second layers of the convolutional BiLSTM. For the CNN-attention-based BiLSTM with CBAM as shown in Table 6, the CBAM and attention-based BiLSTM were used simultaneously to obtain the full attention network with the CNN layer and the BiLSTM layer both using the attention mechanism.