## 3 SE-WiGR Gesture Recognition Method
### Gesture Recognition Based on SE and VGG-16 Model Fusion
#### 3.3.1 VGG16 Network Architecture  
VGGNet is a CNN structure network, which includes an input layer, multiple convolutional and pooling layers, fully connected layers, and an output layer. Specifically, the network architecture comprises 13 convolutional layers utilizing 3 \(\times\) 3 filters for spatial feature learning, each coupled with ReLU activations to introduce nonlinearity, alongside three fully connected layers. Max pooling operations utilize a 2 \(\times\) 2 kernel with a stride of 2, effectively performing down-sampling. The network architecture proceeds as follows: First, Block1 performs two 64-kernel convolutions followed by pooling. Block2 then applies two 128-kernel convolutions with pooling. Subsequently, Block3 executes three 256-kernel convolutions before pooling. This pattern continues in Block4 and Block5, each performing two 512-kernel convolutions followed by pooling. The structure concludes with three fully connected layers--the first two containing 4096 neurons each, and the final layer  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer Type** & **Configuration Parameters** & **Output Size (H \(\times\) W \(\times\) C)** & **SE Module** & **Remarks** \\ \hline Input Layer & & & 224 \(\times\) 224 \(\times\) 3 & - & RGB image input \\ Convolution + ReLU & 64 kernels, 3 \(\times\) 3, stride = 1, & & 224 \(\times\) 224 \(\times\) 64 & - & - \\ Convolution + ReLU & 64 kernels, 3 \(\times\) 3, stride = 1, & & 224 \(\times\) 224 \(\times\) 64 & SE-64 & First SE module \\ Max Pooling & 2 \(\times\) 2, stride = 2 & 112 \(\times\) 112 \(\times\) 64 & - & - \\ Convolution + ReLU & 128 kernels, 3 \(\times\) 3, stride = 1, & 112 \(\times\) 112 \(\times\) 128 & - & - \\ Convolution + ReLU & 128 kernels, 3 \(\times\) 3, stride = 1, & & 112 \(\times\) 112 \(\times\) 128 & SE-128 & - \\ Convolution + ReLU & 128 kernels, 3 \(\times\) 3, stride = 1, & & 56 \(\times\) 56 \(\times\) 128 & - & - \\ Max Pooling & 2 \(\times\) 2, stride = 2 & 56 \(\times\) 56 \(\times\) 128 & - & - \\ Convolution + ReLU (3 layers) & 256 kernels, 3 \(\times\) 3, stride = 1, & 56 \(\times\) 56 \(\times\) 256 & - & First two layers \\ Convolution + ReLU & 256 kernels, 3 \(\times\) 3, stride = 1, & 56 \(\times\) 56 \(\times\) 256 & - & without SE \\ Max Pooling & 2 \(\times\) 2, stride = 2 & 28 \(\times\) 28 \(\times\) 256 & - & - \\ Convolution + ReLU (3 layers) & 512 kernels, 3 \(\times\) 3, stride = 1, & 28 \(\times\) 28 \(\times\) 512 & - & First two layers \\ Convolution + ReLU & 512 kernels, 3 \(\times\) 3, stride = 1, & & & without SE \\ Max Pooling & 2 \(\times\) 2, stride = 2 & 14 \(\times\) 14 \(\times\) 512 & - & - \\ Convolution + ReLU (3 layers) & 512 kernels, 3 \(\times\) 3, stride = 1, & 14 \(\times\) 14 \(\times\) 512 & - & First two layers \\ Convolution + ReLU & 512 kernels, 3 \(\times\) 3, stride = 1, & 14 \(\times\) 14 \(\times\) 512 & - & Without SE \\ Convolution + ReLU & 512 kernels, 3 \(\times\) 3, stride = 1, & 14 \(\times\) 14 \(\times\) 512 & SE-512 & Third layer with SE \\ Max Pooling & 2 \(\times\) 2, stride = 2 & 7 \(\times\) 7 \(\times\) 512 & - & - \\ Fully Connected + ReLU & 4096 units & 1 \(\times\) 1 \(\times\) 4096 & - & Input after \\ Fully Connected + ReLU & 4096 units & 1 \(\times\) 1 \(\times\) 4096 & - & flattening \\ Fully Connected + & 6 units (gesture categories) & 1 \(\times\) 1 \(\times\) 6 & - & Output probability \\ Softmax & 6 units (gesture categories) & 1 \(\times\) 1 \(\times\) 6 & - & Output & for 6 gesture classes \\ \hline \hline \end{tabular}
\end{table}
Table 2: SE-VGG16 network architecture.  
comprising 1000 neurons. These FC layers transform the convolutional feature maps into class probabilities. The Softmax classifier then processes the final layer's output to generate a probability distribution across categories, enabling classification. The complete VGG16 architecture is illustrated in Figure 4.