## 3 SE-WiGR Gesture Recognition Method
### Gesture Recognition Based on SE and VGG-16 Model Fusion
#### 3.3.2 Squeeze and Excitation Networks  
The squeeze-and-excitation networks (SENets) are an innovative neural network architecture that introduces the squeeze-and-excitation (SE) block as its core component. The SE block automatically learns and determines the importance of individual feature channels, explicitly modeling inter-channel dependencies. Figure 5 shows the schematic structure of the SE block. In contrast, the excitation operation uses fully connected layers to learn nonlinear channel-wise relationships and performs feature recalibration--selectively enhancing informative features while suppressing less useful ones. This recalibration strategy allows SENet to leverage global context more effectively, optimizing the network's attention mechanism for improved performance. A squeeze and excitation block is a computational unit whose core function is to map the input data \(x\), \(x\in R^{H^{\prime}\times W^{\prime}\times C^{\prime}}\) to the features \(U,U\in R^{H\times W\times C}\) by the transformation \(F_{tr}\). In this computation, we employ \(F_{tr}\) as the operator of the convolution operation and define a collection of filter kernels \(V\), \(V=[v_{1},v_{2},...,v_{C}]\), and set a parameter for each filter,  
Figure 4: The structure of the VGG16 network. Figure 5: The structure of the squeeze–excitement (SE) module. defined as \(v_{c}\), which are learned and optimized during training. Thus, the output can be written as \(U=[u_{1},u_{2},...,u_{C}]\), where  
\[u_{c}=v_{c}\times X=\sum_{s=1}^{C^{\prime}}v_{c}^{s}*x^{s} \tag{6}\]  
where \(*\) denotes the convolution, \(v_{c}=\left[v_{C}^{1},v_{C}^{2},\ldots v_{C}^{C^{\prime}}\right]\), \(X=\left[x^{1},x^{2},\ldots,x^{C^{\prime}}\right]\) and \(u_{C}\in R^{H\times W}\). \(v_{C}^{S}\) is a two-dimensional spatial kernel denoting the individual channels \(v_{c}\) acting on the corresponding channels of \(X\). The representation is based on a two-dimensional space. (1) Squeezing mechanism  
To address the challenge of leveraging inter-channel dependencies, we begin by examining the feature distribution across individual channels in the output. Due to the locality of each filter's receptive field, the resulting activation units lack awareness of broader spatial context beyond their own regions. To overcome this limitation, we introduce a strategy that condenses global spatial cues into a compact channel-wise representation. This is accomplished through the application of global average pooling, which computes statistical features for each channel. Formally, the statistic \(Z\in R^{C}\) is generated by reducing \(U\) to its spatial dimension \(H\times W\), so that the \(c\)th element of \(z\) is computed using Equation (7):  
\[z_{x}=F_{sq}(U_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{7}\]  
(2) Excitation Mechanism  
An excitation mechanism is introduced to fully utilize the information aggregated during the squeeze operation, enabling effective modeling of channel-wise dependencies. This mechanism employs a simple gating function with sigmoid activation. \[s=F_{ex}(z,W)=\sigma(g(z,W))=\sigma(W_{2}\delta(W_{1}z)) \tag{8}\]  
where \(\delta\) denotes the ReLU function, \(W_{1}\in R^{\frac{C}{r}\times C}\) and \(W_{2}\in R^{C\times r}\). Fully connected layers are placed on both sides of the nonlinear region, aiming to tune the gating mechanism, constrain the model complexity, and at the same time enhance the generalization performance.