## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis  
#### 4.3.1 Performance of Different Classification Algorithms  
(1) Comparison of the accuracy of VGG16, SE-VGG16, ResNet18, ResNet34, ResNet50, MobileNetV3, and EfficientNetB0 for different gestures. In order to evaluate the performance of the SE-VGG16 WiFi gesture recognition method, the recognition accuracy of seven models, including VGG16, SE-VGG16, ResNet18, ResNet34, ResNet50, MobileNetV3, and EfficientNetB0, is compared and analyzed in this study. The results are shown in Figure 8.  
Figure 8 compares the gesture recognition accuracy of seven deep-learning models. Among these models, SE-VGG16 has the highest overall accuracy, with ResNet variants  
Figure 8: Gesture recognition results with different classification models. Figure 7: Experimental scenario and equipment deployment diagram: (**a**) office and (**b**) conference room. ranking closely behind. SE-VGG16's superiority stems from its squeeze-excitation (SE) mechanism, which recalibrates channel-wise features, improving accuracy by 4.13% over VGG16. ResNet models use residual connections for better performance, but trail SE-VGG16 in some challenging scenarios. Lightweight models MobileNetV3 and EfficientNetB0 trade accuracy for computational efficiency; their depthwise separable convolutions and reduced capacity limit performance on intricate gestures. Further analysis of per-gesture accuracy reveals that SE-VGG16's advantage is most pronounced for Enlarge/Narrow gestures, where its attention mechanism effectively isolates subtle finger movements from noise. The results collectively affirm SE-VGG16 as a state-of-the-art solution, particularly for environments requiring high precision across diverse gesture types. (2) Confusion Matrix of Different Gestures in SE-VGG16 Model  
To further assess the SE-VGG16 model's gesture-recognition performance, confusion matrices for office and conference room environments are analyzed. These matrices detail misclassifications among Enlarge, Narrow, Up, Down, Left, and Right gestures. In the office environment (Figure 9a), the SE-VGG16 model shows excellent recognition performance. The confusion matrix shows that the recognition rate of the \"up\" gesture is as high as 94.31%, while the \"zoom in\" and \"zoom out\" gestures with complex motion patterns also maintain a high accuracy of 93.69% and 93.56%, respectively. Despite a small number of misclassifications, the overall misclassification rate is very low, which indicates that the model can effectively distinguish the unique signal characteristics of each type of gesture and maintains stable performance even in the presence of environmental interference. In addition, in the validation of the conference room scenario (Figure 9b), the model also performs well, with a recognition rate of 94.29% for the \"down\" gesture, and other gesture types also maintain a high recognition accuracy. The overall low misclassification rate fully demonstrates the robustness of the model. The results of the confusion matrix clearly demonstrate the effectiveness of the SE-VGG16 model. It is worth noting that the signal uniformity due to the open space of the conference room and the signal variability due to the complex layout of the office have some comparative value, while the model maintains an overall high accuracy rate in different environments. This performance fully demonstrates the model's excellent environmental adaptability and robustness characteristics. Figure 9: Confusion matrix of different gestures in SE-VGG16 mode. (**a**) Confusion matrix for gesture recognition in an office environment. (**b**) Confusion matrix for gesture recognition in a conference room. (3) Comparison of VGG16 and SE-VGG16 performance  
To evaluate the performance enhancement achieved by incorporating the squeeze-excitation mechanism into VGG16, we compared the training and validation accuracy trajectories of both VGG16 and SE-VGG16 across successive epochs.