# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 4 Experiment
### Experimental Results and Analysis
#### 4.3.2 Comparative Experiment  
To verify the effectiveness of the proposed model, the improved network E-MobileNetv2 and the unimproved network MobileNetv2 are trained on the self-built dataset Gesture_II, During the training process, the accuracy of the test set and the training loss value of the model are recorded for every iteration cycle. To grasp the training situation of the model in time, we ensure that each model is in the state of convergence to complete the training. The change curve of loss value and accuracy rate in the training process are shown in Figures 10 and 11, respectively. It can be seen from the loss change curve that the loss function of the two models decreases rapidly in the initial training period, indicating that the model is rapidly fitting and the learning efficiency of the model is high. When the epoch of the improved model was 30, the decrease rate of the loss value of the model began to slow down; when the epoch was 60, the loss curve gradually flattened, the loss value fluctuated around 0.2 after stabilization, and the model converged. The loss value of the original MobileNetv2 declined slowly in the 40 th epoch and then stabilized gradually. It can be seen that the loss value of the improved model is faster than that of the original model, and the loss value of the improved model is smaller after it is stabilized. After the 20 th epoch, the accuracy values of the two models both reached about 90%, and gradually increased with the increase of iterations. Among them, the original MobileNetv2 has great fluctuation in the process of convergence, and the accuracy reaches 93.6% after training, while the improved MobileNetv2 reaches 96.8% after training. Through the comparison of loss and precision experiments, it can be seen that the improved model performs better than the original model in both accuracy change and loss change, and  
Figure 10: Training loss. the improved model has better convergence speed than the original model and is more stable. When iteration stops, it has higher accuracy and lower loss value, which verifies the reliability and effectiveness of the proposed improved method. To verify the competitiveness of the improved E-MobileNetv2, the improved network structure is compared with the existing advanced network models. We chose EfficientNetb0, ShuffleNetv2, ResNet101, and GoogLeNet as the comparison models, and then compared and analyzed the four models in terms of accuracy, number of parameters, and recognition time by training and testing on self-built and public data sets, respectively.The experimental environment and parameter settings are described in Section 3.1. The result is shown in Table 4, where Ts is the single image recognition time on the CPU. The experimental results show that the improved algorithm has the highest accuracy in self-built data, compared with the other two lightweight networks EfficientNetb0 and ShuffleNetv2. The number of parameters and the single image recognition speed are also better. The improved algorithm was 2.65% more accurate than GoogLeNet, a large network. The accuracy of the improved algorithm is second only to ResNet101 on public data sets, which proves that the improved algorithm has certain generalization ability and robustness. The recognition accuracy of the improved algorithm is only 1.7% less than that of ResNet101, but the number of parameters is 1/20 of that of ResNet101, and the single image recognition time on the CPU is only about 1/4 of that of ResNet101. The main reason for this phenomenon is that ResNet101 has deep network layers, a complex model,