## 5 Experiment and Results
### Evaluation and Analysis
#### 5.2.3 Using Both 3D Point Cloud Data and Range-Doppler Data as Input  
It can be concluded that, for point cloud data, the 3D CNN + LSTM has the highest accuracy and the lowest loss among the two networks, while for range-Doppler data, the 3D CNN performs best. Therefore, we choose these two networks to be the two parallel branches of the feature fusion network mentioned above. The obtained network structure is shown in Figure 8. Hence, we train this network and draw the accuracy and loss images with the results of these three different networks in Figures 16 and 17 after obtaining the results and tabulating the specific values, as shown in Table 4.  
From the above results, we find that the network using both 3D point cloud data and range-Doppler data as input has better accuracy and loss than using one of them alone. This shows that using more kinds of data can extract more action features, which has a positive factor for the improvement of network performance. We also find that the convergence speed of the fusion network is faster. Similar to the above two networks, we also draw the confusion matrix, as shown in Figure 18, so as to analyze the verification accuracy of each action. We find that the prediction difficulty of these activities is all close except squatting and circling, which are slightly more difficult to identify.  
The above experimental results tell us that the fusion network has better classification results. We further study this fusion network and carry out an ablation analysis. We change the depth of the network and then observe the changes in accuracy and loss value. We change the depth of the network by changing the number of repetitions of the structure of \"Convolution Layer-Convolution Layer-Maxpooling Layer\". The purpose of this is not to change the overall structure of the network as much as possible so as to achieve the effect of controlling other variables. Based on the original network, we double and halve the repetition times of the structure of \"Convolution Layer-Convolution Layer-Maxpooling Layer\" in the two branch networks of the fusion network-3D point cloud branch network and range-Doppler branch network, respectively. In this way, we have obtained two new  
Figure 17: Loss curves of three different networks.  
Figure 18: The confusion matrix of fusion network.  
networks, one of which has 20 layers of 3D point cloud branch network and 18 layers of range-Doppler branch network, while the other has 11 layers of 3D point cloud branch network and 9 layers of range-Doppler branch network. The 3D point cloud branch network of the original network has 14 layers, while the range-Doppler branch network has 12 layers. We train and verify the two new networks with the same data, respectively, and draw the obtained accuracy and loss value into a table, as shown in Table 5.  
The above experimental results show that increasing or decreasing the number of network layers will lead to a decrease in accuracy and an increase in loss value, which means that the best number of network layers is the number of layers of the original network. We know that, generally speaking, when the parameters are adjusted reasonably, the more layers and neurons, the higher the accuracy, which explains why the network performance will deteriorate after reducing the number of layers. However, this is in contradiction with the phenomenon that the network performance still deteriorates after increasing the number of network layers. The reason is that the increasing number of network layers leads to overfitting.