# Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
## 4 Results  
### Experiment  
In this paper, we compared the performance of the different network configurations in Table 3 by setting the following as parameters: the 2D pseudo image size encoded in PFE, the number of depth-wise separable convolution layers (#ds-conv.), and the channel configuration of the network. Table 4 lists the numbers of output channels per convolution layer for each network (A, B, C, D, E). The Network A configuration has 64 output channels for all depth-wise separable convolution layers, and Network B has 128 output channels for the last depth-wise separable convolution layer. Network C has the last two depth-wise separable convolution layers, each with 128 output channels. Network D has the last depth-wise separable convolution layer with 256 output channels and the previous layer with 128 output channels. Finally, Network E has 256 output channels in the last depth-wise separable convolutional layer and 128 output channels in each of the two previous layers. In this way, a total of 42 networks were configured, and an NVIDIA RTX A6000 GPU [27] (NVIDIA, Santa Clara, CA, USA) was used for network training and verification. In addition, all the networks used the same training settings (300 epochs, learning rate of 0.001, batch size of 24, Adam optimizer, and negative log likelihood loss (NLL loss)). For training and verification, we used a dataset of 11,769 frames collected by ourselves, which were divided into training data of 10,594 frames and test data of 1175 frames. In addition, to investigate the generality of the proposed HAR system, we performed stratified 10-fold cross validation. \"Accuracy\" was calculated to evaluate the classification performance in terms of true positives (_TPs_), true negatives (_TNs_), false positive (_FP_), and false negatives (_FNs_).  
\[\textbf{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN} \tag{1}\]  
Finally, the model with the smallest number of network parameters (64 \(\times\) 64 image size, 3 depth-wise separable convolutions, and B network configuration) was selected as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{4}{c}{**\#ds-conv.**} \\ \cline{2-5}  & **2** & **3** & **4** \\ \hline A & 64, 64 & 64, 64, 64 & 64, 64, 64 \\ B & 64, 128 & 64, 64, 128 & 64, 64, 64, 128 \\ C & 128, 128 & 64, 128, 128 & 64, 64, 128, 128 \\ D & 128, 256 & 64, 128, 256 & 64, 64, 128, 256 \\ E & - & 128, 128, 256 & 64, 128, 128, 256 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Output channel configuration for network (A,B,C,D,E).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & \multicolumn{4}{c}{**Network**} \\ \hline
**Image  
the final image classification network among those models with an HAR accuracy of 95% or higher (Table 3) to configure the HAR system.  
The backbone network structure of the proposed HAR system is shown in Figure 4. When a 2D image of size 64 \(\times\) 64 with 64 channels was input, it was converted to an 8 \(\times\) 8 image with 128 channels using three depth-wise separable convolution layers. It was then passed through max pooling and the fully connected layer to classify the 11 classes of human activity.  
We compared the performance of the optimized depth-wise separable convolution-based network with the existing, representative, image classification networks LeNet5 [28], VGGNet [29], ResNet [30], and MobileNet [31] as the backbone network of the HAR system. Table 5 shows the results of comparing the classification accuracy and network complexity between the networks, confirming that the network proposed in this study is superior in terms of accuracy, number of multiply-accumulate (MAC) operations, and number of network parameters.