## 5 Experimental Results and Discussions
### Influence of CSI Amplitude Coloring  
To identify the root cause of these performance differences, we examine the CSI data distribution for class 2 (i.e., two people) in the Queuing scenario. In this case, when the _Gray Image_ method is used, the rate at which the CNN incorrectly classifies CSI data belonging to class 2 as class 4 is 36%. However, when our method is used, the misclassification rate dramatically decreases to 4%. To understand the factors contributing to the improved results, we conduct an analysis using t-SNE (t-Distributed Stochastic Neighbor Embedding) plots. t-SNE is a machine learning algorithm used for the visualization of high-dimensional data [37]. t-SNE is a popular tool for visualizing complex datasets in a low-dimensional space (typically 2D or 3D), making it easier to identify patterns, clusters, and relationships that might be hidden in higher dimensions. Figure 7 shows the t-SNE plots of the CSI data at the input layer of CNN, and Figure 8 shows the t-SNE plots of the CSI data at the last layer of the CNN, which contains the features that CNN uses for indoor occupancy classification. In these figures, black dots indicate CSI data that both belong to and are correctly classified as class 2 (denoted as C2P2), while blue dots signify the CSI data that are both from class 4 and correctly classified (denoted as C4P4). Red dots represent CSI data belonging to class 2 but mistakenly classified as class 4. They are denoted as C2P4. The spatial distribution of the data points in Figure 7a reveals that the red dots are in closer proximity to the blue dots compared to the black dots. This arrangement indicates that in the CNN feature space shown in Figure 8a, the CSI data associated with C2P4 are more closely related to the CSI data from C4P4 than to the CSI data from C2P2. As a result, CNN mistakenly categorizes the CSI data from C2P4 as belonging to class 4.  
Figure 7b, Figure 7c, and Figure 7d respectively show the t-SNE plots for the red, green, and blue channel data when the measured CSI data belonging to each class are segmented into each channel. In the green channel, we observe that the CSI data for C2P4 remain closer to C4P4 than to C2P2. However, this relationship inverts in the red and blue channels, where C2P4 data align more closely with C2P2 data than with C4P4 data because our CSI data dimension expansion method amplifies the subtle differences between C2P4 and C4P4. In Figure 8b-d, we also examine the t-SNEs of the final classification features extracted after the data from each channel have been processed through CNN. These figures reveal that when red channel data and blue channel data are fed into CNN, the red dots (C2P4 data) are positioned nearer to the black dots (C2P2 data) than to the blue dots (C4P4 data).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Gray Image** & **Median** & **FFT** & **PCA** & **Proposed** \\ \hline Avg. & 0.06 & 1.83 & 4.48 & 0.10 & 0.12 \\ \hline Var. & 0.00 & 0.41 & 1.45 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Preprocessing time comparison (unit: second).  
Figure 8e illustrates the t-SNE plot for the data collected at the last layer of CNN after all RGB channel data go through CNN. As evidenced in this figure, by partitioning the measured CSI data into three distinct channels, CNN gains the ability to delineate more distinct boundaries between CSI data from different classes. In other words, the tripartite channel approach that amplifies the subtle differences among the classes in terms of the measured CSI allows CNN to leverage varied data representations, leading to improved discrimination between classes.  
Figure 8: _Cont_.  
Figure 7: Comparison of t-SNE plots for the data at the input layer of CNN. (The notation C2P2 refers to the subset of class 2 CSI data that the CNN correctly identifies as class 2, while C2P4 represents the set of CSI data belonging to class 2 but that the CNN misclassifies as class 4. C4P4 indicates the set of class 4 CSI data that the CNN successfully classifies as class 4.)