## 5 Experimental Results and Discussions
### Performance on Other Datasets
Each participant sits on the designated chair and works on a computer or reads papers but does not move around during the experiment. The second dataset, which we name \(TTW2\), is constructed in an environment similar to \(TTW\). However, the measurement location for the \(TTW2\) dataset is different from that for the \(TTW\) dataset. The size of the room, the number and arrangement of furniture, and the material of the walls are also different. In addition, the positions of the transmitter and CSI receiver are reversed. In other words, in the \(TTW\) environment, the Wi-Fi transmitter is located in the room where the targets to be detected are present, while the CSI receiver is placed in a different room from the transmitter. On the other hand, in the \(TTW2\) environment, the CSI receiver is \begin{table}
\begin{tabular}{c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline Gray Image & 0.76 & 0.77 & 0.83 & 0.72 & 0.92 & 0.85 \\ \hline Median & 0.91 & 0.75 & 0.98 & 0.86 & 1.00 & 0.87 \\ \hline FFT & 0.76 & 0.78 & 0.90 & 0.69 & 0.87 & 0.78 \\ \hline PCA(k2) & 0.72 & 0.67 & 0.74 & 0.64 & 0.79 & 0.76 \\ \hline PCA(k4) & 0.65 & 0.64 & 0.67 & 0.48 & 0.72 & 0.57 \\ \hline Proposed & 0.72 & 0.68 & 0.84 & 0.76 & 0.82 & 0.86 \\ \hline \end{tabular}
\end{table} 
Table 2: Average Wi-Fi sensing accuracy for the EHUCOUNT dataset. PCA(k2) represent the case where the number of principal components is 2, while PCA(k4) represents the case with 4 principal components. \begin{table}
\begin{tabular}{c c c c} \hline  & **Room A** & **Room B** & **Room C** \\ \hline Gray Image & 0.93 & 0.85 & 0.82 \\ \hline Median & 0.99 & 0.99 & 0.94 \\ \hline FFT & 0.90 & 0.84 & 0.82 \\ \hline PCA(k2) & 0.70 & 0.60 & 0.64 \\ \hline PCA(k4) & 0.65 & 0.54 & 0.49 \\ \hline Proposed & 0.95 & 0.87 & 0.85 \\ \hline \end{tabular}
\end{table} 
Table 3: Average Wi-Fi sensing accuracy for RTV dataset. PCA(k2) represents the case where the number of principal components is 2, while PCA(k4) represents the case with 4 principal components. located in the room with the targets while the transmitter is placed in a different room from the CSI receiver. Furthermore, unlike the _TTW_ environment, where someone other than the experiment participants can move freely between the two rooms without any restriction, in the _TTW_ environment, the CSI is measured under controlled conditions, where no one is allowed to pass between the two rooms. In the _TTW_ environment, the number of people in a room ranges from zero to four. In Table 4, we show the average sensing accuracy obtained by different CSI preprocessing methods when they are applied to the _LAB_ dataset and the _TTW_2 dataset. In the case of the _TTW_2 dataset, the average sensing accuracy is the same as 0.99 regardless of the preprocessing methods used. This is attributed to the fact that compared to other datasets, the differences among the CSI data belonging to each class in the _TTW_2 dataset are significant. As a result, regardless of the different CSI features extracted by various preprocessing techniques, the CNN is able to accurately distinguish the CSI data belonging to each class. On the contrary, when we observe the results of the _LAB_ dataset, our method and the PCA method show the best performance. Table 5 shows the rankings of each method across various environments in terms of the average IOD accuracy. In the table, the number corresponding to the \(i\)-th row and \(j\)-th column represents the accuracy ranking of method \(i\) in environment \(j\), with a smaller number indicating higher accuracy. In the table, we observe that there is no preprocessing method that consistently delivers the best performance across all environments. This result agrees with the claims made in [15]. We also observe in the table that the performance of the proposed method ranks second or higher in 10 out of 14 real-world Wi-Fi sensing environments.