## 6 Conclusions and Future Works  
In this paper, we propose a CSI data preparation method to enhance the classification performance of a Wi-Fi sensing system that counts the number of people. We normalize  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Env.** & **Gray Image** & **Median** & **FFT** & **PCA(k2)** & **PCA(k4)** & **Proposed** \\ \hline _LAB_ & 0.85 & 0.70 & 0.88 & 0.99 & 0.99 & 0.99 \\ \hline _TTW_2 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average Wi-Fi sensing accuracy for TTW2 and LAB datasets. PCA(k2) represents 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 c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Our Datasets**} & \multicolumn{4}{c}{**EHUCOUNT**} & \multicolumn{4}{c}{**RTV**} \\ \hline
**Method** & **TTW** & **Queuing** & **Corner** & **LAB** & **TTW2** & **A** & **B** & **C** & **D** & **E** & **F** & **RoomA** & **RoomB** & **RoomC** \\ \hline Gray Image & 6 & 3 & 4 & 5 & 1 & 2 & 2 & 4 & 3 & 2 & 3 & 3 & 3 & 3 \\ \hline Median & 4 & 6 & 3 & 6 & 1 & 1 & 3 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline FFT & 4 & 5 & 6 & 4 & 1 & 2 & 1 & 2 & 4 & 3 & 4 & 4 & 4 & 3 \\ \hline PCA(k2) & 1 & 4 & 4 & 1 & 1 & 4 & 5 & 5 & 5 & 5 & 5 & 5 & 5 \\ \hline PCA(k4) & 3 & 2 & 2 & 1 & 1 & 6 & 6 & 6 & 6 & 6 & 6 & 6 & 6 \\ \hline Proposed & 2 & 1 & 1 & 1 & 1 & 4 & 4 & 3 & 2 & 4 & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of rankings of each method across various environments in terms of the average IOD accuracy. 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.  
the measure CSI data and divide the normalized range into three sections. Then, we propose three transform functions for each range to amplify the values in each section. We heighten the fine distinctions between CSI classes by expanding the normalized CSI amplitude data of each subcarrier to a three-dimensional vector with the transformation functions. The experimental results in 14 real-world scenarios show that our method is a more universal and comprehensive method for people counting via Wi-Fi sensing, compared to conventional methods.  
As our future works, we will investigate the influence of the CSI measurement configurations such as the CSI sampling frequencies and the size of probe packets on the sensing accuracy. Then, we will propose installation guidelines for Wi-Fi sensing systems. Further dimension expansion can be obtained by applying our method consecutively multiple times. Since this process causes complex nonlinear transformation, we will explore the influence of this transformation on the accuracy of Wi-Fi sensing. In addition, we will train the transform functions in conjunction with CNN training to automatically find the optimal transform functions in different Wi-Fi sensing environments. We are also planning to apply our method to various Wi-Fi sensing applications other than people counting.  
Methodology, J.P.; Software, J.S.; Validation, J.S.; Writing--original draft, J.P.; Supervision, J.P. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Research Foundation of Republic of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1065371).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.