## 5 Experimental Results and Discussions
### Performance on Other Datasets  
To further show the validity of our proposed method, we validate our method by applying it to the publicly available datasets for people counting by Wi-Fi sensing. The first dataset is named the EHUCOUNT dataset described in [15]. The second dataset is the dataset in [38]. Henceforth, we call the second dataset the RTV dataset. The ETHCOUNT dataset is constructed by capturing Wi-Fi signals in six different indoor scenarios over facilities of the faculty of engineering of the university of the Basque Country. The six scenarios are A (Office), B (Lab), C (Corridor), D (Hall + Stairs), E (Corridor), and F (Corridor). Depending on the scenario, the number of people in the scenarios is from 3 to 5, and the number of CSI traces per the number of people and the scenario ranges between 12,000 and 15,000. In the corridor scenario, people maintain a direction for a while before changing it, while people wander in the room scenario (A, B, D). In all scenarios, volunteers are instructed to move slower than 3 km/h. The RTV dataset is constructed by collecting the CSI data in three rooms, which differ in size, the number of furniture pieces, and their locations. Room A is a small-size office room (5 m \(\times\) 5 m) and room B is a medium-size meeting room (5 m \(\times\) 9 m) while room C is a large-size meeting room (6 m \(\times\) 12.5 m). In each scenario, up to 7 people are in the rooms and 5000 CSI samples are collected for each number of people (i.e., 0 to 7). During the CSI collection, the people in the rooms move randomly around or stand still without any guidelines. Tables 2 and 3 show the average sensing accuracy when different CSI preprocessing methods are applied to each dataset. We observe that in most scenarios, the _Median_ method shows the best performance. This is attributed to the fact that the CSI measurement environments for EHUCOUNT and RTV datasets are more favorable for the Wi-Fi signal propagation than our datasets. The biggest difference between the experimental environments of these datasets and our experimental environments is that in the experimental settings for EHUCOUNT and RTV datasets, the transmitter and CSI receiver are located in the same space, while in our experimental environments (especially the TTW and Corner  
Figure 8: Comparison of t-SNE plots for the data at the last layer of CNN. scenarios), the transmitter and CSI receiver are located in different spaces. In case of our Queuing scenario, even though the transmitter and CSI receiver are in the same space, there is no direct signal path between them. In other words, while the EHUCOUNT and RTV datasets measure CSI in line-of-sight (LoS) environments between the transmitter and receiver, we measure CSI in non-line-of-sight (NLoS) environments. Thus, more noise is included in our CSI dataset. As a result, the statistical characteristics of CSIs belonging to different classes become less distinct and are more affected by noise when our dataset is used. Because the statistical differences between the CSI data belonging to different classes are large in the EHUCOUNT and RTV datasets, the _Median_ method effectively distinguishes between classes. However, we observe in these tables that our method is compatible with other CSI preprocessing methods in terms of the sensing accuracy. To test the generality and the robustness of our method, we extend the experimental scope by carrying out additional experiments in different indoor environments. The first dataset is collected in a typical laboratory environment, which we will call the \(LAB\) dataset. The size of the laboratory is 5.6 m \(\times\) 3.4 m. We locate a CSI receiver on the table positioned at the center of the laboratory and place a transmitter at the center of the left side of the laboratory. The laboratory is equipped with typical office furniture and supplies, including seven tables and six chairs. Up to six people participate in the experiments in the \(LAB\) environment.