## 5 Implementation and Evaluation
### Dataset Preparation  
In our experiment, we utilized the PicoScene Wi-Fi sensing platform to gather CSI data. One volunteer performed four different activities--walking, sitting, standing, and bending--as shown in Figure 3, at each of the 10 locations. For every designated activity, we precisely gathered a dataset comprising 10 to 80 CSI subcarriers. This particular data collection was conducted with distinct activity durations and varying motion patterns, executed by the volunteers in a predefined location. As a result, we collected 3219 rows of subcarrier data in our 20 MHz bandwidth for the transmitter experiment setup and 4219 rows of subcarrier data in the 40 MHZ bandwidth of the transmitter in another experiment setup. As shown in Table 4, several sample packets and valid subcarrier segments were placed according to their respective activity and location index. For the systematic investigation of CSI samples, each activity was consistently carried out by four different volunteers across 10 distinct locations. This standardized approach ensured a balanced representation, with 40 packet samples (4 volunteers \(\times\) 10 locations) dedicated to each activity. Within this framework, the segmentation of the subcarriers varied based on the duration of each activity, the unique motion patterns exhibited by volunteers, and the specific packet samples released from a transmitter. For illustrative purposes, as shown in Table 4, consider the example of a walking activity performed at Location 1. The CSI sample for this instance comprises 18 segments. Notable, the unique designation of MST1 encapsulates the following key information: 'M', 'ST' represents the specific activity (Stand), and '1' signifies the particular location. This CSI sample is distinct from others, as it performed the same walking activity in the same location--Location 4. Despite the similarity in activity and location labels (ST1), the CSI segment for 20 segments differs due to the inherent variations in human motion. This emphasizes that even when the activity and location are identical, the CSI subcarrier segments captured from different volunteers have different values, reflecting the individualized nature of each volunteer's motion and location orientation. The real part of the complex number (28) represents the magnitude or strength of the received signal, providing information about the amount of the transmitted signal reaching the destination. The imaginary part of the complex number (6I) conveys the phase information. It indicates the shift in the phase of the received signal concerning the transmitted signal. This phase information is crucial for understanding the alignment and timing of the received signal. In essence, this CSI sample reflects the unique characteristics of the wireless channel during the standing activity conducted at Location 1. The real and imaginary components collectively capture the amplitude and phase information, offering a comprehensive snapshot of the channel's state during this specific instance. Moreover, Figure 8 describes the variation in signals in the same location and activity but performed by different volunteers. Figure 9 illustrates the fluctuation of signals within the same location and activity, although executed by different volunteers. The graph visually depicts how signals vary under identical circumstances, emphasizing the influence of individual volunteers on the observed signal dynamics. This comparison serves to underscore the personalized nature of signal responses, even when the location and activity remain constant. The graph provides a valuable visual representation of the diversity in signal characteristics attributed to the unique patterns and orientation of distinct volunteers engaging in the same task within the same location. Therefore, Figure 9 shows the dataset distribution, but this is not enough to construct a more efficient and accurate model. Consequently, we implemented the CTGAN to synthesize new data from the original raw data.