## 6 Impact of Several Experimental Layouts
### Dataset Analysis  
Following the preprocessing of raw CSI data, we obtained a refined dataset consisting of 835 walking samples, 739 sitting samples, 855 bending samples, and 790 standing  
Figure 16: Comparison of the training and testing accuracy of different models. samples. Each CSI data category was used to train its respective CTGAN model. Once the training process reached a stable state, the trained generator models were employed to synthesize CSI data based on specific requirements. Table 8 presents the accuracy of different models in predicting activity and location under the following two scenarios: using real CSI data alone and using a combination of hybrid synthetic and real CSI data. For real CSI data, the LSTM-GRU model outperforms other models in both activity and location prediction, while the LSTM-BIGRU demonstrates competitive performance. However, in the hybrid scenario, where synthetic and real data are combined, the LSTM-BIGRU emerges as the top performer, achieving the highest accuracy among all models. These results underscore the effectiveness of hybrid models, particularly the LSTM-BIGRU, in leveraging the synergy between synthetic and real data to improve predictive performance in complex tasks such as activity recognition and location estimation. Overall, when synthetic and real data are combined, all models, LSTM, GRU, LSTM-GRU, and LSTM-BIGRU, exhibit a substantial improvement in accuracy compared to using real CSI data alone. In our experiment, we used a well-trained CTGAN model with the previously listed hyperparameters to generate three synthetic CSI datasets of varying sizes as follows: 3219 samples, 6438 samples, and 9756 samples. As shown in Figure 17, the tick \"0\" on the \(x\)-axis represents the scenario where only real CSI data were used for training and evaluation. Conversely, the tick \"all\" indicates the presence of fully synthetic CSI data, which are equivalent in quantity to the real CSI data. The line chart illustrates that as the amount of synthetic CSI data increases, there is a consistent improvement in recognition and estimation accuracy. This trend confirms that the inclusion of synthetic CSI data enhances the accuracy of our proposed LSTM-BIGRU model over time. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Target** & & & **Accuracy (\%)** & \\ \hline  & & **LSTM** & **GRU** & **LSTM-GRU** & **LSTM-BIGRU** \\ \hline Real CSI Data Alone & Activity & 74.55 & 74.84 & 80.43 & 64.75 \\  & Location & 47.09 & 55.59 & 59.94 & 41.15 \\ Real + Synthetic & Activity & 96.45 & 97.98 & 99.40 & 99.81 \\ (CTGAN) & Location & 88.48 & 90.19 & 97.84 & 98.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Impact of synthetic data on model performance. Figure 17: Impact of an increasing number of synthetic data. Furthermore, to validate the effectiveness of incorporating synthetic data into our proposed LSTM-BIGRU model for joint human activity recognition and localization, we analyzed the learning curves depicted in Figures 18 and 19. The graphs illustrate the performance of our model when trained on real CSI data alone compared to a combination of real CSI data and synthetic data. The results demonstrate that the inclusion of synthetic data significantly enhances the accuracy of both activity and location predictions. This finding reinforces the effectiveness of our proposed model in accurately predicting and estimating human activity and location when synthetic data are incorporated alongside real CSI data. _Impact of the features_: In this study, we leveraged two main features, namely the amplitude and phase information of CSI, to facilitate activity recognition and location estimation. The amplitude feature captures the magnitude of the signal, while the phase feature provides insights into the signal's phase shift.