## 6 Impact of Several Experimental Layouts
### Dataset Analysis
By incorporating both amplitude and phase information, our approach enables a comprehensive analysis of the CSI data, allowing us to extract meaningful patterns and characteristics related to human activities and their corresponding locations. This multi-modal feature representation enhances the discriminative power of our model and improves its ability to accurately recognize different activities and estimate precise locations. Table 9 demonstrates the impact of the features on the overall recognition and estimation accuracy of our proposed model. Moreover, the impact of combining amplitude and phase features on the joint human activity recognition and localization task is shown. The results are presented for the following three different models: LSTM, GRU, and LSTM-BIGRU. For both activity and location prediction, the inclusion of fused features (amplitude + phase) generally enhances training and testing accuracy across all models. Specifically,  
Figure 18: Confusion matrix comparison of the real and integrated synthetic CSI data. our proposed LSTM-BIGRU model shows the highest accuracy in both training and testing, reaching up to 99.91% for activity prediction and 97.63% for location prediction. _Impact of the bandwidth_: We performed two significant experiments by varying the signal bandwidths at 20 MHz and 40 MHz within the IEEE 802.11n framework. The IEEE 802.11n offers flexibility in signal bandwidth choice, allowing for options of 20 MHz, 40 MHz, or 80 MHz. The selected bandwidth plays a crucial role in determining the data throughput, with higher bandwidths generally leading to increased data transfer rates. Our investigation focused on evaluating the joint human activity recognition and localization system under bandwidth conditions of 20 MHz and 40 MHz. It is important to note that we did not consider an 80 MHz bandwidth because the transmitter device used in our experiment did not support it. The improvements observed in the accuracy suggest that leveraging combined amplitude and phase information contributes positively to the models' performance in joint human activity recognition and localization. Overall, our investigation uncovered a decrease in accuracy when transitioning from 20 MHz to 40 MHz. However, the hybrid models, particularly the LSTM-GRU and LSTM  
Figure 19: Learning curve of the real and synthetic data CSI data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Accuracy**} \\ \hline  & \multicolumn{3}{c}{**Amplitude**} & \multicolumn{3}{c}{**Phase**} & \multicolumn{3}{c}{**Amplitude + Phase**} \\ \hline
**Model** & **Target** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline LSTM & Activity & 85.00 & 81.83 & 92.70 & 37.42 & 86.56 & 82.30 \\  & Location & 69.00 & 61.18 & 57.38 & 14.13 & 68.61 & 60.40 \\ GRU & Activity & 86.32 & 78.88 & 85.74 & 39.75 & 86.44 & 75.47 \\  & Location & 71.10 & 61.02 & 56.26 & 13.51 & 71.29 & 55.90 \\ LSTM-BIGRU & Activity & 93.86 & 86.18 & 99.91 & 39.75 & 97.63 & 75.78 \\  & Location & 79.53 & 65.99 & 98.06 & 13.82 & 87.41 & 62.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Impact of the features on the recognition and estimation of our model. BIGRU, seem to counteract this decline, displaying enhanced performance at the lower bandwidth. The impact on the accuracy is determined by the complexity of data patterns and the architectures of the models, as illustrated in Table 10.