## 5 Implementation and Evaluation
### Experimental Results
Figure 10: Learning curve and confusion matrix of the LSTM-GRU model at 20 MHz. integrated approach aims to provide a comprehensive solution for joint activity recognition and location estimation tasks from sequential CSI data. The data visualizations in Figures 12 and 13 illustrate the comprehensive training progression of the LSTM-BIGRU model concerning our concurrent task in a 20 MHz bandwidth. The activity loss curve exhibits a consistent descent throughout most epochs; however, a notable deviation occurs around epoch 55. This divergence raises concerns about potential challenges in the model's generalization capabilities. However, the model achieved better accuracy compared to other models. Figure 12: Learning curve of the LSTM-BIGRU at 20 MHZ. Figure 13: Confusion matrix of the LSTM-BIGRU at 20 MHZ. Upon comparing the models, ranging from simple architectures to hybrid designs, for the joint prediction of activity and location, several key observations can be made. Among these models, LSTM-BIGRU demonstrated the best performance for simultaneous activity recognition and localization, as illustrated in Figures 14 and 15. As shown in Tables 6 and 7, the RNN models achieved higher accuracy across both 20 MHz and 40 MHz bandwidths. The impact of the bandwidth on model performance is further highlighted in the provided accuracy tables. Figure 16 presents a comparative analysis of the results at 40 MHz and 20 MHz, revealing distinct performance trends across different models. Table 6 demonstrates that our proposed LSTM-BIGRU model outperforms the LSTM-GRU architecture in joint activity and location prediction tasks. While both LSTM and GRU models achieve good accuracy, LSTM generally performs better than the GRU. However, the LSTM-GRU and LSTM-BIGRU hybrid models exhibit significant improvements, achieving high accuracy in both training and testing for activity and location prediction. Additionally, Figure 15 further confirms that our proposed hybrid architectures enhance the model's ability to capture intricate patterns in CSI data. Notably, location prediction accuracy is generally lower than activity prediction accuracy, suggesting that location estimation might be a more complex task or require further optimization. For activity prediction, the LSTM model demonstrates slightly higher accuracy at 40 MHz (96.80%) compared to 20 MHz (96.45%) during training, with comparable testing accuracy (92.77% at 40 MHz vs. 91.23% at 20 MHz). However, for location prediction, there is a noticeable decrease in accuracy when transitioning from 40 MHz to 20 MHz; training accuracy drops from 92.78% (40 MHz) to 88.48% (20 MHz), and testing accuracy declines from 82.64% (40 MHz) to 79.50% (20 MHz). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Accuracy** & \\ \hline
**Model** & **Target** & **Training** & **Testing** \\ \hline LSTM & Activity & 96.80 & 92.77 \\  & Location & 92.78 & 82.64 \\ GRU & Activity & 96.86 & 93.07 \\  & Location & 91.67 & 82.52 \\ LSTM-GRU & Activity & 96.26 & 93.66 \\  & Location & 95.07 & 89.69 \\ LSTM-BIGRU & Activity & 96.99 & 93.07 \\  & Location & 95.04 & 88.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model comparison in the 40 MHz bandwidth. Figure 15: Confusion matrix of the LSTM-BIGRU at 40 MHZ. Similarly, the GRU model exhibits a slight decrease in both training and testing accuracy for activity prediction when moving from 40 MHz to 20 MHz. Additionally, location prediction accuracy declines, highlighting the impact of a reduced bandwidth on the model's ability to estimate location. In contrast, the LSTM-BIGRU hybrid model achieves higher accuracy at 20 MHz for both activity and location prediction, surpassing the results obtained at 40 MHz. Overall, our experiment indicates a general reduction in accuracy when transitioning from 20 MHz to 40 MHz. However, the hybrid models LSTM-GRU and LSTM-BIGRU appear to mitigate this, exhibiting enhanced performance at lower bandwidths.