## 6 Impact of Several Experimental Layouts
### Discussion  
In [41], contrastive learning is utilized to separate content and style features, ensuring that enhanced underwater images maintain their structural integrity while improving visibility. This approach focuses on learning discriminative representations by enforcing style-based feature separation, optimizing image enhancement tasks. Similarly, in human activity recognition, distinguishing between different movements (e.g., walking, running, and sitting) relies on learning fine-grained sequential differences, much like how contrastive learning separates style and content features in image enhancement. However, while contrastive learning in image enhancement is used to disentangle and reconstruct meaningful visual representations, human activity recognition models primarily learn temporal dependencies to differentiate activities. The activity recognition approach often relies on sequential models like the LSTM-BIGRU, which process temporal information, whereas contrastive learning in image processing is typically applied in convolutional or transformer-based architectures. We utilized CSI to investigate cooperative human activity recognition and localization. Our solution employed hybrid deep neural networks to accurately identify and localize specific actions performed by individuals in a given environment. By integrating modern deep learning algorithms, our study aimed to enhance the accuracy and reliability of human activity detection and location estimation using CSI data. We explored various deep neural network architectures, including 1D-CNN and RNN models such as LSTM and the GRU. Additionally, we examined hybrid RNN models, notably the LSTM-GRU and LSTM-BIGRU, which were extensively trained under different experimental conditions. Our research involved two key real-world studies, where we utilized the PicoScene Wi-Fi sensor platform to collect CSI data. The first experiment was conducted using a 20 MHz bandwidth, while the second used a 40 MHz bandwidth, resulting in 7538 subcarrier segments across both settings. However, due to data limitations, constructing a highly accurate model within our proposed framework proved challenging. To address this limitation, we implemented a data generation technique. Specifically, we leveraged the CTGAN algorithm to generate synthetic data from real CSI data. After training the CTGAN model on our real CSI dataset, we successfully generated three distinct synthetic datasets that closely mirrored the characteristics of actual CSI data, complete with the corresponding labels. Following this, we conducted a series of experiments aimed at developing our proposed LSTM-BIGRU model for the joint task of activity recognition and localization. This process involved integrating the synthetically generated data with real CSI data to enhance the model's accuracy. Before constructing our proposed model, we performed parameter tuning using a grid search approach, focusing on hidden size and the number of layers as the search space. This meticulous approach allowed us to select the most reliable parameters, ensuring the development of an accurate model capable of effectively predicting activities and estimat \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} \\ \hline
**Model** & **Activity** & **Location** \\ \hline CNN-ResNet [35] & 88.13 & 95.68\% \\
1D-CNN [40] & 89.57\% & 95.68\% \\ LSTM-BIGRU (Our proposed model) & 99.81\% & 98.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Model comparison with related works. ing locations. Our investigation provided key insights into the impact of incorporating synthetic data from CTGAN on the performance of our LSTM-BIGRU model for human activity recognition and localization. Using only real CSI data, our model achieved an accuracy of 81.21% for activity recognition and 61.96% for location estimation.