## 6 Impact of Several Experimental Layouts
### Discussion
However, when synthetic data were integrated with real CSI data, our LSTM-BIGRU model significantly improved, achieving 99.81% accuracy for activity recognition and 98.93% for location estimation. Furthermore, Table 10 presents a comparative analysis with two relevant studies that exclusively used real CSI data without incorporating synthetic data, providing a contextual foundation for our findings. To verify the statistical significance of our results, we conducted 10-fold cross-validation, yielding a standard deviation of \(\pm\)0.3% for activity recognition accuracy, confirming the model's robustness. Notably, our proposed hybrid neural network model, reinforced with synthetic data, outperformed previous models that relied solely on real CSI data. By leveraging the unique features generated by the CTGAN, our model demonstrated exceptional performance, highlighting the effectiveness of synthetic data in enhancing the network's ability to distinguish and localize human activities. This finding makes a significant contribution to the existing body of knowledge on integrated human activity recognition and localization. While previous research has primarily focused on CSI data, our study pioneers the inclusion of synthetic data, demonstrating its efficacy in improving the accuracy of hybrid neural networks. Additionally, our findings underscore the benefits of hybrid models in utilizing multiple data sources to produce more robust and accurate predictions. These insights not only advance our theoretical understanding but also have practical implications for designing more effective human activity recognition and localization systems. Generally, the CNN-ResNet and 1D-CNN are highly effective for feature extraction but struggle with capturing sequential dependencies. In contrast, LSTM and the GRU excel at processing sequential data but operate in a single direction, limiting their ability to fully capture contextual relationships. The BIGRU enhances performance by processing sequences in both forward and backward directions, leading to richer feature representations and improved contextual learning. The LSTM-BIGRU hybrid model leverages the strengths of both architectures--LSTM's ability to capture long-term dependencies and BIGRU's efficient bidirectional processing. This combination results in improved classification accuracy while maintaining a balance between performance and computational efficiency. Additionally, the BIGRU uses fewer parameters than LSTM, making it more memory-efficient and faster to train while still delivering high accuracy. By integrating these advantages, the LSTM-BIGRU model offers superior performance compared to traditional LSTM and GRU models, making it a powerful choice for sequence-based tasks. While transformer-based models have demonstrated superior performance in various sequential tasks, LSTM was chosen as the backbone for this study due to its efficiency in capturing temporal dependencies and its proven success in human activity recognition and localization. Although transformers excel in modeling long-range dependencies, they require large-scale datasets and significant computational power for self-attention mechanisms. Given our dataset's size, even after augmentation with the CTGAN, the computational cost of training transformers remained considerably higher than that of LSTM-based architectures.