## 3 Research Methodology  
_Training Process of the CTGAN_  
The CTGAN synthesizer class from the synthetic data vault (SDV) library enables the generation of high-quality synthetic data. The process begins by initializing the synthesizer, where an instance of the CTGAN synthesizer is created with carefully selected parameters. These parameters significantly influence the training process, including key aspects such as batch size, learning rate, and the architecture of both the generator and discriminator networks. To improve the synthesizer's ability to capture the complexities of the dataset, it is essential to provide metadata that accurately describes the structural relationships within the original data. This metadata acts as a guide, ensuring that the synthetic data closely mimic the underlying patterns and constraints of the source dataset. The training phase involves exposing the CTGAN model to the provided dataset for a specified number of epochs. Throughout this process, the training progress is systematically logged, and the synthesizer may utilize a proportional-integral (PI) or proportional-integral-derivative (PID) controller to ensure convergence, refining the model's generative capabilities effectively. A critical aspect of this phase is fine-tuning the generator and discriminator networks, which involves configuring key parameters such as network dimensions, learning rates, and weight decay to optimize their performance. The CTGAN algorithm determines loss values using a predefined formula, further detailed in the corresponding discussion. The fundamental objective is to minimize the discrepancy between the generated synthetic data and the original dataset. This difference is quantified using \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Refs.** & **Title** & **Proposed Algorithm** & **Identified Gaps** \\ \hline \multirow{4}{*}{[35]} & Joint activity recognition & \multirow{4}{*}{\begin{tabular}{c} CNN-ResNet, \\ MINN \\ \end{tabular} } & The phase information is not stable \\  & and indoor localization & & & in real time, is susceptible to signal \\  & with Wi-Fi sensing based & & & variation, and degrades the \\  & on multi-view fusion & & & performance. \\ \hline \multirow{4}{*}{[36]} & Adaptability decomposing & \multirow{4}{*}{\begin{tabular}{c} Encoder-decoder network \\ action \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Encoder–decoder network \\ recognition and \\ localization. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Its performance is constrained by \\ the accuracy of object proposals \\ generated by the localization \\ subnetwork. \\ \end{tabular} } \\  & Action recognition and & & Spatio-temporal & Reliance on handcrafted features \\  & localization with spatial & & context model & and limited experimentation with \\  & and temporal contexts & & (STCM) & diverse datasets. \\ \hline \multirow{4}{*}{[23]} & Human activity detection & \multirow{4}{*}{\begin{tabular}{c} Support vector machine \\ (SVM), K-nearest neighbor \\ (KNN), and \\ micro-Doppler signatures. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Lack of evaluation of diverse and \\ complex datasets and limited \\ fine-grained localization. In \\ addition, a high sampling rate was \\ required. \\ \end{tabular} } \\  & Joint activity recognition & \multirow{4}{*}{\begin{tabular}{c} Reliance on a specific set of \\ activities, a lack of experimentation \\ with more diverse and complex \\ datasets, and limitations in \\ scalability in larger areas. \\ \end{tabular} } \\  & and indoor localization & & \multirow{4}{*}{\
\begin{tabular}{c} 1D-CNN \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Reliance on a specific set of \\ activities, a lack of experimentation \\ with more diverse and complex \\ datasets, and limitations in \\ scalability in larger areas. \\ \end{tabular} } \\  & With Wi-Fi fingerprints. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work on joint activity recognition and indoor localization.