## 6 Conclusions for Future Research  
This study introduces a DL model called CNN-GRU-AttNet, designed to automatically recognize human behavior from WiFi CSI signals. Human activity can be represented as time-series data with temporal and spatial characteristics. The CNN-GRU-AttNet model addresses this challenge by extracting spatial and significant features simultaneously using convolutional blocks and attention modules, respectively. Additionally, the GRU block is employed to capture latent temporal patterns within the CSI signals. By combining these three components, the model effectively represents the CSI signal's characteristics and focuses its attention on activity-related information. Consequently, the CNN-GRU-AttNet model improves the accuracy of activity recognition. Evaluations were conducted on two distinct datasets, CSI-HAR and StanWiFi, resulting in recognition accuracies of 99.62% and 98.66%, respectively. A comparative analysis with existing approaches demonstrated the superiority of the proposed model, achieving improvements of 4.62% and 0.66% in accuracy, respectively. There are potential plans to collect and analyze empirical datasets obtained from environments with significant interference. The task of identifying multi-user activity in real-world situations poses a more realistic and complex challenge compared to identifying single-user activity. As a result, this study's research will be expanded to include HAR for multiple users. Publicly available datasets often contain common activities that do not accurately represent real-world situations, as individuals engage in a variety of activities on a daily basis. Therefore, the acquisition of a WiFi dataset that includes a broader range of indoor human activities will be deferred for future research. Conceptualization, S.M. and A.J. ; methodology, S.M. ; software, A.J. ; validation, W.P. and A.J. ; formal analysis, S.M. ; investigation, S.M. and A.J. ; resources, N.H.; data curation, S.M. ; writing--original draft preparation, S.M. and N.H.; writing--review and editing, N.H. and A.J. ; visualization, W.P. ; supervision, A.J. ; project administration, N.H.; funding acquisition, S.M. and A.J. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**Activity**} & \multicolumn{3}{c}{**Recognition Performance**} \\ \cline{3-5}  & & **Accuracy** & **Recall** & **F1-Score** \\ \hline Subject 1 & Lie down & 82.4\% & 100.0\% & 90.3\% \\ (an adult) & Fall & 100.0\% & 96.6\% & 98.2\% \\  & Bend & 86.2\% & 100.0\% & 92.6\% \\  & Run & 92.1\% & 100.0\% & 95.9\% \\  & Sit down & 100.0\% & 76.9\% & 87.0\% \\  & Stand up & 100.0\% & 87.0\% & 93.0\% \\  & Walk & 100.0\% & 89.3\% & 94.3\% \\ \hline Subject 2 & Lie down & 100.0\% & 96.3\% & 98.1\% \\ (a middle-aged person) & Fall & 100.0\% & 100.0\% & 100.0\% \\  & Bend & 100.0\% & 100.0\% & 100.0\% \\  & Run & 100.0\% & 100.0\% & 100.0\% \\  & Sit down & 96.6\% & 96.6\% & 96.6\% \\  & Stand up & 100.0\% & 100.0\% & 100.0\% \\  & Walk & 96.7\% & 100.0\% & 98.3\% \\ \hline Subject 3 & Lie down & 100.0\% & 100.0\% & 100.0\% \\ (an elderly person) & Fall & 100.0\% & 93.3\% & 96.6\% \\  & Bend & 88.9\% & 100.0\% & 94.1\% \\  & Run & 100.0\% & 95.5\% & 97.7\% \\  & Sit down & 100.0\% & 92.9\% & 96.3\% \\  & Stand up & 92.6\% & 100.0\% & 96.2\% \\  & Walk & 100.0\% & 100.0\% & 100.0\% \\ \hline \end{tabular}
\end{table} 
Table 8: Recognition performances of the proposed CNN-GRU-AttNet based on CSI data from different subjects. **Funding:** This project was funded by the Thailand Science Research and Innovation Fund; University of Phayao (Grant No. FF66-UoE001); National Science, Research and Innovation Fund (NSRF); and [PERSON]'s University of Technology North Bangkok with Contract no. KMUTNB-FF-66-07. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All data were presented in the main text.