## 5 Conclusions  
This study is driven by the need for a non-invasive method to monitor sleep, focusing on identifying sleep apnea--a condition prevalent among the elderly and individuals who sleep alone. While previous research, such as [33], has explored the use of Wi-Fi Channel State Information (CSI) for detecting physical activity and movement, our study pushes the boundaries by using CSI to measure sleep RRsTo overcome environmental challenges in typical sleeping environments and enhance Wi-Fi signal stability, we aligned our methodology with RR measurements obtained from the Vernier Belt. Despite initial challenges posed by the intrinsic characteristics of CSI, which allow signal transmission through obstacles, we developed a robust signal processing approach. This involved initial noise reduction using Sections 2.5.1-2.5.3, followed by Section 2.5.4 to isolate frequencies corresponding to human breathing rates. This significantly improved signal accuracy and reliability. Furthermore, we implemented the \"Z-Score for Anomaly Detection\" method to detect errors in our data analysis. Subsequently, we applied a local peak function to determine the number of pulses, enabling us to calculate a precise bpm value. To validate our methodology, we conducted tests with a diverse group of volunteers. The comparison of our results with those obtained from the Vernier Belt confirmed the efficacy of our approach.  
Our findings highlight the feasibility of using widely available Wi-Fi CSI for non-intrusive sleep monitoring, particularly benefiting the elderly and individuals with sleep apnea. The absence of direct contact with the monitoring device offers a seamless sleep experience. Detailed results and parameter adjustments are documented in Section 3, showcasing the adaptability and effectiveness of our method. By demonstrating the capability of common Wi-Fi-enabled devices to capture vital health metrics, this study lays the groundwork for future investigations into the broader applications of Wi-Fi CSI in health monitoring. It opens avenues for further exploration into non-invasive diagnostic tools, promising exciting possibilities for the future of healthcare technology.  
Conceptualization, T.H.; methodology, R.B. and T.H.; software, R.B.; validation, R.B., A.T. and B.R.L.; formal analysis, R.B., A.T. and B.R.L.; investigation, R.B.; resources, T.H., R.B., A.T. and B.R.L.; data curation, R.B.; writing--original draft preparation, R.B.; writing--review and editing, T.H., A.T. and B.R.L.; visualization, R.B.; supervision, T.H.; project administration, T.H.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Thammasat University Research Fund grant number TUFT 87/2566.  
The study was conducted in accordance with the Declaration of Helsinki, the Belmont report, CIOMS guidelines and the International practice (ICH-GCP), and approved by the Institutional Review Board (or Ethics Committee) of The Human Research Ethics Committee of Thammasat University (Science), Thailand, (149/2565 approved on 11 February 2023).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data cannot be provided due to privacy.  
The authors would like to thanks the Advance Geospatial Technology Research Unit, SIIT, and the Center of Excellence in Digital Earth and Emerging Technology (DEET), Thammasat University for providing technical environment and supportive information. We gratefully acknowledge the financial support provided by the Thammasat University Research Fund.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.