## 6 Conclusions  
This research demonstrates significant advancements in the use of WSNs for the non-invasive monitoring of sleep quality and vital signs in elderly individuals. By leveraging the capabilities of WSNs, this study provides a modern and efficient approach to addressing the health challenges faced by the aging population. Key contributions include the implementation of WSNs to continuously monitor various physiological parameters and environmental factors, such as breathing frequency, deep sleep, snoring, HRV, and temperature. The integration of artificial intelligence for data analysis further enhances the effectiveness of the monitoring system, offering personalized health recommendations that improve sleep quality and overall well-being. The results show substantial improvements in several key metrics, highlighting the potential of this technology to significantly enhance the quality of life for elderly individuals. By focusing on non-invasive and continuous monitoring within the home environment, this study not only contributes to the field of geriatric health but also demonstrates the feasibility and scalability of low-cost technologies in providing personalized healthcare solutions. The findings underscore the potential of WSNs to revolutionize elderly care, offering real-time data that support proactive health management and timely interventions, thereby reducing the burden on caregivers and improving the overall quality of life for the elderly. Moving forward, it is essential to explore the broader implications of this technology, including its adaptability to different living environments and its potential for integration with other health monitoring systems. Future research should also investigate additional environmental variables and sensor types to capture a more comprehensive range of factors that influence sleep quality and vital signs. The results of such research could pave the way for more scalable and adaptable solutions in geriatric health monitoring, thereby contributing to the development of more holistic and effective care strategies for the elderly. The main limitations of the proposed model in this research include the study's reliance on a single type of wireless sensor network and the fact that specific environmental variables may not capture the full range of factors affecting sleep quality and vital signs in diverse settings. The adaptability of the system to different living environments and varying technological infrastructures also remains untested, posing challenges for its broader application. Nevertheless, this research represents a significant milestone in geriatric health monitoring, offering an integrated solution to the challenges of aging. The findings underscore the importance of continuous, non-invasive monitoring and the value of personalized health interventions. By advancing the use of WSNs and AI in healthcare, this study paves the way for future innovations in health monitoring technologies, providing a foundation for more effective and efficient healthcare solutions. The insights gained from this research are expected to inform and inspire future studies, driving further advancements in the field of geriatric care and beyond. This research opens new avenues for future investigation, particularly in exploring the adaptability of WSN technology across diverse living environments and its integration with other health monitoring systems. Further studies could expand on the range of environmental and physiological variables monitored, as well as assess the long-term impact of such technologies on elderly health outcomes. By continuing to refine and adapt these systems, future research can build on the foundations laid by this study, paving the way for more comprehensive and integrated care strategies that address the complex health needs of aging populations. [PERSON] developed the analysis, supervised the research methodology and the approach of this work, prepared the scenario, analyzed the results, and coordinated the overall project.