# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 4 Discussion
The authors acknowledge that the proposed prototype requires extensive testing, validation, and refinement for production deployment. The current work presents an initial concept rather than a fully developed production-ready system. The primary objective of this research is to introduce an IoT-based approach and highlight the feasibility of integrating AI, IoT, and cloud technologies for elderly health monitoring. The proposed solution is a conceptual framework demonstrating how these technologies can be leveraged for continuous health assessment. While the current prototype provides a foundation for further development, future work should focus on extensive clinical trials and ensuring compliance with medical standards. By presenting this model, the authors emphasize the potential of intelligent health monitoring systems in addressing the challenges of elderly care. The integration of AI enables personalized analysis, IoT devices facilitate real-time data collection, and cloud computing ensures scalability and accessibility. In this research, the Azure platform was chosen due to its native integration capabilities with the development tools used, namely ASP:NET and ML_NET. Moreover, the platform offers the necessary advantages for the proposed application type, which aims to process data in real-time and generate automatic alerts. It is important to mention that the Azureplatform is a proprietary platform, which involves a series of monthly costs, making it inaccessible to user categories with a limited budget. However, the platform demonstrates the proposed prototype's ability to achieve its objectives. Alternative solutions to this platform include Amazon Web Services (AWS) Free Tier, which is a commercial solution with a free plan but limited in certain features, and Google Cloud Platform (GCP), which is also available for free but with limited features, ThingsBoard, which is free, or developing solutions locally. In Table 1, the alternative solutions are summarized along with a series of advantages and disadvantages. Most existing solutions are currently available in a commercial version, but there is also an open-source version, such as ThingsBoard, which implies no costs but requires advanced programming skills, resulting in similar overall solution costs. healthcare plans. ML algorithms deployed in the cloud analyze vast datasets, recognizing trends and deviations indicative of potential health risks. This proactive approach enhances preventive care and supports timely medical decisions. Moreover, cloud-based architectures provide scalability and flexibility, allowing integration with IoT-enabled sensors and wearable devices [64]. This interconnected system ensures that elderly individuals receive tailored healthcare interventions based on real-time physiological data. Additionally, cloud platforms enhance data security and interoperability, addressing concerns related to patient privacy and compliance with medical standards. Our proposed approach aligns with modern digital health initiatives by leveraging cloud technologies. This advancement underscores the potential of cloud-based health monitoring to revolutionize elderly care. Integrating cloud computing with health monitoring opens new research directions in digital health. This study provides a framework for future research in personalized healthcare. The proposed methodology optimizes algorithms for real-time health assessment and anomaly detection. It also emphasizes the need for interdisciplinary collaboration between computer science, medicine, and data analytics to enhance predictive healthcare models. From a practical perspective, our findings offer valuable insights for healthcare providers, policymakers, and technology developers. The proposed system enables remote and continuous patient monitoring, reducing the burden on healthcare facilities while ensuring timely medical interventions. This is particularly relevant for elderly care, where early detection of health deterioration can improve patient outcomes.