# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 5 Conclusions  
This work addresses the issue of implementing an integrated IoT system for monitoring and anomaly detection. The proposed prototype improves medical intervention time by facilitating the early detection of situations that pose a risk to the elderly's lives. The results show that the proposed prototype--a wearable bracelet equipped with MAX30100 and MPU-6050 sensors, combined with an anomaly detection algorithm based on PCA and integration with the Azure cloud infrastructure--can monitor the vital parameters of the elderly. The proposed system generates alerts within an average interval of approximately 11 s. The system reported a sensitivity of 100% and an accuracy of 98.5%. These results confirm the method's feasibility for early detection of physiological changes.  
The research involved developing and testing a hardware prototype, implementing a custom anomaly detection algorithm, and conducting continuous evaluations over two years for an elderly subject.  
Integrating cloud technologies with innovative data analysis methods enables continuous and personalized health monitoring of the elderly, contributing to the fields of medicine and healthcare.  
Future studies will include a more diverse sample of patients, covering different age groups and medical conditions, to validate and extend the system's applicability. Additionally, the authors plan to optimize the detection algorithms to reduce the rate of false alerts, which could be achieved by integrating additional machine learning methods. Extending functionality by adding other types of sensors and improving communication between the device and the cloud infrastructure will also represent important directions for development.  
In conclusion, the study demonstrates the superior quality of Microsoft Azure's technologies in implementing IoT solutions, even when these involve monitoring sensitive data that can impact people's lives. The research falls within the field of medicine and healthcare, supporting medical staff and elderly individuals who require monitoring but are not in an advanced stage of illness. As long as they are healthy, mobile, and wish to remain home, they can be automatically monitored remotely.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app1513727/s1](https://www.mdpi.com/article/10.3390/app1513727/s1)]([https://www.mdpi.com/article/10.3390/app1513727/s1](https://www.mdpi.com/article/10.3390/app1513727/s1)), Heart_Rate.txt; Steps.txt; Sleep_Phases.txt; Battery_Level.txt.  
**Author Contributions:** Conceptualization, C.-M.R.; methodology, C.-M.R. and A.S.; software, C.-M.R.; validation, C.-M.R. and A.S.; formal analysis, C.-M.R. and A.S.; investigation, A.S.; resources, A.S.; data curation, C.-M.R.; writing--original draft preparation, C.-M.R. and A.S.; writing--review and editing, A.S.; visualization, C.-M.R. and A.S.; supervision, C.-M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Petroleum-Gas University of Ploiesti, Romania.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.