# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 4 Discussion  
The main results of the study indicate that the proposed IoT system, which combines a wearable bracelet equipped with an M5 Stack module, MAX30100 and MPU-6050 sensors, and an anomaly detection algorithm based on PCA, demonstrated a sensitivity of 100% and an accuracy of 98.5% in the continuous monitoring of vital parameters for a patient over two years. The device utilizes a dual-core ESP32 microcontroller operating at a frequency of 240 MHz, a two-inch LCD screen with a resolution of 320 \(\times\) 240 pixels, and a 1400 mAh battery. The energy consumption tests have highlighted that the best performance is achieved when the M5 Stack module operates in deep sleep mode, resulting in a battery life of 22 h. This system transmits the data acquired from the sensors to the Azure cloud infrastructure, where it is stored in a Microsoft SQL Server database, preprocessed, and evaluated in real-time for the generation of automatic alerts. The obtained results can be explained by the fact that the multidimensional analysis of the data (including heart rate, step count, and sleep quality) enables the identification of complex correlations between parameters, making it possible to detect even the most  
Figure 13: Correlation between S and anomaly detection. subtle physiological changes. Implementing energy-saving techniques, such as deep sleep mode, extended the device's operational life, enhancing the system's practical utility. The research presented in this study represents a conceptual prototype for monitoring the health of elderly individuals through continuous data collection, anomaly detection, and real-time alert generation. While the system's performance has been evaluated based on a single patient, the results provide valuable insights into the feasibility of such a system in a real-world production context. It is important to note that this study does not provide an exhaustive comparison with other AI methods or existing commercial wearable devices. Instead, it serves as a starting point for future research and development aimed at mass production. The research demonstrates that the proposed solution is feasible and can offer practical benefits, such as real-time health monitoring and prompt caregiver alerts. While limited in scope, the current prototype has shown promising results in detecting anomalies in the monitored parameters. The precision and recall rates indicate that the system is sensitive to potential health issues, albeit at the cost of generating false positives. This trade-off is important when dealing with elderly individuals, as it prioritizes safety by erring on the side of caution. These contributions are highly significant in elderly health monitoring, offering an innovative, non-invasive, personalized solution for early anomaly detection. Through the rapid interventions triggered by the alert system, serious medical complications can be prevented, thus contributing to improved quality of life and reduced pressure on healthcare systems. Although PCA is not compared with other AI techniques like LSTMs, Random Forest, or Isolation Forest, it is worth noting that the choice of PCA was made based on its simplicity, ease of implementation, and suitability for anomaly detection in this particular use case. However, the future development of this system could include testing alternative AI models to further optimize its performance. Similarly, the absence of comparisons with commercial wearable health devices such as Fitbit, Apple Watch, or Garmin should not detract from the significance of this research. These devices primarily focus on specific health metrics, such as heart rate and step count, but do not offer the same integrated anomaly detection and real-time alerting features tailored for elderly care as the HADA prototype. The proposed system aims to surpass conventional wearables by offering continuous, comprehensive monitoring of multiple physiological parameters and alerting caregivers to potentially critical changes in health conditions.