# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 1 Introduction
### Paper Contributions  
The authors of this paper possess expertise in the field of the integration of IoT technologies [58], AI-specific methods [59], and addressing the compatibility issues between them [60], enabling the development of a new infrastructure to integrate anomaly detection technologies through user data analysis. The analyzed data focuses on the vital functions of users, with applicability in generating alerts for elderly or at-risk individuals. The research questions (RQs) of this paper are as follows:  
* RQ1. How can an Azure cloud-based IoT infrastructure be used for a critical situation alert system associated with the elderly? * RQ2. Can a principal component analysis (PCA)-based anomaly detection algorithm be used to monitor vital parameters for the elderly? * RQ3. To what extent can a wearable device equipped with integrated sensors prevent medical complications by providing real-time alerts? Based on these questions, the paper analyzes, in the following sections, the use of IoT technologies for monitoring the health of the elderly and anomaly detection in a cloud context, providing concrete solutions for each. To evaluate the uniqueness of our proposed solution, we conducted a comprehensive search in WOS using the query TS = (\"wearable bracelet\" OR \"wearable device\") AND\"anomaly\" AND \"elderly.\" This search yielded only three results [61; 62; 63], highlighting the limited research in this specific domain. Notably, none of these studies propose a cloud-based solution akin to the one presented in this manuscript. Among the identified studies, only one explores an anomaly detection approach based on life patterns [61]. However, the methodology and implementation described in that study differ from those proposed in our solution. Specifically, the existing work relies on localized data processing and does not leverage cloud computing for real-time monitoring or advanced analytics. In contrast, our work introduces a multidimensional anomaly detection algorithm integrated with a cloud-based architecture, enabling real-time tracking of elderly individuals using wearable bracelets. The authors disseminate through this article the following innovative elements from their research in the field of elderly health monitoring:  
* A customized IoT system is proposed for the continuous health monitoring of an elderly person;
* A wearable bracelet prototype with specialized sensors (MAX30100 and MPU-6050) is proposed. Collecting vital parameters--heart rate (average, minimum, and maximum), step count, and sleep quality (deep and superficial sleep)--provides a holistic view of the patient's health status. Although such a bracelet already exists, this prototype is compatible with the IoT Azure infrastructure proposed in this work;
* A multidimensional anomaly detection algorithm is proposed using a dimensionality reduction method based on PCA. The health anomaly detection algorithm (HADA) identifies anomalies in the monitored data by analyzing complex correlations between the proposed parameters. This approach allows for personalized analysis tailored to the specifics of each patient;
* Integration of the Microsoft Azure cloud infrastructure into the system ensures secure data transmission. Preprocessing of data and real-time automatic alert generation is facilitated by the Azure IoT infrastructure, enabling prompt medical interventions in critical situations;
* The tests conducted in the Results section disseminate energy analysis for wearable devices. Implementing energy-saving strategies, including deep sleep mode, extends the bracelet's operational life, making the system practical for continuous use at home;
* The study presents results from continuous patient monitoring over two years, demonstrating a high sensitivity of 100% and an overall accuracy of 98.5%. These contributions represent a milestone in research in the field, combining embedded technologies, IoT, and Cloud.