# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 1 Introduction
### Health Monitoring Using IoT-Based Wearable Devices  
In the specialized literature, several Internet of Things (IoT) configuration proposals for monitoring patients' health status are available. [PERSON] et al. [38] propose a hardware configuration that includes temperature and heart rate sensors connected to the Arduino Uno microcontroller and ESP8266. Tests resulted in an accuracy of 96% in monitoring temperatures above 37.5 \({}^{\circ}\)C and a heart rate above 120 beats per minute (BPM). The work by [PERSON] et al. [39] also employs DS18B sensors, the MAX30102 module, and an Arduino Nano to monitor oxygen saturation and electrocardiogram (ECG). The same configuration, which includes Arduino Nano for monitoring using DHT11 sensors for temperature, MAX30100 for heart rate and blood oxygen level, and a buzzer that sounds when the results are ready, is also described by [PERSON] et al. [40]. [PERSON] [41] monitors the same parameters using a pulse sensor for photoplethysmography (PPG) measurement, a Negative Temperature Coefficient (NTC) sensor for body temperature, an HC-06 Bluetooth module for communication, and an Arduino Pro Mini platform. The collected data is transmitted to the Blynk application in real-time, allowing for graphical display on Android and iOS devices. Similar configurations based on sensors for monitoring vital functions are presented in works [42, 43, 44]. [PERSON] et al. [45] include radio frequency identification (RFID) technology to monitor patients' medical conditions. Data collected from sensors attached to the patient's body are updated in the database via a Wireless Personal Network (WPN) or mobile connection, depending on the patient's location. The system updates patients' medical records using a unique radio frequency identification (RFID) tag.  
[PERSON] et al. [46] propose an innovative sensor for monitoring vital signs through blood pressure, heart rate, and respiratory signals. This sensor uses a low-frequency reactive field to detect changes in arterial blood flow. The sensor was validated by comparing measurements with those of a commercial PPG sensor and a traditional sphygomanometer in 30 subjects. The sensor proposed by the authors yields values that are correlated with those obtained using conventional methods. [PERSON] et al. [47] compare the performance of wearable medical devices against traditional devices. The research shows no major differences between the values reported by these devices for the vital functions of the 60 patients. [PERSON] et al. [48] demonstrate that the Impulse-Radio Ultra-Wideband (IR-UWB) radar simultaneously monitors respiratory activity and carotid pulses in a non-contact manner, relying on electromagnetic waves to detect fine body movements. A biometric driver seat is presented by [49] using Six-Port receiver technology.  
[PERSON] et al. [50] introduce an automated health risk assessment system for the elderly integrated through wearable sensors and deep learning technology, such as LSTM. The model presented in this research uses five vital signs (blood pressure, heart rate, blood oxygen saturation, respiratory rate, and temperature), and the predicted values are used to calculate a risk score, thereby implementing an early warning system. Experiments demonstrated that the optimal result was achieved using a dataset of 1000 patients, a 12 h time window, and a configuration of 50 epochs and 5 LSTM neurons. [PERSON] et al. [51] point out that wearable systems offer a level of safety for the elderly, contributing to their improved health. The authors analyze 50 people aged between 50 and 70 years. Using the monitoring system resulted in a 4% improvement in inner peace for women, while for men, this impact was only 2%. Most participants showed a reduction in heart rate when the system was active. These results contribute to the positive implications of monitoring systems in the lives of elderly and lonely individuals.