HOT Watch: IoT-Based Wearable Health Monitoring System
The Internet of Things (IoT) has revolutionized healthcare by enabling real-time remote monitoring of vital health parameters. Sensor-equipped devices facilitate the continuous tracking of parameters such as body temperature, respiratory rate, heart rate, blood pressure, sugar levels, SpO₂, and ECG, allowing for early illness detection and personalized treatment.
Although wearable health monitoring devices have gained significant attention for tracking physiological data, many still face challenges such as high cost, limited accuracy, and complex setups. To overcome these limitations, the IoT-based HOT Watch has been designed as a cost-effective, accurate, and user-friendly solution, seamlessly integrating IoT technology with wearable sensors.
This wearable health monitoring system incorporates multiple sensors, including the MLX90614 temperature sensor, the MAX30100 oximeter sensor, and the AD8232 ECG sensor. These sensors track key health indicators such as body temperature, heart rate, and oxygen saturation.
Data collected by these sensors is then processed using an Arduino ESP8266 microcontroller and transmitted via Bluetooth to a mobile application, where it is analyzed and displayed in an easy-to-read format for real-time health monitoring.
The Pan-Tompkins Algorithm (PTA) is employed to detect QRS complexes in ECG signals and enhance the accuracy of heart rate calculations. The mobile application paired with the watch provides real-time health data visualization, sends notifications, and includes GPS-based location alerts to ensure timely medical intervention.
Prior research on IoT-enabled wearable devices has explored various health tracking solutions, highlighting advancements in accuracy and integration while acknowledging challenges such as cost, power consumption, and system complexity. These issues are addressed by incorporating advanced sensors and a streamlined data processing approach, ensuring reliable and real-time health monitoring.
The system’s effectiveness was evaluated through experimental validation across diverse age groups and genders. Heart rate monitoring revealed distinct patterns influenced by these factors, underscoring the need for personalized health tracking.
Oxygen saturation levels demonstrated an age-dependent trend, with females exhibiting slightly lower oxygen concentrations than males. Additionally, body temperature tracking confirmed an inverse correlation between age and temperature, with minimal gender-based variations.
The device was further assessed through a comparative accuracy analysis against existing wearable health monitoring systems.
With an accuracy exceeding 99.4%, this system outperformed competing devices such as the Sensor Patch (98%), WS-IoT (98.7%), and NeoWear (96.4%). This superior performance underscores its enhanced reliability in real-time health tracking and notifications.
This wearable health monitor advances IoT-based health tracking by integrating multiple sensors, efficient data processing, and seamless mobile connectivity. It continuously tracks key health parameters, providing personalized insights while maintaining high accuracy and low power consumption, ensuring reliability and long-term use.
Additionally, its real-time alerts for abnormal health conditions and GPS tracking enable quick emergency response. The device enhances accessibility and usability and ensures timely medical intervention and proactive health management for people of all backgrounds without requiring technical expertise.
Future developments could incorporate predictive algorithms to analyze health trends, enabling early detection of medical conditions and paving the way for more personalized and intelligent healthcare solutions.