IoT-Enabled Health Monitoring Glove with Machine Learning-Based Risk Classification

The IoT-Enabled Health Monitoring Glove with Machine Learning-Based Risk Classification is designed to provide continuous monitoring of vital physiological parameters for early health risk detection and remote patient supervision. The proposed system integrates multiple biomedical sensors within a wearable glove to measure important health indicators such as stress level using a GSR sensor, heart rate using a pulse sensor, and body temperature using a Dallas temperature sensor. An Arduino microcontroller collects and processes the sensor data, while an LCD module displays the health parameters locally. The acquired data is transmitted to the ThingSpeak IoT cloud platform through the NodeMCU module for remote monitoring and data storage. A Machine Learning model based on the Random Forest algorithm is employed to analyze the collected physiological data and classify the user’s health condition into normal or risk categories. In case of abnormal health detection, the system activates a buzzer alert and automatically sends warning messages through a GSM module to caregivers or medical personnel. This smart health monitoring glove enables continuous, and intelligent healthcare supervision, making it highly suitable for elderly care, stress monitoring, and remote health management applications.