IoT-Based Stress and Non-Invasive Glucose Monitoring System Using Wearable Technology and Edge AI

The proposed system is an advanced integration of Internet of Things (IoT) and wearable technology, designed to enable real-time, non-invasive monitoring of both stress levels and blood glucose concentration in humans. It combines physiological sensing, embedded processing, and cloud connectivity to deliver continuous health insights. The stress detection module utilizes a heart rate sensor and a Galvanic Skin Response (GSR) sensor to monitor changes in pulse rate and skin conductivity. For glucose estimation, Near-Infrared (NIR) spectroscopy is employed. An ESP32 microcontroller serves as the system’s core, collecting sensor data, preprocessing it, and wirelessly transmitting it to cloud-based dashboards. A Python-based simulation environ- ment facilitates debugging and analysis. The system offers a compact and scalable solution for wearable applications. Future enhancements include machine learning integration for improved calibration and personalization.