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

Abstract

The proposed system is an advanced integration of Internet of Things (IoT) and wearable technology, designed to enablereal-time,non-invasivemonitoringofbothstresslevelsand bloodglucoseconcentrationinhumans.Itcombinesphysiological 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 enhancementsincludemachinelearningintegrationforimproved calibration and personalization

Introduction

Overview

The system addresses the growing need for continuous, non-invasive health monitoring, particularly for psychological stress and blood glucose levels—both of which are linked to chronic conditions like hypertension, diabetes, and cardiovascular disease. Traditional methods involve clinical tools and invasive sampling (e.g., finger pricks), which are not ideal for frequent monitoring.

This project leverages IoT and wearable technologies to enable real-time, remote, and user-friendly health tracking.

System Architecture

Central Unit:

• ESP32 microcontroller handles sensor input, processing, and wireless communication.

Key Hardware Components:

• MAX30102 Heart Rate Sensor: Measures pulse via photoplethysmography.

• GSR Sensor: Assesses skin conductance to infer stress.

• NIR Sensor (LED + Photodiode): Estimates blood glucose from reflected near-infrared light.

• Optional: OLED display for vitals, buzzer for abnormal readings.

Software Architecture:

• Captures and digitizes sensor data.

• Processes stress and glucose estimates.

• Sends results to cloud platforms like Firebase or ThingSpeak via Wi-Fi.

Methodology

Sensor Data Collection:

• Heart Rate: Detected using peak analysis.

• GSR: Resistance derived via voltage divider circuit.

• NIR: Reflectance levels mapped to glucose concentration.

Signal Processing:

• Data cleaned and analyzed on-board.

• Custom algorithms compute stress index and estimate glucose levels.

Simulation and Results

Environment:

• Simulated in Python using libraries like NumPy and Matplotlib.

Generated Data:

• Heart Rate: 65–105 BPM

• GSR: 8–30 k?

• NIR readings: 600–2800 units

Outputs:

• Graphs for heart rate, GSR, stress index, and glucose.

• Alerts triggered when thresholds are exceeded.

Cloud Integration:

• Real-time data successfully uploaded to cloud dashboards.

Performance:

• Accurate stress classification using HR and GSR.

• Reliable glucose estimates from NIR mapping.

• Successful wireless data transmission.

Conclusion

Thesystemintegratesnon-invasivestressandglucosemonitoringusingIoT.Itfeatureswearabledesign,cloudconnectiv- ity,andPython-basedsimulationforvalidation.Thecombined use of MAX30102, GSR, and NIR with ESP32 provides a reliable, scalable health monitoring platform.

References

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Copyright

Copyright © 2025 Mohith D, Sneha Patel, K. B. Ramesh, Haron Khadar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.