Stress Monitoring System

Status: published

Abstract

Continuous and objective stress monitoring is essential for effective mental health management, as traditional assessment methods are subjective and lack real-time capability. This paper presents an IoT-based Stress Monitoring System that integrates multiple physiological sensors and cloud computing for real-time stress detection. The system uses a GSR sensor for skin conductance, a MAX30102 sensor for heart rate and pulse measurement, and a DHT11 sensor for temperature and humidity monitoring, all interfaced with an ESP8266 microcontroller. Sensor data is transmitted to cloud platforms using ThingSpeak for real-time visualization and Firebase for secure data storage and web-based monitoring. A lightweight machine learning model classifies stress levels into low, moderate, and high categories. Experimental results demonstrate reliable stress identification, efficient data transmission, and effective cloud integration, proving the system suitable for continuous, non-invasive, and remote stress monitoring applications.

Authors

• Dr. Kiran H. Sonawane — Assistant Professor (Dept. of Electronics And Telecommuincation) — kiran.sonawane@rcpit.ac.in
• Premraj G. Sonar — UG Student (Dept. of Electronics And UG Student (Dept. of Electronics And Telecommuincation) — sonarpremraj2@gmail.com
• Vedika M. Wani — UG Student (Dept. of Electronics And UG Student (Dept. of Electronics And Telecommuincation) — vedikamahendrawani@gmail.com
• Anushka S. Kothavade — UG Student (Dept. of Electronics And UG Student (Dept. of Electronics And Telecommuincation) — kothavadeanushka@gmail.com
• Himanshi V. Jaware — UG Student (Dept. of Electronics And UG Student (Dept. of Electronics And Telecommuincation) — himanshijaware524@gmail.com

Keywords: Stress Monitoring System, Internet of Things (IoT), Galvanic Skin Response (GSR), MAX30102 Sensor, Physiological Signals, Machine Learning, Cloud Computing, ThingSpeak, Firebase, Real-Time Monitoring

Download published PDF