Stress is a critical contemporary health issue, causing significant mental and physical damage. Regular monitoring of a person's stress level is vital for early diagnosis of abnormalities that can lead to chronic diseases in the future. While various stress detection methods exist, this research introduces a novel and accessible IoT-based system that integrates a unique combination of physiological parameters: heart rate, body temperature, and, distinctively, exhaled CO₂ concentration. This approach utilizes low-cost, readily available components, including an Arduino Mega microcontroller, an ESP8266 Wi-Fi module, a Pulse Heart Rate sensor, a DS18B20 temperature sensor, and an MQ-2 sensor to measure respiratory CO₂. The significance of this work is demonstrated through its successful implementation and testing on five young adult subjects. The results establish a clear correlation between the measured biometrics and four distinct stress classifications (severe, moderate, mild, and normal). All data is displayed in real-time on a local LCD and transmitted to the Thingspeak IoT platform for continuous analysis. This study confirms the feasibility of using this novel sensor combination to create an affordable, real-time stress monitoring tool, offering a significant contribution to preventive healthcare through early detection and intervention.
Prayitno et al. (Mon,) studied this question.