Design and implementation of an internet of things enabled stress level detection system using fuzzy logic method for enhanced accuracy and real-time monitoring

Abstract

Stress can affect individuals of all ages, from the young to the elderly, leading to a compromised immune system and increased susceptibility to illness. This study addresses this issue by developing an internet of things (IoT) based stress level detection system utilizing fuzzy logic methods. The device measures multiple physiological parameters and processes the data using an ESP32 microcontroller. This allows individuals to monitor and understand their stress levels efficiently and automatically through a liquid crystal display (LCD) display and Android devices. The system integrates various sensors to capture vital signs such as heart rate (HR), respiration rate, and body temperature. These readings are then analyzed using its algorithms to determine the stress level, which is displayed on both the onboard LCD and the connected Android device via an IoT interface. This real-time feedback mechanism empowers users to take proactive measures in stress management. Testing and validation of the device were conducted by comparing its readings with the depression anxiety stress scales (DASS-42) test results. The comparison showed an 80% correlation, demonstrating the device’s accuracy and reliability in detecting stress levels. This innovative approach leverages the advantages of IoT and fuzzy logic to provide a practical and effective solution for stress monitoring and management.