IoT-Based Mental Health Monitoring Headband using ECG, PPG, and GSR

Abstract

At the moment, the world is experiencing an increase in case of mental health disorder such as stress, anxiety and mental exhaustion caused by academic  and work-related issues, in addition to lifestyle changes. Conventional mental health state monitoring methods are either subjective and objective nature and involve methods such as assessments. However, with recent innovations in wearable technologies and IoT systems, it has become possible to develop wearable devices that could continuously monitor physiological parameters associated with mental health status.

In this project, we suggested the design and implementation of an IoT-Based Mental Health Monitoring Headband using various sensors such as Electrocardiogram (ECG), Photoplethysmogram (PPG), and Galvanic Skin Response (GSR) to monitor stress levels and mental health states. The proposed system includes an ESP32 Microcontroller to conduct real-time experiments on the extraction of physiological features such as heart rate variability (HRV), pulse characteristics, and electrodermal responses using various machine learning techniques to classify mental states such as relaxed, neutral, and stressed.

The proposed system can be considered cost-effective, non-invasive, and wearable, thus enabling the users to have a comfortable experience while being monitored at the same time. The results from the experimental tests conducted in controlled environments have proved the possibility of the proposed system in the reliable acquisition of signals, as well as the differentiation of stressed and relaxing states. The proposed system proves the possibility of the Internet of Things in the preventive mental health of the user as well as personalized wellness.