IoT-Enabled Smart Cap for Real-Time Stress Monitoring

This is the authors' abstract. We don't add key points for this paper.

Stress is a physiological and psychological response that can affect the well-being and daily functioning of an individual. In this study, we propose an IoT-enabled smart cap for real-time stress monitoring using multiple physiological signals. The system uses electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and pulse measurements to monitor physiological changes associated with varying levels of stress. The sensor data is captured and communicated to the Blynk Cloud platform using a Raspberry Pi 3 Model B. The acquired data is processed by a Node.js backend and also allows real-time display using a web-based dashboard. The physiological measurements are combined with a rule-based stress classification method. Stress is classified as mild, moderate, and severe. Depending on the detected stress level, the system provides appropriate relaxation and intervention suggestions through the monitoring interface. The proposed architecture demonstrates the feasibility of integrating multimodal physiological sensing with IoT-based monitoring in a wearable form factor. The system is intended to provide continuous, accessible, and real-time stress monitoring while supporting personalized intervention. Further validation using larger datasets, diverse participants, and data-driven machine learning or deep learning models is required to establish the accuracy, generalizability, and clinical applicability of the proposed system.