<![CDATA[Stress has become a major global health concern affecting both physical and mental well-being. Conventional stress assessment methods rely on subjective self-reports that cannot capture real-time physiological changes. Existing systems are often limited to controlled laboratory environments or depend on traditional machine learning approaches requiring extensive manual feature engineering. This study aims to develop an Internet of Things–based stress monitoring system using deep learning to enable objective, continuous, and practical real-world stress detection. The system incorporates wearable sensors using an ESP32-DevKit V1 microcontroller, a MAX30102 photoplethysmography sensor, and a Grove-GSR module for real-time acquisition of Heart Rate Variability and Galvanic Skin Response signals. A dual-branch Convolutional Neural Network architecture processes preprocessed HRV and GSR time-series data to automatically learn discriminative features without manual feature engineering. Data were collected from 30 participants, resulting in 8,100 labeled samples across four stress levels. The proposed CNN model achieved 91.3% classification accuracy, outperforming baseline machine learning models such as Support Vector Machine (78.4%), Random Forest (81.7%), and XGBoost (84.3%). Real-time system evaluation demonstrated an average latency of 1.47 seconds and battery endurance exceeding 13 hours, confirming the feasibility of continuous wearable stress monitoring. The integration of IoT infrastructure with deep learning provides an effective framework for physiological stress assessment, offering potential applications in preventive healthcare, workplace health management, and personalized mental-wellness monitoring.]]>
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