Smart Stress Detection: Integrating Deep Learning and Health Sensors for Real-Time Monitoring

Stress detection plays a critical role in promoting mental and physical well-being. This project introduces an advanced deep learning-based system that integrates facial emotion recognition with health parameter analysis to identify and respond to stress indicators. A Convolutional Neural Network (CNN) classifies facial emotions such as happiness, sadness, anger, or neutrality through video or image input. If negative emotions like sadness or anger are detected, the system automatically evaluates health parameters—including body temperature, SpO2, heart rate, respiration rate, and sweat levels—via sensors connected to an Arduino. Based on detected abnormalities, the system sends alerts to the individual or concerned authorities, ensuring timely intervention. This hybrid solution utilizing an Arduino and laptop combination offers an efficient and scalable approach to real-time stress detection.