Artificial Intelligence – Powered Stress Monitoring and Adaptive Solution

Stress is a major problem today, especially among young people. Life once considered carefree is now filled with stress, causing illnesses like depression, heart attacks, and suicide. In this work, It is working to predict stress levels among IT professionals and students based on their facial expressions, focusing on groups that often experience high stress. Our goal is to detect stress in real time by analyzing facial features like eye movement, tension in mouth, position of eyebrows, and tension in muscles. It can use a real-time camera output to capture such a face expression, and these are analyzed through convolutional neural networks (CNNs)—a kind of model trained to recognize patterns in stress in face images. The dataset for training includes facial images showing different stress levels. By using CNNs, the system can tell the difference between relaxed and stressed states. Real-time image processing allows the system to quickly respond, making it effective in active environments like workplaces and schools. After the system detects stress, it offers suggestions to help reduce it. If a user has mild stress, they may be recommended to try short breathing exercises, while someone with high stress may be advised to take a longer break or meditate. A number of simple and effective methods for reducing stress are based on psychological research on stress management. In this work, image processing and machine learning are combined to provide a non- intrusive, real-time solution for stress management. Creating healthier, more productive environments can be made easier by early stress detection. In this system, users are able to manage their stress in real-time through facial analysis to support mental health.