Real-Time Stress Detection for IT Employees Using Deep Learning and Image Processing

The “Real-Time Stress Detection Using Deep Learning and Image Processing” identifies IT professionals stress levels from subtle face image assessments that occur at unexpected times. Employees perform registration and login activities through the web interface designed in Flask to access the stress monitoring solution. Deep learning analysis detects emotions such as anger, sadness, happiness, fear, neutrality, disgust, and surprise, through which high, medium, and low stress level classifications are generated. Unannounced image recording procedures prevent workers from manipulating their reactions because they cannot predict when the pictures will be acquired. CNN serves as the fundamental technology in this approach because it evaluates pictures to identify refined facial expression modifications. Facial movements, starting from eye position to mouth placement, reveal the current stress levels of test subjects. Detected stress levels are processed in real-time to create custom alerts that notify employees. Instant notifications show stress status ranging from high to low to medium while providing guidance for coping with stress. The combination of deep learning and image processing provides stress level data while simultaneously preventing employee burnout at work. Employee engagement in monitoring mental health is encouraged, building a healthier occupational atmosphere for everyone. The objective stays focused on raising employee performance and mental wellness by giving workers visibility of their emotional condition and stress management tools that operate inside their work environment.