Stress Detection in IT Professionals by Image Processing and Machine Learning

G.Manoj Kumar 1, Muchi Dharmendra 2

1Assistant Professor, 2 MCA Final Semester, Master of

Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

gantedamanojkumar@gmail.com,mucchidharmendra@gmail.com

ABSTRACT

The main motive of our project is to detect stress in IT professionals using advanced Machine Learning and Image Processing techniques. The system is designed to monitor the stress levels of employees in real time and provide accurate analysis based on their behavioral and facial patterns. Our project is an upgraded version of traditional stress detection systems that mainly focused only on basic analysis without live monitoring features. Unlike the existing systems, our proposed system includes live stress detection along with periodic mental health assessment. The system continuously analyzes facial expressions, eye movements, and other visual indicators using image processing algorithms. Machine learning models are trained to identify different levels of stress with higher accuracy and efficiency. The project also conducts periodic surveys to understand the emotional and psychological condition of employees. Based on the collected data, the system evaluates both physical and mental stress levels of an individual. Employees experiencing high stress levels are provided with proper remedies and stress management suggestions such as relaxation exercises, meditation, counseling sessions, and short breaks. This helps employees improve their mental well-being and maintain a balanced work life. The project mainly focuses on creating a healthy, positive, and productive working environment in IT organizations. By reducing stress levels, employees can improve their concentration, efficiency, and overall job performance. The system also helps management identify workplace stress trends and take preventive measures at an early stage. Our solution aims to increase employee satisfaction, reduce burnout, and encourage better teamwork within organizations.