STRESS DETECTION FOR IT PROFESSIONAL BY IMAGE PROCESSING AND MACHINE LEARNING

Abstract

Stress has become a significant health concern among IT professionals due to long working hours, tight deadlines, continuous computer usage, and high mental workload. Prolonged stress can negatively affect both physical and psychological well-being, leading to reduced productivity and increased health risks. Therefore, early detection of stress is essential to maintain employee performance and workplace wellness. This project proposes a stress detection system for IT professionals using image processing and machine learning techniques to automatically identify stress levels through facial expressions and behavioral patterns.The proposed system captures facial images using a webcam and processes them through image preprocessing techniques such as face detection, normalization, and feature extraction. Important facial features like eye movement, lip position, and facial muscle variations are analyzed to detect emotional changes related to stress. Machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN) are applied to classify stress levels accurately. The system performs realtime monitoring and provides early alerts when stress levels exceed a threshold.This approach helps organizations monitor employee well-being non-invasively and supports timely intervention strategies such as counseling or workload adjustment. Compared to traditional questionnaire-based methods, the proposed system provides faster, more objective, and continuous stress assessment. Thus, the implementation of image processing and machine learning-based stress detection systems can significantly contribute to improving workplace productivity, employee health, and overall organizational efficiency