Stress Detection in It Professionals by Image Processing and Machine Learning

Abstract

Abstract: The project aims to leverage image processing and machine learning techniques for the detection of stress in IT professionals. The primary focus is on monitoring the emotional well-being of individuals who spend extended periods working in front of a computer, with the goal of identifying and alleviating stress to create a more conducive work environment. Key objectives include predicting stress based on observed symptoms, assessing stress levels in employees, and delivering pertinent information. Previous research in stress detection for IT professionals has employed machine learning and image processing, prioritizing secure monitoring through captured images of authenticated users. These systems analyze stress levels using standard conversion and image processing methods, incorporating live detection and periodic analysis. In contrast to earlier systems, they provide personalized counseling and solutions for managing both physical and mental stress levels, integrating surveys for regular assessments. Furthermore, existing systems may incorporate digital signal processing, taking into account factors such as Galvanic skin response, blood volume, pupil dilation, and skin temperature. Alternatively, they may rely on a variety of physiological signals and visual features to monitor stress levels during work.