Passive workplace stress monitoring through analysis of computer mouse dynamics

Abstract Stress is a growing issue in modern workplaces, affecting mental well-being, productivity, and health. This paper explores the use of computer mouse movement patterns as non-invasive behavioral indicators of psychological stress. The study presents a system for detecting stress levels based on mouse interaction data. An experimental task was designed to induce different stress levels. The collected data were used to train machine learning models, several of which achieved high classification accuracy. The results demonstrate the potential of mouse-based behavioral analysis as a practical approach to real-time workplace stress detection. This research contributes to the development of accessible stress monitoring tools and highlights the value of behavioral data analysis for improving occupational health.