AI-Driven Cognitive Fatigue Detection in Remote Work Environment

Cognitive fatigue is a novel problem in work-at-home settings, as it is due to long-term exposure to the screen, multitasking, and constant contact with a computer or other devices. Compared to the body-related fatigue, cognitive fatigue builds up and can hardly be quantified using intrusive physiological sensors. In this paper, we introduce a non-invasive AI-driven cognitive fatigue monitoring system using behavioral analytics, including keyboard dynamics, mouse movement patterns, idle moments, and session time. The system incorporates Electron-based desktop monitoring to make real-time predictions of fatigue by relying on machine learning models such as Random Forest and Support Vector Machines. Dashboards, alerts, and report generation modules are also available in the system to use in practice. Experimental evaluation shows that the prediction accuracy is over 95%. The suggested solution can be applied in academic research and real-world deployments in remote work environments.