Mental and cognitive fatigue have become more common with prolonged computer use or prolonged sitting in front of smart device screens in modern digital environments, significantly impacting concentration, individual performance, and overall health. This comprehensive review covers the scientific literature published from 2012 to 2025, examining AI-based approaches for detecting mental and cognitive fatigue using webcam data and nonverbal behavioral cues. Recent innovations in artificial intelligence and computer vision have led to the design of intelligent systems capable of detecting fatigue through behavioral and visual cues. This literature review examines some studies on AI-generated fatigue detection, focusing on approaches that use webcams to analyze nonverbal behavior. It also highlights the main trends in detection methods—physiological, visual, and dual—with an emphasis on the growing importance of deep learning models and multi-source data fusion in improving the efficiency and accuracy of detection. Research indicates a significant evolution from traditional feature extraction methods to intelligent network models that automatically learn features, which can recognize subtle indicators of fatigue such as eye blink rate, head movement and direction, and subtle facial expressions. This study represents a fundamental step towards building a sophisticated intelligent system based on nonverbal behavior analysis that can continuously analyze nonverbal behavior to detect fatigue during actual computer use.
This review aims to analyze and compare AI-based approaches for detecting mental and cognitive fatigue through nonverbal behavior and computer vision techniques, including recent methodological progress and important discoveries to inform future research directions.