Fatigue Induced

Fatigue research spans diverse fields, aiming to understand and predict fatigue's impact on human performance and material durability. Current investigations utilize machine learning models, including neural networks, Gaussian state-space models, and XGBoost, to analyze various data sources such as EEG signals, thermal facial images, and even handwriting characteristics, to detect and quantify fatigue levels. These advancements have significant implications for improving safety in transportation and industry, optimizing rehabilitation strategies, and enhancing the design of durable materials and user interfaces. The development of accurate and real-time fatigue detection methods holds considerable promise for improving human well-being and technological efficiency.