Real-time cognitive workload assessment using non-intrusive methods: a systematic review

Real-time cognitive workload (CWL) assessment has been used to enhance human performance and safety across various operational domains. This review synthesizes findings from 50 peer-reviewed studies to examine current practices, methodological trends, and technological advances in physiological and behavioural CWL monitoring. Studies utilized electrocardiography (ECG), photoplethysmography (PPG), electrodermal activity (EDA), eye-tracking, electroencephalography (EEG), and skin temperature (SKT). The use of wearable devices were predominant (∼74%). Task categorization into cognitive, perceptual, motor, and physical domains revealed alignment between physiological measures and task demands. Computational approaches favour traditional machine learning (32%) and statistical models (∼21%) over advanced deep learning models (∼14%). The use of hybrid approaches (∼22%), where multiple models are combined or applied in parallel rather than using a single model, suggests evolution towards adaptive frameworks for real-world implementation. The review offers guidelines on measurement and model selection based on task requirements and outlines future directions for real-world CWL system deployment.