CLUES: Cognitive Load Understanding through Experimental Sensing Dataset

Understanding Cognitive Load (CL) is essential to advance our knowledge of human cognitive resources and improve performance in demanding environments. Studying CL helps prevent overload and improve learning, productivity, and well-being in daily and professional life, which is particularly useful in the context of human computer interaction. This study presents a comprehensive multi-modal dataset from 55 participants across two sites, examining CL under relaxed, easy, and difficult task conditions. The dataset features synchronized, high-resolution recordings from multiple devices: Emteq OCOsense smartglasses for facial and head measurements; Empatica E4 wristband for heart rate variability, skin temperature and electrodermal activity; Tobii eye-tracker for gaze dynamics; and camera recordings (depth, RGB, and thermal) for behavioral and thermal measurements. Participants also provided subjective CL ratings, enabling richer analysis by integrating self-reports with sensor data. To validate data quality, the study provides statistical analyses of perceived difficulty and task performance. Machine learning experiments for CL estimation include person-dependent, person-independent, and task-independent models, highlighting the dataset's potential for assessing model generalization. Feature importance analysis across models identifies the most informative signals for CL modeling. This resource supports interdisciplinary research, enabling applications from CL modeling and machine learning personalization, to adaptive interface design and aims to advance CL assessment through robust, reproducible methods.