MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks

• Published in: IEEE transactions on affective computing Vol. 16; no. 1; pp. 116 - 132
• Main Authors: Jo, Wonse, Wang, Ruiqi, Cha, Go-Eum, Sun, Su, Senthilkumaran, Revanth Krishna, Foti, Daniel, Min, Byung-Cheol
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Arousal; Behavioral sciences; Biomedical monitoring; Closed circuit television; cognitive workload assessment; Datasets; Human-machine systems; human-robot teams; Monitoring; multimodal dataset; Personality; Self assessment; Self evaluation; Sensors; Task analysis; Taskload; Workload; Workloads
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2024.3414330

This paper presents MOCAS , a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS , two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self-Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin (SAM), which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks; its results support the high quality of the collected multimodal signals.