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

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...