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

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