  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.
