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.

翻译：本文介绍了MOCAS——一个专门用于人类认知负荷评估的多模态数据集。与现有基于虚拟游戏刺激的数据集不同，MOCAS的数据采集自真实闭路电视监控任务，从而提升了其在现实场景中的适用性。为构建MOCAS，研究采用两个商用可穿戴传感器与一个网络摄像头，从21名受试者处采集生理信号与行为特征。每项任务结束后，参与者通过填写NASA任务负荷指数量表与瞬时自我评估量表报告其认知负荷水平。通过人口统计学问卷与大五人格问卷收集个人背景信息（如人格特质与既往经验），同时采用自我评估曼尼克量表获取唤醒度与愉悦度两个维度的主观情绪信息，这些信息可作为提升认知负荷识别性能的潜在指标。技术验证结果表明，在同步CCTV监控任务中成功诱发了目标认知负荷水平，其结果支持所采集多模态信号的高质量特性。