Dataset Open Access

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

Wonse Jo; Ruiqi Wang; Su Sun; Revanth Krishna Senthilkumaran; Daniel Foti; Byung-Cheol Min

This MOCAS is 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, 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.

This material is based upon work supported by the National Science Foundation under Grant No. IIS-1846221. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Files (19.0 GB)
Name Size
1_subjective_information.zip
md5:7b5983fd96f29cd213e3ccfa4aa36ae3 		5.6 kB Download
2_raw_dataset_rosbag2.zip
md5:9e70b81b16c66cbfeda50279dc18f4a4 		49.0 kB Download
3_downsampled_dataset.zip
md5:3cc70a5119f4e93b889895cf8e5f82ee 		18.7 GB Download
4_supplementary_code.zip
md5:20510b65837a0fabf93d2912ac1c74a6 		326.9 MB Download
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Publication date:
August 25, 2022
DOI:
10.5281/zenodo.7023242

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10.5281/zenodo.7023242

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Keyword(s):
affective dataset affective computing cognitive load stress rosbag2
Grants:
National Science Foundation:
  • CAREER: Adaptive Human Multi-Robot Systems (1846221)
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Creative Commons Attribution 4.0 International

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Version 2022.0.0 10.5281/zenodo.7023242 Aug 25, 2022
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