COGCAM: Contact-free Measurement of Cognitive Stress During Computer Tasks with a Digital Camera
Open accessAbstract
Contact-free camera-based measurement of cognitive stress opens up new possibilities for human-computer interaction with applications in remote learning, stress monitoring, and optimization of workload for user experience. The autonomic nervous system controls the inter-beat intervals of the heart and breathing patterns, and these signals change under cognitive stress. We built a participant-independent cognitive stress recognition model based on photoplethysmographic signals measured remotely at a distance of 3 meters. We tested the model on naturalistic responses from 10 individuals completing randomized-order computer-based tasks (ball control and card sorting). The system successfully detected increased stress during the tasks, which were consistent with self-report measures. Changes in heart rate variability were more discriminative indicators of cognitive stress than were heart rate and breathing rate.
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Previous research has shown that various eye movements can indicate increased cognitive demand or derive the user proficiency during interaction.
“However, previous research has shown that various eye movements can also indicate increased cognitive demand [63,131,133] or derive the user proficiency during interaction [151].”
From A Survey on Measuring Cognitive Workload in Human-Computer Interaction (2023) · cites this paper as [151]
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Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Self-citationIqbal, S. T., & Bailey, B. P. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1056948 · Cited by 203 · see citing works
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A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304 · see citing works
“…Smooth Pursuits and Ocular Movements [133] Cardiovascular Heart Rate [4, 10, 25, 27, 40, 41, 49, 50, 76, 78, 82, 85, 87, 99, 106, 146, 152, 160, 169, 184, 185, 194, 201, 202, 214, 216, 217, 224, 225, 227, 240, 244, 264, 264, 266, 268] Respiration [87, 99, 106, 184, 185, 194] Temperature [1, 2,…”
Nowara, E. M., McDuff, D., & Veeraraghavan, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00150 · Cited by 66 · see citing works
Self-citationIn AI We Trust: Investigating the Relationship between Biosignals, Trust and Cognitive Load in VR
Gupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., & Billinghurst, M. (2019) · 25th ACM Symposium on Virtual Reality Software and Technology · doi:10.1145/3359996.3364276 · Cited by 59 · see citing works
- Authors
- McDuff, D. J., Hernandez, J., Gontarek, S., & Picard, R. W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2016-05-07
- Pages
- 4000-4004
- OpenAlex
- W2394865193
- Cited by
- 113
- References
- 2
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 9 · 2020: 17 · 2019: 15 · 2018: 19 · 2017: 16 · 2016: 4
- FWCI
- 20.82
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, heart rate variability, respiratory rate, heart rate, autonomic nervous system, workload optimization, human-computer interaction, card sorting
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