PupilNet, Measuring Task Evoked Pupillary Response using Commodity RGB Tablet Cameras: Comparison to Mobile, Infrared Gaze Trackers for Inferring Cognitive Load
Abstract
Pupillary diameter monitoring has been proven successful at objectively measuring cognitive load that might otherwise be unobservable. This paper compares three different algorithms for measuring cognitive load using commodity cameras. We compare the performance of modified starburst algorithm (from previous work) and propose two new algorithms: 2 Level Snakuscules and a convolutional neural network which we call PupilNet. In a user study with eleven participants, our comparisons show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. We show that the difference between PupilNet and a gold standard head-mounted gaze tracker varies only from -2.6% to 2.8%. Finally, we also show that PupilNet gives similar conclusions about cognitive load during a longer duration typing task.
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Phoneme recognition using time-delay neural networks
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“For instance, Beste et al. [54] developed a piano learning tool called BACh that could automatically adjust musical score sheet difficulty levels based on the cognitive load of a user.”
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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
- Authors
- Wangwiwattana, C., Ding, X., & Larson, E. C.
- Affiliations
- Southern Methodist University (US)
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2018-01-08
- Volume
- 1
- Issue
- 4
- Pages
- 1-26
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2782788244
- Cited by
- 26
- References
- 5
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 1 · 2022: 1 · 2021: 6 · 2020: 6 · 2019: 1 · 2018: 2
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- cognitive load, convolutional neural networks, eye tracking, typing task, illumination robustness
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