CALM: Cognitive Assessment using Light-insensitive Model
Akhil Meethal, Anita Paas, Nerea Urrestilla Anguiozar, David St-Onge

TL;DR
This paper demonstrates that combining pupillometry with heart rate variability data enhances the robustness and accuracy of cognitive load assessment under varying lighting conditions, using both clinical and low-cost equipment.
Contribution
It introduces a multimodal approach that reduces light sensitivity in cognitive load estimation and validates the use of fitness-grade devices as effective alternatives.
Findings
Multimodal data improves robustness to lighting changes.
Cognitive load estimation accuracy increases by over 20%.
Fitness-grade devices perform comparably to clinical-grade equipment.
Abstract
The demand for cognitive load assessment with low-cost easy-to-use equipment is increasing, with applications ranging from safety-critical industries to entertainment. Though pupillometry is an attractive solution for cognitive load estimation in such applications, its sensitivity to light makes it less robust under varying lighting conditions. Multimodal data acquisition provides a viable alternative, where pupillometry is combined with electrocardiography (ECG) or electroencephalography (EEG). In this work, we study the sensitivity of pupillometry-based cognitive load estimation to light. By collecting heart rate variability (HRV) data during the same experimental sessions, we analyze how the multimodal data reduces this sensitivity and increases robustness to light conditions. In addition to this, we compared the performance in multimodal settings using the HRV data obtained from…
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Taxonomy
TopicsColor perception and design
