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A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification

arXiv 2303.12757 ↗First posted 3 Mar 2023cs.CVcited by 0

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Abstract (arXiv)

In this paper, we present a new feature that can be used to classify cognitive load based on pupil information. The feature consists of a temporal segmentation of the eye tracking recordings. For each segment of the temporal partition, a probability distribution of pupil size is computed and stored. These probability distributions can then be used to classify the cognitive load. The presented feature significantly improves the classification accuracy of the cognitive load compared to other statistical values obtained from eye tracking data, which represent the state of the art in this field. The applications of determining Cognitive Load from pupil data are numerous and could lead, for example, to pre-warning systems for burnouts. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FCognitiveLoadFeature&mode=list

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 60 references that are in the map. First posted on arXiv 3 Mar 2023.

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6 of 60 references.

References · 6

  1. 2014Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interactioncited by 43
  2. 2016PupilNet: Convolutional Neural Networks for Robust Pupil Detectioncited by 20
  3. 2015ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environmentscited by 16
  4. 2017Fast camera focus estimation for gaze-based focus controlcited by 10
  5. 2021A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysiscited by 7
  6. 2022HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genarationcited by 5

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