# Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

arXiv: https://arxiv.org/abs/2102.06690 · HTML: https://mlsanity.com/paper/2102.06690/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 82 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors](https://mlsanity.com/paper/1807.00966/) (2018)
- [Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging](https://mlsanity.com/paper/1901.00449/) (2018)
- [DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings](https://mlsanity.com/paper/1708.06026/) (2017)
- [Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging](https://mlsanity.com/paper/1705.06628/) (2017)
- [Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns](https://mlsanity.com/paper/1803.02310/) (2018)

## Built on by (most cited first, 3 of 3)

- [FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing](https://mlsanity.com/paper/2411.01542/) (2024)
- [EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration](https://mlsanity.com/paper/2604.23562/) (2026)
- [AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination](https://mlsanity.com/paper/2606.03774/) (2026)

## Abstract (arXiv)

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.