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CIDER: Enhancing the Performance of Computational Eyeglasses.

Addison Mayberry1, Yamin Tun1, Pan Hu1

  • 1University of Massachusetts, Amherst.

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|April 10, 2018
PubMed
Summary
This summary is machine-generated.

We developed CIDER, a low-power eye-tracking system for continuous health and attention monitoring. It robustly tracks the eye in both indoor and outdoor conditions with high accuracy.

Keywords:
eye trackinglow-power sensingmHealthpupilometry

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Human-Computer Interaction

Background:

  • The human eye provides insights into health, attention, and decision-making.
  • Current methods lack continuous, real-world monitoring capabilities.

Purpose of the Study:

  • To develop a low-power, robust eye-tracking system (CIDER) for continuous monitoring in diverse environments.
  • To address power consumption and accuracy challenges in pupillary parameter estimation.

Main Methods:

  • CIDER utilizes a Search-Refine controller for efficient indoor eye tracking.
  • Adaptive model switching enables robust performance in challenging outdoor sunlight.
  • Near-infrared illumination and optimized pixel digitization minimize power usage.

Main Results:

  • CIDER achieves pupil center estimation error < 2 pixels (0.6°).
  • Pupil diameter estimation error is < 1 pixel (0.22mm).
  • Operates at ~7mW (4Hz) or ~32mW (250Hz), demonstrating significant power efficiency.

Conclusions:

  • CIDER offers a holistic solution for low-power, accurate eye tracking in natural environments.
  • The system's adaptability to lighting conditions enhances its real-world applicability.
  • Enables continuous, non-intrusive monitoring of physiological and cognitive states.