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iShadow: Design of a Wearable, Real-Time Mobile Gaze Tracker.

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Researchers developed a low-power, real-time eye tracking system. This novel approach significantly reduces computational needs, enabling consumer applications by minimizing power consumption and device size.

Keywords:
eye trackinglifelogneural network

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Continuous, real-time eye gaze tracking is crucial for applications like hands-free interaction and behavior analysis.
  • Current eye trackers are power-hungry and computationally intensive, limiting consumer adoption.
  • Bridging the gap to everyday use requires significant reductions in power consumption and form-factor.

Purpose of the Study:

  • To design an eye tracker that drastically reduces sensing and computation requirements.
  • To achieve orders of magnitude reduction in power consumption and form-factor for eye tracking devices.
  • To enable widespread consumer use of real-time eye gaze tracking technology.

Main Methods:

  • Utilizing the redundancy in eye images to estimate gaze from a small subset of carefully chosen pixels per frame.
  • Developing a sparse pixel-based gaze estimation algorithm using a multi-layer neural network.
  • Implementing a prototype hardware platform with a low-power image sensor, microcontroller, and Bluetooth radio.

Main Results:

  • Continuous eye gaze estimation at 30 Hz with approximately 3-degree error.
  • Operation at a low power consumption of roughly 70mW.
  • Demonstration of a significantly reduced sensing and computation load for eye tracking.

Conclusions:

  • The proposed sparse pixel-based approach enables a new generation of low-power, compact eye trackers.
  • This technology has the potential to unlock numerous consumer applications previously limited by power and computational constraints.
  • Further development can lead to widespread adoption of eye tracking in everyday devices.