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Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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Research on Eye-Tracking Control Methods Based on an Improved YOLOv11 Model.

Xiangyang Sun1, Jiahua Wu1, Wenjun Zhang2

  • 1Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Ministry of Education), Changchun University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study enhances eye-tracking accuracy for medical rehabilitation by improving the YOLOv11 model with EFFM and ORC modules. Enhanced eye movement detection enables precise control of robotic arms for rehabilitation tasks.

Keywords:
eye tracking codingeye tracking controlhuman–computer interactionobject detection

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Rehabilitation Technology

Background:

  • Eye-tracking is a non-invasive rehabilitation tool.
  • Current methods lack accuracy in eye detection and movement analysis.

Purpose of the Study:

  • Improve eye-tracking accuracy using enhanced YOLOv11.
  • Develop precise robotic arm control via eye movements.

Main Methods:

  • Enhanced YOLOv11 with EFFM and ORC modules for eye socket and iris recognition.
  • Frame voting and eye movement area discrimination for direction detection.
  • Eye movement encoding for robotic arm control command generation.

Main Results:

  • Improved recognition accuracy for eye socket (1.7%) and iris (9.9%).
  • Increased recall rates for eye socket (5.5%) and iris (44%).
  • High accuracy in eye movement direction discrimination (92.8%-95.3%) and robotic arm control (93.4%-96.8%).
  • Successful object-grabbing task completion rates (78%-98%) using eye-controlled robotic arm.

Conclusions:

  • The enhanced eye-tracking system offers improved accuracy and reliability.
  • This technology shows significant potential for advanced medical rehabilitation and human-computer interaction.