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SteadEye-Head-Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data.

Lukas Wöhle1, Marion Gebhard1

  • 1Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, 45877 Gelsenkirchen, Germany.

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Summary
This summary is machine-generated.

This study integrates eye tracking with Magnetic AngularRate Gravity (MARG) sensors for accurate head orientation estimation, even with magnetic interference. This novel approach enhances motion measurement accuracy in applications like robotics and medical diagnostics.

Keywords:
IMUMARGdata fusioneye trackerhead motion measurementself-contained

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Sensor Fusion

Background:

  • Magnetic AngularRate Gravity (MARG) sensors are crucial for motion measurement but susceptible to magnetic field disturbances in indoor environments.
  • Accurate head orientation estimation is vital for applications such as rehabilitation robotics and medical diagnostics.
  • Existing methods often struggle with magnetic interference, limiting their reliability.

Purpose of the Study:

  • To present a novel method for head orientation estimation using eye tracking data to augment MARG-sensor data.
  • To improve the accuracy and robustness of MARG-sensor based motion measurement in the presence of magnetic disturbances.
  • To develop a self-contained system that does not require external stationary or environmental references.

Main Methods:

  • Integration of eye tracking (visual fixations) with MARG-sensor data fusion.
  • Implementation of an online visual fixation detection algorithm.
  • Utilizing a dynamic angular rate threshold for adaptive noise parameterization.
  • Adaptation of Madgwick's gradient descent filter for MARG-sensor data fusion.

Main Results:

  • Eye tracking data successfully enabled zero orientation change updates in the MARG-sensor data fusion.
  • The proposed system demonstrated improved heading accuracy by up to a factor of 0.5 compared to standard MARG fusion under magnetic disturbance.
  • The system was benchmarked against a Qualisys motion capture system, a gold standard in motion analysis.

Conclusions:

  • The integration of eye tracking data offers a robust solution for enhancing head orientation estimation using MARG sensors in challenging environments.
  • This self-contained approach improves motion measurement accuracy and reliability, particularly in indoor settings with magnetic interference.
  • The method holds significant potential for advancing applications in rehabilitation robotics, medical diagnostics, and other motion analysis fields.