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Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an

Francisco David Pérez-Reynoso1, Liliam Rodríguez-Guerrero2, Julio César Salgado-Ramírez3

  • 1Mechatronic Engineering, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico.

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Summary
This summary is machine-generated.

This study introduces an electrooculography (EOG)-based Human-Machine Interface (HMI) to aid individuals with severe disabilities. The EOG HMI enables control of devices, like an omnidirectional robot, through eye movements, offering a customizable and intuitive assistive technology.

Keywords:
EOGmachine learningomnidirectional robotone hot encoding

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Area of Science:

  • Biomedical Engineering
  • Assistive Technology
  • Machine Learning

Background:

  • Individuals with severe disabilities often require external assistance for daily activities.
  • Human-Machine Interfaces (HMIs) can empower users to control devices and regain independence.
  • Electrooculography (EOG) offers a non-invasive method for detecting eye movements, suitable for HMI development.

Purpose of the Study:

  • To develop and validate a portable EOG-based HMI for individuals with severe disabilities.
  • To implement machine learning algorithms for accurate classification of eye movements and blinks.
  • To demonstrate real-time control of an omnidirectional robot using the developed EOG HMI.

Main Methods:

  • Acquisition of horizontal and vertical EOG signals using portable glasses.
  • Classification of eye movements using machine learning algorithms and one-hot encoding.
  • Real-time signal processing for controlling a three-wheeled omnidirectional robot.

Main Results:

  • Demonstrated the feasibility of classifying eye movement signals in real time.
  • Successfully controlled an omnidirectional robot's trajectory using gaze orientation via the EOG HMI.
  • Achieved discrimination of blinks to prevent interference with gaze control signals.

Conclusions:

  • The developed EOG-based HMI provides a promising solution for assistive technology.
  • Real-time signal classification and customizable interface minimize user learning curves.
  • Future applications include gaze-controlled positional assistance systems for enhanced user independence.