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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.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
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.
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.
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