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Sensors
Volume 21
Issue 17
10.3390/s21175882
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Article

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

by
Francisco David Pérez-Reynoso
1,
Liliam Rodríguez-Guerrero
2,*,
Julio César Salgado-Ramírez
3,* and
Rocío Ortega-Palacios
3,*
1
Mechatronic Engineering, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico
2
Research Center on Technology of Information and Systems (CITIS), Electric and Control Academic Group, Universidad Autónoma del Estado de Hidalgo (UAEH), Pachuca de Soto 42039, Mexico
3
Biomedical Engineering, Universidad Politécnica de Pachuca (UPP), Zempoala 43830, Mexico
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(17), 5882; https://doi.org/10.3390/s21175882
Submission received: 29 July 2021 / Revised: 24 August 2021 / Accepted: 26 August 2021 / Published: 31 August 2021
(This article belongs to the Collection Survey on Research of Sensors and Robot Control)
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Abstract

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.
Keywords: EOG; one hot encoding; machine learning; omnidirectional robot EOG; one hot encoding; machine learning; omnidirectional robot

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MDPI and ACS Style

Pérez-Reynoso, F.D.; Rodríguez-Guerrero, L.; Salgado-Ramírez, J.C.; Ortega-Palacios, R. Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors 2021, 21, 5882. https://doi.org/10.3390/s21175882

AMA Style

Pérez-Reynoso FD, Rodríguez-Guerrero L, Salgado-Ramírez JC, Ortega-Palacios R. Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors. 2021; 21(17):5882. https://doi.org/10.3390/s21175882

Chicago/Turabian Style

Pérez-Reynoso, Francisco David, Liliam Rodríguez-Guerrero, Julio César Salgado-Ramírez, and Rocío Ortega-Palacios. 2021. "Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot" Sensors 21, no. 17: 5882. https://doi.org/10.3390/s21175882

APA Style

Pérez-Reynoso, F. D., Rodríguez-Guerrero, L., Salgado-Ramírez, J. C., & Ortega-Palacios, R. (2021). Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot. Sensors, 21(17), 5882. https://doi.org/10.3390/s21175882

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