TY - JOUR
T1 - A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot
AU - Perez Reynoso, Francisco D.
AU - Niño Suarez, Paola A.
AU - Aviles Sanchez, Oscar F.
AU - Calva Yañez, María B.
AU - Vega Alvarado, Eduardo
AU - Portilla Flores, Edgar A.
N1 - Publisher Copyright:
© Copyright © 2020 Perez Reynoso, Niño Suarez, Aviles Sanchez, Calva Yañez, Vega Alvarado and Portilla Flores.
PY - 2020/9/29
Y1 - 2020/9/29
N2 - Although different physiological signals, such as electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally the systems must adapt to the user parameters. Therefore, in this work the use of a multilayer neural network (MNN) to model the EOG signal as a mathematical function is presented, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person to calibrate the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration performed every 3 min; if an assistance system is not calibrated, it loses functionality. Artificial intelligence techniques, such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve, and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space (X, Y, Z) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.
AB - Although different physiological signals, such as electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally the systems must adapt to the user parameters. Therefore, in this work the use of a multilayer neural network (MNN) to model the EOG signal as a mathematical function is presented, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person to calibrate the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration performed every 3 min; if an assistance system is not calibrated, it loses functionality. Artificial intelligence techniques, such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve, and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space (X, Y, Z) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.
KW - EOG
KW - HMI
KW - MNN
KW - customization calibration
KW - optimization
KW - robots trajectories
UR - http://www.scopus.com/inward/record.url?scp=85092519929&partnerID=8YFLogxK
U2 - 10.3389/fnbot.2020.578834
DO - 10.3389/fnbot.2020.578834
M3 - Artículo
AN - SCOPUS:85092519929
SN - 1662-5218
VL - 14
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
M1 - 578834
ER -