ArticleFrontiers in neurorobotics2020
A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- GMM-HMM-Based Eye Movement Classification for Efficient and Intuitive Dynamic Human-Computer Interaction Systems.Journal of eye movement research · 2025Article
- Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.Sensors (Basel, Switzerland) · 2024Article
- A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.Frontiers in robotics and AI · 2024Article
- EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.Sensors (Basel, Switzerland) · 2023Article
- Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
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 (
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