ArticleSensors (Basel, Switzerland)2021
Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Hybrid Adaptive Segmentation and Morphology-Based Classification of EOG for Automated Detection of Phasic and Tonic REM Sleep.Sensors (Basel, Switzerland) · 2026Article
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD).Entropy (Basel, Switzerland) · 2024Article
- Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism.Sensors (Basel, Switzerland) · 2023Article
- EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT.Sensors (Basel, Switzerland) · 2023Article
- Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human-Machine Interfaces.ACS applied electronic materials · 2023Article
- Advances in Materials, Sensors, and Integrated Systems for Monitoring Eye Movements.Biosensors · 2022Review
- Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.