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Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation
In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the comple...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471050/ https://www.ncbi.nlm.nih.gov/pubmed/34562933 http://dx.doi.org/10.3390/bios11090343 |
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author | Lin, Chin-Teng Jiang, Wei-Ling Chen, Sheng-Fu Huang, Kuan-Chih Liao, Lun-De |
author_facet | Lin, Chin-Teng Jiang, Wei-Ling Chen, Sheng-Fu Huang, Kuan-Chih Liao, Lun-De |
author_sort | Lin, Chin-Teng |
collection | PubMed |
description | In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications. |
format | Online Article Text |
id | pubmed-8471050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84710502021-09-27 Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation Lin, Chin-Teng Jiang, Wei-Ling Chen, Sheng-Fu Huang, Kuan-Chih Liao, Lun-De Biosensors (Basel) Article In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications. MDPI 2021-09-17 /pmc/articles/PMC8471050/ /pubmed/34562933 http://dx.doi.org/10.3390/bios11090343 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lin, Chin-Teng Jiang, Wei-Ling Chen, Sheng-Fu Huang, Kuan-Chih Liao, Lun-De Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title_full | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title_fullStr | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title_full_unstemmed | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title_short | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation |
title_sort | design of a wearable eye-movement detection system based on electrooculography signals and its experimental validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471050/ https://www.ncbi.nlm.nih.gov/pubmed/34562933 http://dx.doi.org/10.3390/bios11090343 |
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