Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation.
- Publisher:
- MDPI AG
- Publication Type:
- Journal Article
- Citation:
- Biosensors, 2021, 11, (9), pp. 1-28
- Issue Date:
- 2021-09-17
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Lin, C-T | |
dc.contributor.author | Jiang, W-L | |
dc.contributor.author | Chen, S-F | |
dc.contributor.author | Huang, K-C | |
dc.contributor.author | Liao, L-D | |
dc.date.accessioned | 2022-03-31T05:47:23Z | |
dc.date.available | 2021-09-14 | |
dc.date.available | 2022-03-31T05:47:23Z | |
dc.date.issued | 2021-09-17 | |
dc.identifier.citation | Biosensors, 2021, 11, (9), pp. 1-28 | |
dc.identifier.issn | 2079-6374 | |
dc.identifier.issn | 2079-6374 | |
dc.identifier.uri | http://hdl.handle.net/10453/155786 | |
dc.description.abstract | 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. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | MDPI AG | |
dc.relation.ispartof | Biosensors | |
dc.relation.isbasedon | 10.3390/bios11090343 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | 0301 Analytical Chemistry, 0601 Biochemistry and Cell Biology | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Electrooculography | |
dc.subject.mesh | Eye Movements | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Signal Processing, Computer-Assisted | |
dc.subject.mesh | User-Computer Interface | |
dc.subject.mesh | Wearable Electronic Devices | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Electrooculography | |
dc.subject.mesh | Eye Movements | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Signal Processing, Computer-Assisted | |
dc.subject.mesh | User-Computer Interface | |
dc.subject.mesh | Wearable Electronic Devices | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Eye Movements | |
dc.subject.mesh | Electrooculography | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Signal Processing, Computer-Assisted | |
dc.subject.mesh | User-Computer Interface | |
dc.subject.mesh | Wearable Electronic Devices | |
dc.title | Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation. | |
dc.type | Journal Article | |
utslib.citation.volume | 11 | |
utslib.location.activity | Switzerland | |
utslib.for | 0301 Analytical Chemistry | |
utslib.for | 0601 Biochemistry and Cell Biology | |
pubs.organisational-group | /University of Technology Sydney | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | /University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute | |
pubs.organisational-group | /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science | |
utslib.copyright.status | open_access | * |
pubs.consider-herdc | false | |
dc.date.updated | 2022-03-31T05:47:15Z | |
pubs.issue | 9 | |
pubs.publication-status | Published | |
pubs.volume | 11 | |
utslib.citation.issue | 9 |
Abstract:
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.
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