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Tytuł:
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Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation.
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Autorzy:
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Lin CT; Australia Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia.
Jiang WL; Institute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.; Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Chen SF; Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli City 35053, Taiwan.
Huang KC; Institute of Electrical Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.; Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Liao LD; Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan Township, Miaoli City 35053, Taiwan.
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Źródło:
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Biosensors [Biosensors (Basel)] 2021 Sep 17; Vol. 11 (9). Date of Electronic Publication: 2021 Sep 17.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI Pub.
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MeSH Terms:
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Electrooculography*
Wearable Electronic Devices*
Algorithms ; Eye Movements ; Humans ; Movement ; Signal Processing, Computer-Assisted ; User-Computer Interface
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References:
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Front Hum Neurosci. 2018 Mar 21;12:96. (PMID: 29618975)
Micromachines (Basel). 2020 Jul 10;11(7):. (PMID: 32664268)
IEEE Trans Rehabil Eng. 2000 Mar;8(1):107-17. (PMID: 10779114)
IEEE J Transl Eng Health Med. 2014 Nov 06;2:2700307. (PMID: 27170884)
Sensors (Basel). 2017 Jun 23;17(7):. (PMID: 28644398)
J Healthc Eng. 2018 Jan 15;2018:5081258. (PMID: 29599950)
J Med Eng Technol. 2000 Jul-Aug;24(4):154-62. (PMID: 11105288)
IEEE Trans Biomed Eng. 2011 May;58(5):1200-7. (PMID: 21193371)
Appl Opt. 1985 Feb 15;24(4):527. (PMID: 18216982)
IEEE Trans Pattern Anal Mach Intell. 2011 Apr;33(4):741-53. (PMID: 20421675)
J Neural Eng. 2019 Apr;16(2):026021. (PMID: 30620927)
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Contributed Indexing:
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Keywords: blink; electrooculography; eye-movement detection; fixation; human–computer interface; saccade
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Entry Date(s):
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Date Created: 20210925 Date Completed: 20211104 Latest Revision: 20211104
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Update Code:
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20240105
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PubMed Central ID:
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PMC8471050
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DOI:
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10.3390/bios11090343
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PMID:
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34562933
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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.
