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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2023 - 8

A Study on Eye-Blink Detection-Based Communication System by Using K-Nearest Neighbors Classifier

EKIM, G. See more information about EKIM, G. on SCOPUS See more information about EKIM, G. on IEEExplore See more information about EKIM, G. on Web of Science, IKIZLER, N. See more information about  IKIZLER, N. on SCOPUS See more information about  IKIZLER, N. on SCOPUS See more information about IKIZLER, N. on Web of Science, ATASOY, A. See more information about ATASOY, A. on SCOPUS See more information about ATASOY, A. on SCOPUS See more information about ATASOY, A. on Web of Science
 
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Download PDF pdficon (1,252 KB) | Citation | Downloads: 850 | Views: 1,239

Author keywords
assistive technology, biomedical communication, biomedical signal processing, electroencephalography, machine learning

References keywords
brain(9), communication(7), interface(6), writing(5), system(5), efficient(5), typing(4), time(4), text(4), systems(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-02-28
Volume 23, Issue 1, Year 2023, On page(s): 71 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.01008
Web of Science Accession Number: 000937345700008
SCOPUS ID: 85150264613

Abstract
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There are 450 thousand patients in the world who is diagnosed Amyotrophic Lateral Sclerosis. These patients lose control of all the muscles in their body except the eye movements. The purpose of the studies in this research area is to design a system, which the patients can express the basic needs, works with high accuracy, which all patients can afford financially, with minimal discomfort to person. The aim of the developed system is to analyze the eye-blinks made with binary coding and convert them into sound. First, eye-blink signals coded with binary number sequence is obtained with NeuroSky MindWave Mobile device. These coded signals are given as an input to the designed system. In the eye-blink analysis section, the location and number of eye-blinks are determined. Then, the eye-blinks containing coded information in the input signal are classified using the K-Nearest Neighbors algorithm, and the class of the eye-blink is determined. Finally, the letters corresponding to the code of the eye-blink sequence are obtained and the resulting word is vocalized. With its low cost and high accuracy, this system causes minimal discomfort to the patient and has a simple structure.


References | Cited By  «-- Click to see who has cited this paper

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[3] A. Kaya, F. Ozcan, "Communication with patients with Amyotrophic Lateral Sclerosis (ALS) and current technologies." The Journal of Turkish Family Physician, 8.2: 43-48, 2017.
[CrossRef]


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[8] F. Akram, H. Seung Moo, K. Tae-Seong, "An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier," Computers in Biology and Medicine 56, 30-36, 2015.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 83]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 56]


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[14] C. Zhang, R. Yao, J. Cai," Efficient eye typing with 9-direction gaze estimation," Multimedia Tools, and Applications, 77.15: 19679-19696, 2018.
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[15] H. Ozbek Ulkutas, "Development of computer-based eye-writing system by using EOG," M.Sc. Thesis, Department of Biomedical Engineering, Baskent University, Ankara, Turkey, 2015.

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[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 80]


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[20] F. Fang, T. Shinozaki, "Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems," PloS one, 13(2), 2018, e0192684.
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[CrossRef]


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[28] P. Thanh Noi, M. Kappas, "Comparison of random forest, K-Nearest Neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery", Sensors, 18.1, 18, 2017.
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References Weight

Web of Science® Citations for all references: 3,846 TCR
SCOPUS® Citations for all references: 4,822 TCR

Web of Science® Average Citations per reference: 137 ACR
SCOPUS® Average Citations per reference: 172 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-10-10 06:44 in 135 seconds.




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