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Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems
Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806888/ https://www.ncbi.nlm.nih.gov/pubmed/29425248 http://dx.doi.org/10.1371/journal.pone.0192684 |
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author | Fang, Fuming Shinozaki, Takahiro |
author_facet | Fang, Fuming Shinozaki, Takahiro |
author_sort | Fang, Fuming |
collection | PubMed |
description | Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. |
format | Online Article Text |
id | pubmed-5806888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58068882018-02-23 Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems Fang, Fuming Shinozaki, Takahiro PLoS One Research Article Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. Public Library of Science 2018-02-09 /pmc/articles/PMC5806888/ /pubmed/29425248 http://dx.doi.org/10.1371/journal.pone.0192684 Text en © 2018 Fang, Shinozaki http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Fang, Fuming Shinozaki, Takahiro Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title | Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title_full | Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title_fullStr | Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title_full_unstemmed | Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title_short | Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
title_sort | electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5806888/ https://www.ncbi.nlm.nih.gov/pubmed/29425248 http://dx.doi.org/10.1371/journal.pone.0192684 |
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