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Computational Intelligence and Neuroscience
Volume 2016, Article ID 3054258, 6 pages
Research Article

User Adaptive Text Predictor for Mentally Disabled Huntington’s Patients

1Automation Department, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentų g. 50-154, LT-51368 Kaunas, Lithuania
2Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology, Studentų g. 50-414a, LT-51368 Kaunas, Lithuania

Received 3 December 2015; Accepted 2 February 2016

Academic Editor: Marcin Woźniak

Copyright © 2016 Julius Gelšvartas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper describes in detail the design of the specialized text predictor for patients with Huntington’s disease. The main aim of the specialized text predictor is to improve the text input rate by limiting the phrases that the user can type in. We show that such specialized predictor can significantly improve text input rate compared to a standard general purpose text predictor. Specialized text predictor, however, makes it more difficult for the user to express his own ideas. We further improved the text predictor by using the sematic database to extract synonym, hypernym, and hyponym terms for the words that are not present in the training data of the specialized text predictor. This data can then be used to compute reasonable predictions for words that are originally not known to the text predictor.