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Assistive computer technologies for people with disabilities

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EN
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The paper examines modern ways to improve the efficiency of information perception using multimedia technology, thus ensuring the use of a powerful new tool for understanding of information by persons with various forms of nosology. The use of assistive computer technologies for people with disabilities is investigated. The peculiarities of formation of multimedia information content for users with special needs were analyzed. The digital library as a set of information technology services is proposed making multimedia information content accessible to users with various forms of nosology. It was investigated that in spite of rapid development of modern communication means and assistive technologies their use is limited for people with disabilities, in particular for hearing impaired people. Most of available technologies for hearing impaired people translate speech into text. As was shown this approach is not fairly efficient because Sign language turned out to be the most convenient way of communication for the respondents.
Twórcy
autor
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
autor
  • Lviv Polytechnic National University
Bibliografia
  • 1. Simpson J. 2009. Inclusive Information and Communication Technologies for People with Disabilities.Disability Studies Quarterly, Vol. 29, No.1. Access mode: http://dsq-sds.org/article/view/167/167.
  • 2. Davydov M., Lozynska O. 2016. Linguistic models of assistive computer technologies for cognition and communication. In Proceedings of XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, September 6-10, pp. 171–174.
  • 3. Pasichnyk V., Kunanets N., Malynovskyi O., 2013. Digital Libraries for Disabled Persons. In Proceedings of 7th Internacional conference on Intelligent data acquistion and advanced computing systems (IDAACS), Berlin, Germany, September 12-14, pp. 212–215.
  • 4. Project Vcom3D. 2007. Vcommunicator signing avatar. Access mode: http://www.vcom3d.com/
  • 5. Tomasco S. 2007. IBM Media Relations IBM Research Demonstrates Innovative 'Speech to Sign Language' Translation System. Access mode: http://www-03.ibm.com/press/us/en/pressrelease/22316.wss.
  • 6. Ouhaddi H., Horain P. 1999. 3D hand gesturetracking by model registration. In Workshop on Synthetic-Natural Hybrid Coding and Three Dimensional Imaging, pp. 70–73.
  • 7. Voskresenskii A. L. 2003. Сomputer bank of sign languages. In Proceedings of International Conference Dialogue'2003, Moscow, pp. 688-691 [in Russian].
  • 8. Krak Iu. V., Kryvonos Iu. H., Barmak O. V., Ternov A. S. 2007. Information technology for nonverbal communication of deaf people.Artificial intelligence,Vol. 3, pp. 325-331 [in Ukrainian].
  • 9. Smith H. 2013. Krown Sign Language Translator. Access mode: http://madaportal.org/assistive-technologies/pocket-sign-language-translator.
  • 10. Cartwright B. 2017. Signing Savvy. Access mode: https://www.signingsavvy.com/
  • 11. Tran J. J., Kim J., Chon J., Riskin E., Ladner R., Wobbrock J. O. 2011. Evaluating quality and comprehension of real-time sign language video on mobile phones. In Proceedings of ASSETS, pp. 115–122.
  • 12. Huseyinov I. N. 2012. Fuzzy Linguistic Modelling Computer Interaction: Adaptation to Cognitive Styles using Multi Level Fuzzy Granulation Method. In: U. Shanker, T. J. Siddiqui (Eds.), Speech, Image, and Language Processing for Human Computer Interaction: Multi-Modal Advancements, IGI Global, pp. 1481-1496.
  • 13. Cercone N. J., Naruedomkul K. 2013. Computational Approaches to Assistive Technologies for People with Disabilities, Cercone, IOS Press, 276 p.
  • 14. Davydov M. V., Nikolski I. V., Pasichnyk V. V. 2010. Real-time Ukrainian sign language recognition system. In Proceedings of International Conference of Intelligent Computing and Intelligent Systems (ICIS), Xiamen, pp. 875-879.
  • 15. Davydov M. V., Lozytskyy O. A., Nikolski I. V. 2013. The method of sounding mathematical formulas and symbols in the Ukrainian language. Scientific works of the Petro Mohyla MDGU, Series: Computer Technologies, Vol. Issue 201, 2013, pp. 50–56 [in Ukrainian].
  • 16. Davydov M. V. 2013. Synthesis of virtual character visual articulation from audiostream for sign language translation.Periodical of LPNU, Computer Sciences and Information Technologies, Vol. 771, pp. 94–100 [in Ukrainian].
  • 17. Lozynska O., Davydov M. 2015. Information technology for Ukrainian Sign Language translation based on ontologies.An international quarterly journal Econtechmod, Vol. 4, Nо 2, Lublin, pp. 13–18.
  • 18. Shlykova O. V. 2004. Culture of multimedia: a manual for students,” in Proceedings of International Conference "Crimea", pp. 416 [in Russian].
  • 19. Richard E. M. 2005. The Cambridge Handbook of Multimedia Learning (Ed. ByRichard E. Mayer), New York: Cambridge University Press, 680 p.
  • 20. ReeseT., Banerjee K. 2008. Building digital libraries, New York:Neal-Schuman Publishers, 277 p.
  • 21. Watstein S. B., Calarco P. V., Ghaphery F. S. 1999. Didital library: keywords,” Reference Services Review, Vol. 27,№ 4, pp. 344–352.
  • 22. Sukiasyan E. R. 2000. Discussion Club “The term”. Scientific and technical libraries, Vol. 6, pp. 113–119 [in Russian].
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
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