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A Prototype Model of Hand Assistive System Useful for Hearing Impaired

  • J. Divya UdayanEmail author
  • Anupama K. Ingale
  • R. Hemalatha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Science and technology have made human life addictive to comfort yet at the same time there exists an underprivileged gathering of individuals who are battling to find a creative way that can make the procedure of correspondence less demanding for them. As per the World Health Organization, around 285 million individuals on the planet are visually impaired, 300 million are deprived of hearing and 1 million are moronic. In this paper, we propose a framework which encourages dump to speak with others with the intention to avoid any hindrance during communication between the blind, deaf and dumb individuals. This work utilizes wearable technology to devise methods for attaining our goal. Here we assume that a person who is deprived of hearing is also moronic.

Keywords

Gesture recognition Wearable technology Voice recognition Embedded system 

References

  1. 1.
    Padmanabhan, V., Sornalatha, M.: Hand gesture recognition and voice conversion system for dumb people. Int. J. Sci. Eng. Res. 5(5) (2014)Google Scholar
  2. 2.
    Sharma, D., Vora, K., Shukla, S.: Hand assistive device for deaf and dumb people. Int. J. Adv. Res. 5(10), 1042–1046 (2017)CrossRefGoogle Scholar
  3. 3.
    Arsan, T., Ulgen, O.: Sign language converter. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 6, 4 (2015)Google Scholar
  4. 4.
    Higgins, E.L., Zvi, J.C.: Assistive technology for postsecondary students with learning disabilities: from research to practice. Ann. Dyslexia 45, 123–143 (1995)CrossRefGoogle Scholar
  5. 5.
    Wetzel, K.: Speech-recognizing computers: a written communication tool for students with learning disabilities. J. Learn. Disabil. 29(4), 371–380 (1996)CrossRefGoogle Scholar
  6. 6.
    Schreier, E.M., Levanthal, D.D., Uslan, M.M.: Access technology for blind and visually impaired persons. Technol. Disabil. 1(1), 19–23 (1991)Google Scholar
  7. 7.
    Voice Recognition for Blind Computer Users. http://www.abilitynet.org.uk/content/factsheets/pdfs/Voice_Recognition_for_Blind_Computer_Users_PDF. Accessed 25 Feb 2012
  8. 8.
    Dail, P.W.: Prime-time television portrayals of older adults in the context of family life. Gerontologist 28, 700–706 (1988)CrossRefGoogle Scholar
  9. 9.
    Mark, H., Rebecca, P., Peter, O.: A voice-input voice-output communication aid for people with severe speech impairment. IEEE Trans. Neural Syst. Rehabil. Eng. 21(1), 1534–4320 (2013)Google Scholar
  10. 10.
    Amarjot, S., Devinder, K., Phani, S., Srikrishna, K., Niraj, A.: An intelligent multi-gesture spotting robot to assist persons with disabilities. Int. J. Comput. Theory Eng. 4(6), 1290–1420 (2012)Google Scholar
  11. 11.
    Otiniano, R., Amara, C.: Finger spelling recognition from RGB-D information using kernel descriptor. IEEE Trans. Neural Syst. Rehabil. Eng. 28(8), 124–184 (2006)Google Scholar
  12. 12.
    Alois, F., Stefan, R., Clemens, H., Martin, R.: Orientation sensing for gesture-based interaction with smart artefacts. IEEE Trans. Audio Speech Lang. Process. 28(8), 1434–1520 (2017)Google Scholar
  13. 13.
    Zhengmao, Z., Prashan, R., Monaragala, N., Malin, P.: Dynamic hand gesture recognition system using moment invariants. IEEE Trans. Neural Network. Comput. 21(1), 1034–1320 (2010)Google Scholar
  14. 14.
    Mohandes, M., Buraiky, S.A., Halawani, T., Al-Baiyat, S.: Automation of the arabic sign language recognition. In: International Conference on Information and Communication Technology (ICT04), pp. 479–480 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • J. Divya Udayan
    • 1
    Email author
  • Anupama K. Ingale
    • 1
  • R. Hemalatha
    • 1
  1. 1.School of Information Technology and EngineeringVITVelloreIndia

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