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Home Automation Using Hand Recognition for the Visually Challenged

  • Ammannagari Vinay Kumar
  • Adityan Jothi
  • Adil Hashim
  • Pulivarthi Narender
  • Mayank Kumar Goyal
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 771)

Abstract

Human–computer interaction has spawned much research in recent years, which has greatly reduced the complexity which existed in interactions between humans and computers. The visually challenged have benefited through systems resulting from this research. This paper aims at developing a system that allows the visually challenged to interact with the electronic systems in their vicinity. The objective of this paper is to overcome the shortcomings identified in previous systems and enhance access for the visually challenged. The paper describes two submodules whose integration allows the visually challenged to effortlessly interact with electronic devices around them.

Keywords

Hand gesture recognition Human computer interaction Home automation Arduino Raspberry pi 

References

  1. 1.
    Sulfayanti, Dewiani and Armin Lawi. 2016. A real time alphabets sign language recognition system using hands tracking. In 2016 international conference on computational intelligence and cybernetics, 69–72, Makassar.Google Scholar
  2. 2.
    Islam, M.M., S. Siddiqua and J. Afnan. 2017. Real time Hand Gesture Recognition using different algorithms based on American Sign Language. In 2017 IEEE international conference on imaging, vision & pattern recognition (icIVPR), 1–6, Dhaka.Google Scholar
  3. 3.
    Kartika,. D.R., R. Sigit and Setiawardhana. 2016. Sign language interpreter hand using optical-flow. In 2016 international seminar on application for technology of information and communication (ISemantic), 197–201, Semarang.Google Scholar
  4. 4.
    Kumarage, D., S. Fernando, P. Fernando, D. Madushanka and R. Samarasinghe. 2011. Real-time sign language gesture recognition using still-image comparison & motion recognition. In 2011 6th International Conference on Industrial and Information Systems, 169–174, Kandy.Google Scholar
  5. 5.
    Hatami, N., P. Prinetto and G. Tiotto. 2010. Sign language synthesis using hand motion acquisition. In 2010 East-West Design & Test Symposium (EWDTS), 226–229, St. Petersburg.Google Scholar
  6. 6.
    Natesh, A., G. Rajan, B. Thiagarajan and V. Vijayaraghavan. 2017. Low-cost wireless intelligent two hand gesture recognition system. In 2017 annual IEEE international systems conference (SysCon), 1–6, Montreal, QC.Google Scholar
  7. 7.
    Fan, Y.C., and H. K. Liu. 2015. Three-dimensional gesture interactive system design of home automation for physically handicapped people. In 2015 IEEE international symposium on medical measurements and applications (MeMeA) proceedings, 432–435, Turin.Google Scholar
  8. 8.
    Starner, T., J. Auxier, D. Ashbrook and M. Gandy. 2000. The gesture pendant: A self-illuminating, wearable, infrared computer vision system for home automation control and medical monitoring. Digest of Papers. In: Fourth international symposium on wearable computers, 87–94, Atlanta, GA, USA.Google Scholar
  9. 9.
    Chandra, M., and B. Lall. 2016. A novel method for low power hand gesture recognition in smart consumer applications. In 2016 international conference on computational techniques in information and communication technologies (ICCTICT), 326–330, New Delhi.Google Scholar
  10. 10.
    Chou, P.H., et al. 2017. Development of a smart home system based on multi-sensor data fusion technology. In 2017 international conference on applied system innovation (ICASI), 690–693, Sapporo.Google Scholar
  11. 11.
    Hung, C.H., Y.W. Bai and H.Y. Wu. 2016. Home outlet and LED array lamp controlled by a smartphone with a hand gesture recognition. In 2016 IEEE international conference on consumer electronics (ICCE), 5–6, Las Vegas, NV, 2016.Google Scholar
  12. 12.
    Fang, W.P. 2012. An intelligent hand gesture extraction and recognition system for home care application. In 2012 sixth international conference on genetic and evolutionary computing, 457–459, Kitakushu.Google Scholar
  13. 13.
    Irie, K., M. Wada and K. Umeda. 2007. 3D measurement by distributed camera system for constructing an intelligent room. In 2007 fourth international conference on networked sensing systems, 118–121, Braunschweig.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ammannagari Vinay Kumar
    • 1
  • Adityan Jothi
    • 1
  • Adil Hashim
    • 1
  • Pulivarthi Narender
    • 1
  • Mayank Kumar Goyal
    • 1
  1. 1.Amity UniversityNoidaIndia

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