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Research Article

An Air Gesture Based Keyboard for Visually Impaired People Using Machine Learning and Embedded System Approach

  • Paper ID 206
  • Volume 5, Issue 3
  • Pages 223-229
  • Published Online: May-June 2024
  • 3 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • Hemashree M1
  • Kavitha B C2
  • Leena Ranganath3
  • Suhas4

1234 Dept. of ECE, BGS Institute of Technology, Adichunchanagiri University, B G Nagara, Karnataka, India.

Article Information

Published Online
May-June 2024
Pages
223-229
Article Type
Research Article
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No DOI available for cross-reference

References

1. Clark, J. (2018). Keyboard Layouts and Their Influence on Typing Efficiency. *Journal of Computer Science, 12*(6), 244-256. DOI:
10.3844/jcssp.2018.244.256
2. Smith, A., & Johnson, B. (2022). Enhancing Text Input withRemote-Based Gesture Keyboards. *Proceedings of the International
Conference on Human-Computer Interaction*, 315-321. DOI: 10.1007/978-3-319-95276-3_32
3. Rodriguez, M., et al. (2021). Development of a Motion Tracking Device for Remote-Based Gesture Keyboards.*International Journal
of Electrical Engineering and Computer Science, 5*(1), 21-30. DOI:10.15627/jee.2021.0002
4. Wang, Y., & Chen, Z. (2019). Remote-Based Gesture Keyboard: A Novel Approach for Text Input. *Journal of Human-Computer
Interaction, 35*(2), 127-140. DOI:10.1080/07370024.2018.1566789
5. Shakunthaladevi, M., & Revathi, R. B. (n.d.). Real-time hand gesture recognition using AVR Microcontroller. *SKP Engineering
College*, Tiruvannamalai, India.
6. Jaramillo, G., & Benalcázar, M. E. (2017). Real-time hand gesture recognition with EMG using machine learning.*Proceedings of
the IEEE ETCM*, 1-5. DOI:10.1109/ETCM.2017.8247487
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