IoT Based Model Bridge Between Deaf and Mute Community with Normal People

Authors

  • Maharshi Vivekanand School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Shrinivas R G School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Piyush Kumar School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Rahul C G School of Computer Science and Engineering, REVA University, Bengaluru, India
  • Naveen Chandra Gowda School of Computer Science and Engineering, REVA University, Bengaluru, India

DOI:

https://doi.org/10.5281/zenodo.10254235

Keywords:

Embedded system, flex sensor, sign language, Bluetooth Module

Abstract

 

This paper delves into the analysis of fundamental components of sign language using embedded systems, a crucial exploration aimed at facilitating the development of an extensive vocabulary encompassing the factorial of 10. The primary emphasis is on shedding light on an unaddressed issue within the realm of sign language communication. By leveraging embedded systems, we seek to provide an innovative solution that not only enhances the understanding of basic sign language elements but also enables the creation of an expansive vocabulary. The research underscores the significance of addressing this unexplored challenge, emphasizing the potential impact on effective communication for the deaf and mute community. Through a meticulous examination of sign language components and the utilization of embedded systems, this paper contributes to bridging gaps in communication accessibility, paving the way for a more inclusive and comprehensive linguistic framework.

References

Axisa, F., Gehin, C., Delhomme, G., Collet, C., Robin, O., & Dittmar, A. (2004, September). Wrist ambulatory monitoring system and smart glove for real time emotional, sensorial and physiological analysis. In The 26th annual international conference of the IEEE engineering in medicine and biology society (Vol. 1, pp. 2161-2164). IEEE.

Chouhan, T., Panse, A., Voona, A. K., & Sameer, S. M. (2014, September). Smart glove with gesture recognition ability for the hearing and speech impaired. In 2014 IEEE Global Humanitarian Technology Conference-South Asia Satellite (GHTC-SAS) (pp. 105-110). IEEE.

Patel, N., Jethwa, N., Mali, C., & Deone, J. (2022). Deepfake Video Detection using Neural Networks. In ITM Web of Conferences (Vol. 44, p. 03024). EDP Sciences.

Dipietro, L., Sabatini, A. M., & Dario, P. (2008). A survey of glove-based systems and their applications. Ieee transactions on systems, man, and cybernetics, part c (applications and reviews), 38(4), 461-482.

Ahmed, S. S. T., Thanuja, K., Guptha, N. S., & Narasimha, S. (2016, January). Telemedicine approach for remote patient monitoring system using smart phones with an economical hardware kit. In 2016 international conference on computing technologies and intelligent data engineering (ICCTIDE'16) (pp. 1-4). IEEE.

Al-Shammari, N. K., Syed, T. H., & Syed, M. B. (2021). An Edge–IoT framework and prototype based on blockchain for smart healthcare applications. Engineering, Technology & Applied Science Research, 11(4), 7326-7331.

Downloads

Published

2023-12-04

How to Cite

Maharshi Vivekanand, Shrinivas R G, Piyush Kumar, Rahul C G, & Naveen Chandra Gowda. (2023). IoT Based Model Bridge Between Deaf and Mute Community with Normal People. International Journal of Computational Learning & Intelligence, 2(4), 148–154. https://doi.org/10.5281/zenodo.10254235

Issue

Vol. 2 No. 4 (2023): October

Section

RESEARCH ARTICLES

License

Copyright (c) 2023 Maharshi Vivekanand, Shrinivas R G, Piyush Kumar, Rahul C G, Naveen Chandra Gowda

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CC Attribution-NonCommercial-NoDerivatives 4.0