Authors : Gaddala Finny Theophorus; Balwant Singh

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/y7yj5rbx

Scribd : https://tinyurl.com/mt8mrekd

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY2510

Abstract : " Intersign Glove " is a groundbreaking innovation designed to address the communication barriers faced by the deaf and mute community. With the aim of revolutionizing traditional methods of communication, this paper introduces an innovative approach that utilizes gesture recognition technology to interpret Indian Sign Language (ISL) in real-time. The paper endeavors to eliminate the dependency on sign language interpreters by creating an embedded system comprising both hardware and software components. By leveraging advanced sensors strategically placed on a glove, the system captures intricate gesture parameters and translates them into code for computation. This code is then processed by a microcontroller board integrated into the glove, which generates corresponding speech output through an attached speaker, facilitating seamless communication between the user and non-sign language speakers. The wearable nature of the device ensures portability and ease of use, empowering individuals with hearing and speech impairments to communicate effectively in various social settings. Furthermore, by bridging the communication gap between the deaf and mute community and the wider population, this paper opens up new opportunities for inclusion and collaboration. "Intersign Glove" represents a significant advancement in assistive technology, promising to enhance the quality of life for individuals with hearing and speech disabilities while fostering greater understanding and integration within society.

Keywords : Intersign Glove, Sign Language.

References :

[1].    Vasani, N., Autee, P., Kalyani, S., &Karani, R. (2020). Generation of Indian sign language by sentence processing and generative adversarial networks. Proceedings of the Third International Conference on Intelligent Sustainable Systems [ICISS 2020], 1250-1255. https://doi.org/10.1109/ICISS49785.2020. 9315979
[2].    Kahlon, N. K., &Singh, W. (2021). Machine translation from text to sign language: a systematic review. Universal Access in the Information Society, 1-36. https://doi.org/10.1007/s10209-021-00823-1
[3].    B. G. Lee and S. M. Lee, "Smart Wearable Hand Device for Sign Language Interpretation System With Sensors Fusion," in IEEE Sensors Journal, vol. 18, no. 3, pp. 1224-1232, 1 Feb.1, 2018, doi: 10.1109/JSEN.2017.2779466.
[4].    Sampada.S. Wazalwar & Urmila Shrawankar (2017) Interpretation of sign language into English using NLP techniques, Journal of Information and Optimization Sciences, 38:6, 895-910, DOI: 10.1080/02522667. 2017.1372136 
[5].    T. Yuan et al., "Large Scale Sign Language Interpretation," 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France, 2019, pp. 1-5, doi: 10.1109/FG.2019.8756506.
[6].    Paul, P., Bhuiya, M.AUA., Ullah, M.A., Saqib, M.N., Mohammed, N., Momen, S. (2019). A Modern Approach for Sign Language Interpretation Using Convolutional Neural Network. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_35 
[7].    Lee, B.G., Chung, W.Y. (2021). Study of Sign Language Recognition Using Wearable Sensors. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_24 
[8].    Golda Jeyasheeli P and Annapoorani K Miss 2019 J. Phys.: Conf. Ser. 1362 012034DOI 10.1088/1742-6596/1362/1/012034