Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63879

GESTURE BASED TOOL FOR DISABLED USING AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63879

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63879

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

RBN

Renuka B N

Master of Computer Application

Bapuji Institute of Engineering and Technology, Davanagere

Download Author Certificate
VRM

VIJAYLAXMI RAJANNA MIRAJAKAR

Master of Computer Application

Bapuji Institute of Engineering and Technology, Davanagere

Download Co-Author Certificate

Author Services & Downloads

Full ArticlesAcceptance Certificate

Abstract

The current project, "Gesture Based Tool For Disabled Using AI" Gesture-based totally verbal exchange structures play a essential function in helping speech- and hearing-impaired people by way of permitting non-verbal interaction through visual signals. This undertaking proposes an AI-based totally gesture verbal exchange tool the use of MediaPipe and system getting to know techniques to recognize and interpret hand gestures in actual time. The device captures live video input via a digicam and makes use of MediaPipe’s hand landmark detection version to extract particular spatial coordinates of hand joints. these landmarks are used as feature inputs to a trained type model that correctly identifies predefined gestures. once a gesture is identified, the machine converts it into corresponding textual content or audio output, enabling seamless conversation with non-disabled users. The proposed approach gets rid of the need for wearable sensors or outside hardware, making the system cost-effective and person-pleasant. The model is designed to function successfully under actual-time constraints whilst keeping high accuracy. This AI-pushed solution may be deployed in instructional institutions, healthcare environments, and public service areas, contributing to inclusive communication and assistive technology development.

KEYWORDS

Keywords: Sign language information retrieval computer vision natural language processing accessibility deaf individuals.

Cite This Article

Renuka B N, VIJAYLAXMI RAJANNA MIRAJAKAR, "GESTURE BASED TOOL FOR DISABLED USING AI," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026. https://doi.org/10.55041/IJSREM63879

Full Article/Conclusion

In conclusion, the AI-based gesture tool for disabled persons is a simple, effective, and user-friendly solution to improve communication between impaired people and society. The system uses artificial intelligence and computer vision to recognize hand movements and translate them into real-time actions or messages. This builds confidence, independence, and lessens dependence. The proposed system is cost-effective, scalable, and customizable with speech output, language support, and mobile or IoT integration, making it a useful assistive tool for inclusive communication.

References

[1]  Starner, T., Pentland, A., & Weiser, M. (1998). Real-time American sign language recognition from video using hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1371-1375. [DOI: 10.1109/34.735807]   [2]  Huenerfauth, M., Kuber, R., & Savage, J. (2013). Signglasses: A sign language translation system using mobile devices. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-8). ACM. [DOI: 10.1145/2513383.2513442]   [3]  Sutton-Spence, R., & Woll, B. (1999). The Linguistics of British Sign Language: An Introduction. Cambridge University Press.   [4]  Camgoz, N. C., Hadfield, S., Koller, O., & Bowden, R. (2018). Neural sign language translation. Computer Vision and Image Understanding, 169, 150-159. [DOI: 10.1016/j.cviu.2018.01.006]   [5]  World Wide Web Consortium (W3C). (2018). Web Content Accessibility Guidelines (WCAG) 2.1. Retrieved from https://www.w3.org/TR/WCAG21/   [6]  Stoll, C., Nickchen, T., Aein, M. J., & Cremers, D. (2020). Sign language recognition using 3D neural networks. arXiv preprint arXiv:2003.09011.

Editorial & Review Transparency

Publication Date

5/24/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

3

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...