- Home
- Archives
- 2026
- Vol. 6 Issue 2
- Article
Empowering Women’s Safety with Hand Sign- Based Communication
Authors
1 2 3 4 5 Department of Computer Science & Engineering, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India. 6 Assistant Professor, Computer Science Engineering, Dhanekula Institute of Engineering and Technology, Andhra Pradesh, India.
Article Information
- Published In
- Volume 6, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 337-343
- Article Type
- Review Article
- Check for updates
-
References
1. R. Kaur et al., “Real-Time Distress Gesture Recognition Using Compact CNN with Twilio API and LLM-Based Adaptive Response,” Int. J. AI Safety Syst., vol. 14, no. 2, pp. 87–101, 2025.
2. S. Patel and A. Menon, “OpenPose-Based Gesture Detection for Mobile Surveillance Robots in Public Safety Applications,” IEEE Trans. Robot. Autom., vol. 41, no. 3, pp. 213–226, 2025.
3. P. Sinha and K. Roy, “CNN-Driven Wearable Safety Device with Wireless Distress Alert and Haptic Feedback Mechanisms,” J. Embedded Syst. Safety Eng., vol. 9, no. 1, pp. 34–48, 2023.
4. C. Lugaresi et al., “MediaPipe: A Framework for Building Perception Pipelines,” arXiv: 1906.08172, 2019.
5. G. Bradski and A. Kaehler, “Learning OpenCV: Computer Vision with the OpenCV Library,” O’Reilly Media, Sebastopol, CA, 2008.
6. L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
7. C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
8. T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, 1967.
9. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
10. F. Zhang et al., “MediaPipe Hands: On-Device Real-Time Hand Tracking,” arXiv: 2006.10214, 2020.
11. Duarte, S. Palaskar, L. Ventura, D. Ghadiyarm, K. DeHaan, F. Metze, J. Torres, and X. Giro-i-Nieto. “How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language”. In: Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
12. Graves, A.-r. Mohamed, and G. E. Hinton. “Speech Recognition with Deep Recurrent Neural Networks”. In: CoRR abs/1303.5778 (2013).
13. H. Brashear, T. Starner, P. Lukowicz, and H. Junker. “Using multiple sensors for mobile sign language recognition”. In: Nov. 2005, pp. 45–52. isbn: 0-76952034-0. doi: 10.1109/ISWC.2003.1241392. [
14. D. Uebersax, J. Gall, M. van den Bergh, and L. V. Gool. “Real-time sign language letter and word recognition from depth data”. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (2011), pp. 383–390.
15. S. A. Mehdi and Y. N. Khan. “Sign language recognition using sensor gloves”. In: Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02. 5 (2002), 2204–2206 vol.5.
16. Z. Zafrulla, H. Brashear, T. Starner, H. Hamilton, and P. Presti. “American sign language recognition with the kinect”. In: Proceedings of the 13th international conference on multimodal interfaces. 2011, pp. 279–286.
17. R. Sutton-Spence and B. Woll. The Linguistics of British Sign Language: An Introduction. Cambridge University Press, 1999. isbn: 9781107494091.
18. P. Boyes-Braem, R. Sutton-Spence, and R. te Leiden. The Hands are the Head of the Mouth: The Mouth as Articulator in Sign Languages. International studies on sign language and the communication of the deaf. Gallaudet University Press, 2001.
19. O. M. Sincan, J. C. S. J. Junior, S. Escalera, and H. Y. Keles. ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research. 2021. arXiv: 2105.05066 [cs.CV].
20. R. L. McKinley and J. W. Rohrer, "A Machine Learning Approach to American Sign Language Recognition," in IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 42, no. 6, pp. 1069-1078, Nov. 2012, doi: 10.1109/TSMCC.2011.2167896.
21. M. A. Garg and S. Kumar, "ASL Recognition Using Machine Learning and Computer Vision Techniques," in IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICECCT.2017.8117854.
2. S. Patel and A. Menon, “OpenPose-Based Gesture Detection for Mobile Surveillance Robots in Public Safety Applications,” IEEE Trans. Robot. Autom., vol. 41, no. 3, pp. 213–226, 2025.
3. P. Sinha and K. Roy, “CNN-Driven Wearable Safety Device with Wireless Distress Alert and Haptic Feedback Mechanisms,” J. Embedded Syst. Safety Eng., vol. 9, no. 1, pp. 34–48, 2023.
4. C. Lugaresi et al., “MediaPipe: A Framework for Building Perception Pipelines,” arXiv: 1906.08172, 2019.
5. G. Bradski and A. Kaehler, “Learning OpenCV: Computer Vision with the OpenCV Library,” O’Reilly Media, Sebastopol, CA, 2008.
6. L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
7. C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
8. T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, 1967.
9. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
10. F. Zhang et al., “MediaPipe Hands: On-Device Real-Time Hand Tracking,” arXiv: 2006.10214, 2020.
11. Duarte, S. Palaskar, L. Ventura, D. Ghadiyarm, K. DeHaan, F. Metze, J. Torres, and X. Giro-i-Nieto. “How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language”. In: Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
12. Graves, A.-r. Mohamed, and G. E. Hinton. “Speech Recognition with Deep Recurrent Neural Networks”. In: CoRR abs/1303.5778 (2013).
13. H. Brashear, T. Starner, P. Lukowicz, and H. Junker. “Using multiple sensors for mobile sign language recognition”. In: Nov. 2005, pp. 45–52. isbn: 0-76952034-0. doi: 10.1109/ISWC.2003.1241392. [
14. D. Uebersax, J. Gall, M. van den Bergh, and L. V. Gool. “Real-time sign language letter and word recognition from depth data”. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (2011), pp. 383–390.
15. S. A. Mehdi and Y. N. Khan. “Sign language recognition using sensor gloves”. In: Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02. 5 (2002), 2204–2206 vol.5.
16. Z. Zafrulla, H. Brashear, T. Starner, H. Hamilton, and P. Presti. “American sign language recognition with the kinect”. In: Proceedings of the 13th international conference on multimodal interfaces. 2011, pp. 279–286.
17. R. Sutton-Spence and B. Woll. The Linguistics of British Sign Language: An Introduction. Cambridge University Press, 1999. isbn: 9781107494091.
18. P. Boyes-Braem, R. Sutton-Spence, and R. te Leiden. The Hands are the Head of the Mouth: The Mouth as Articulator in Sign Languages. International studies on sign language and the communication of the deaf. Gallaudet University Press, 2001.
19. O. M. Sincan, J. C. S. J. Junior, S. Escalera, and H. Y. Keles. ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research. 2021. arXiv: 2105.05066 [cs.CV].
20. R. L. McKinley and J. W. Rohrer, "A Machine Learning Approach to American Sign Language Recognition," in IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 42, no. 6, pp. 1069-1078, Nov. 2012, doi: 10.1109/TSMCC.2011.2167896.
21. M. A. Garg and S. Kumar, "ASL Recognition Using Machine Learning and Computer Vision Techniques," in IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICECCT.2017.8117854.
Related Articles
Share Article
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.