Page 165 - 2024-Vol20-Issue2
P. 165
161 | Murad & Alasadi
XI. CONCLUSION [6] R. F. Pinto Jr, C. D. Borges, A. M. Almeida, and I. C.
Paula Jr, “Static hand gesture recognition based on con-
The review emphasizes the importance and challenges of hand volutional neural networks,” Journal of Electrical and
gesture recognition in various fields, such as human-computer Computer Engineering, vol. 2019, no. 1, p. 4167890,
interaction, sign language recognition, virtual reality, gam- 2019.
ing, and robotics. It explores different approaches, such as
vision-based, sensor or data glove-based, and colored-marker [7] A. K. H. AlSaedi and A. H. H. AlAsadi, “A new hand
techniques, and their advantages and limitations. Accurate gestures recognition system,” Indonesian journal of elec-
hand modeling and feature extraction are crucial for capturing trical engineering and computer science, vol. 18, no. 1,
and analyzing hand gestures. Machine learning algorithms pp. 49–55, 2020.
are essential for classifying and recognizing gestures based
on extracted features. Challenges in hand gesture recognition [8] P. Das, T. Ahmed, and M. F. Ali, “Static hand gesture
include lighting variations, complex backgrounds, noise, and recognition for american sign language using deep con-
real-time performance. The review acknowledges the need volutional neural network,” in 2020 IEEE Region 10
for further research and advancements to improve hand ges- symposium (TENSYMP), pp. 1762–1765, IEEE, 2020.
ture recognition systems’ robustness, accuracy, and usability.
The review provides valuable insights into the current state of [9] I. Papastratis, C. Chatzikonstantinou, D. Konstantinidis,
hand gesture recognition, its applications, and the potential for K. Dimitropoulos, and P. Daras, “Artificial intelligence
enhancing human-computer interaction and communication technologies for sign language,” Sensors, vol. 21, no. 17,
between different communities. p. 5843, 2021.
CONFLICT OF INTEREST [10] L. I. Khalaf, S. A. Aswad, S. R. Ahmed, B. Makki, and
M. R. Ahmed, “Survey on recognition hand gesture by
The authors have no conflict of relevant interest to this article. using data mining algorithms,” in 2022 International
Congress on Human-Computer Interaction, Optimiza-
REFERENCES tion and Robotic Applications (HORA), pp. 1–4, IEEE,
2022.
[1] “Webster’s dictionary accessed: 12-oct-2022.”
[11] M. Oudah, A. Al-Naji, and J. Chahl, “Hand gesture
[2] T. Zhang, Y. Ding, C. Hu, M. Zhang, W. Zhu, C. R. recognition based on computer vision: a review of tech-
Bowen, Y. Han, and Y. Yang, “Self-powered stretch- niques,” Journal of Imaging, vol. 6, no. 8, p. 73, 2020.
able sensor arrays exhibiting magnetoelasticity for real-
time human–machine interaction,” Advanced Materials, [12] D. V. Suma, “Computer vision for human-machine
vol. 2203786, p. 2203786, 2022. interaction-review,” Journal of Trends in Computer Sci-
ence and Smart Technology, vol. 1, no. 2, pp. 131–139,
[3] F. A. Farid, N. Hashim, J. Abdullah, M. R. Bhuiyan, 2019.
W. N. S. M. Isa, J. Uddin, M. A. Haque, and M. N.
Husen, “A structured and methodological review on [13] T. Vuletic, A. Duffy, L. Hay, C. McTeague, G. Campbell,
vision-based hand gesture recognition system,” Journal and M. Grealy, “Systematic literature review of hand ges-
of Imaging, vol. 8, no. 6, p. 153, 2022. tures in human-computer interaction interfaces,” Inter-
national Journal of Human-Computer Studies, vol. 129,
[4] M. G. A. J. P. Rawat, L. Kane and S. Sehgal, “A re- pp. 74–94, 2019.
view on vision-based hand gesture recognition targeting
rgb-depth sensors,” International Journal of Informa- [14] H. Kawashima, Active Appearance Models, pp. 1–5.
tion Technology and Decision Making, vol. 22, no. 01, 2020.
pp. 115–156, 2023.
[15] T. H. Tsai, C. C. Huang, and K. L. Zhang, “Design of
[5] S. Wu, Z. Li, S. Li, Q. Liu, and W. Wu, “An overview hand gesture recognition system for human-computer
of gesture recognition,” in International Conference on interaction,” Multimedia tools and applications, vol. 79,
Computer Application and Information Security (IC- pp. 5989–6007, 2020.
CAIS 2022), vol. 12609, pp. 600–606, SPIE, Mar 2023.
[16] T. L. Dang, H. T. Nguyen, D. M. Dao, H. V. Nguyen,
D. L. Luong, B. T. Nguyen, S. Kim, and N. Monet,
“Shape: a dataset for hand gesture recognition,” Neural
Computing and Applications, vol. 34, pp. 21849–21862,
Dec 2022.