CFP last date
20 May 2025
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2025

Submit your paper
Know more
The week's pick
Responsive image
Exploring HCP Influence through Machine Learning: A Predictive Analysis of Lyumjev Prescribing Trends in the North Zone in India

Gourav Shairy Kamalpreet Kaur
Random Articles
Reseach Article

Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends

by Md Selim Sarowar, Nur E Jannatul Farjana, Md. Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, Mohaiminul Islam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 2
Year of Publication: 2025
Authors: Md Selim Sarowar, Nur E Jannatul Farjana, Md. Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, Mohaiminul Islam
10.5120/ijca2025924776

Md Selim Sarowar, Nur E Jannatul Farjana, Md. Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, Mohaiminul Islam . Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends. International Journal of Computer Applications. 187, 2 ( May 2025), 1-33. DOI=10.5120/ijca2025924776

@article{ 10.5120/ijca2025924776,
author = { Md Selim Sarowar, Nur E Jannatul Farjana, Md. Asraful Islam Khan, Md Abdul Mutalib, Syful Islam, Mohaiminul Islam },
title = { Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 2 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number2/hand-gesture-recognition-systems-a-review-of-methods-datasets-and-emerging-trends/ },
doi = { 10.5120/ijca2025924776 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:32.648668+05:30
%A Md Selim Sarowar
%A Nur E Jannatul Farjana
%A Md. Asraful Islam Khan
%A Md Abdul Mutalib
%A Syful Islam
%A Mohaiminul Islam
%T Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 2
%P 1-33
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hand gestures are a powerful method of communication that serve as a bridge between humans and computers, enabling intuitive interaction. Hand Gesture Recognition (HGR) systems aim to support this vision but face several challenges such as gesture irregularity, illumination variation, background interference, and computational complexity. This study evaluates 252 peer-reviewed articles published between 1995 and 2024, with a focus on input modalities, algorithmic approaches, benchmark datasets, application domains, and system-level challenges such as automation, scalability, generalization, and real-time performance.The evolution of HGR methods is categorized chronologically, beginning with early rulebased models, progressing through classical machine learning techniques such as SVM, KNN, and HMM, and advancing to deep learning frameworks including CNNs, RNNs, LSTMs, 3D CNNs, and Graph Convolutional Networks (GCNs). In recent years, hybrid and pretrained architectures including LSTM+3DCNN, MAE+STGCN, and Transformer-based models have been proposed to address existing limitations and improve performance. Various input modalities have been explored, including RGB image and video data, depth sensors, skeletal tracking, IMU, and EMG signals. Widely adopted benchmark datasets include SHREC, DHG- 14/28, and NVGesture. A temporal classification framework is introduced to segment the progression of HGR technologies across decades. The study highlights key trends, technological advancements, and unresolved challenges, offering insights that may guide the development of accurate, efficient, and user-centric HGR systems, particularly in mobile and embedded computing contexts.

References
  1. J. P. Sahoo, A. Jaya Prakash, P. Plawiak, and S. Samantray, “Real-time hand gesture recognition using fine-tuned convolutional neural network,” Jan. 2022. [Online]. Available: https://doi.org/10.3390/s22030706
  2. C. K. Tan, K. M. Lim, R. K. Y. Chang, C. P. Lee, and A. Alqahtani, “Hgr-vit: Hand gesture recognition with vision transformer,” Jan. 2023. [Online]. Available: https://doi.org/10.3390/s23125555
  3. K. O. Oyedotun and A. Khashman, “Deep learning in vision-based static hand gesture recognition,” Springer London. [Online]. Available: https: //doi.org/10.1007/s00521-016-2294-8
  4. J. Qi, L. Ma, Z. Cui, and Y. Yu, “Computer vision-based hand gesture recognition for human-robot interaction: a review,” Feb. 2024. [Online]. Available: https://doi.org/10. 1007/s40747-023-01173-6
  5. I. Archive, “Petabox,” https://archive.org/web/petabox.php, 2023. [Online]. Available: https://archive.org/web/petabox. php
  6. L. Leal-Taixe and S. Roth, Eds., Computer Vision ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part III, ser. Lecture Notes in Computer Science. Cham, Switzerland: Springer, 2019, vol. 11131, part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP). Included in the conference series: ECCV: European Conference on Computer Vision. 109k Accesses, 944 Citations, 10 Altmetric.
  7. P. K. Sa, M. N. Sahoo, M. Murugappan, Y. Wu, and B. Majhi, Eds., Progress in Intelligent Computing Techniques: Theory, Practice, and Applications: Proceedings of ICACNI 2016, Volume 1, ser. Advances in Intelligent Systems and Computing, vol. 518. Springer, 2018.
  8. F. d. C. E. y. N. Universidad de Buenos Aires, “Biblioteca digital,” https://bibliotecadigital.exactas.uba.ar/, 2023, accessed: [2-2-2025]. [Online]. Available: https: //bibliotecadigital.exactas.uba.ar/
  9. H. I. Mohammed, B. A. Sultan, and K. H. Hamee, “Hand gestures recognition classification,” International Journal of Engineering Research Updates, vol. 3, no. 2, pp. 008 – 012, Oct. 2022.
  10. U. catholique de Louvain (UCLouvain), “Uclouvain website,” https://sites.uclouvain.be/, 2023, accessed: [Insert Date of Access]. [Online]. Available: https://sites.uclouvain.be/
  11. G. Benitez-Garcia, J. Olivares-Mercado, G. Sanchez-Perez, and K. Yanai, “Ipn hand: A video dataset and benchmark for real-time continuous hand gesture recognition,” in 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, Jan. 2021. [Online]. Available: http: //dx.doi.org/10.1109/ICPR48806.2021.9412317
  12. A. Robotics, “Artificial life robotics,” https://alife-robotics. co.jp/, 2023, accessed: [2-2-2025]. [Online]. Available: https://alife-robotics.co.jp/
  13. S. Shanmugam and R. S. Narayanan, “An accurate estimation of hand gestures using optimal modified convolutional neural network,” Expert Systems with Applications, vol. 249, p. 123351, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2024.123351
  14. I. J. of Electrical and C. E. (IJECE), “Ijece: International journal of electrical and computer engineering,” https: //ijece.iaescore.com/, 2023, accessed: 2-2-2025. [Online]. Available: https://ijece.iaescore.com/
  15. D. Sarma and M. K. Bhuyan, “Methods, databases and recent advancement of vision-based hand gesture recognition for hci systems: A review,” SN Computer Science, vol. 2, no. 6, Aug. 2021.
  16. M. Yasen and S. Jusoh, “A systematic review on hand gesture recognition techniques, challenges and applications,” PeerJ Computer Science, vol. 5, p. e218, Sep. 2019. [Online]. Available: http://dx.doi.org/10.7717/peerj-cs.218
  17. B. Kareem Murad and A. H. Hassin Alasadi, “Advancements and challenges in hand gesture recognition: A comprehensive review,” Iraqi Journal for Electrical and Electronic Engineering, vol. 20, no. 2, pp. 154-164, Jul. 2024. [Online]. Available: http://dx.doi.org/10.37917/ijeee.20.2.13
  18. S. Gupta, P. Bujade, V. Singh, and S. Tiwari, “Hand gesture recognition system using deep learning,” International Journal For Multidisciplinary Research, vol. 6, no. 3, May 2024. [Online]. Available: http://dx.doi.org/10.36948/ijfmr. 2024.v06i03.19602
  19. R. Dhiman, P. Luthra, and N. T. Singh, “Different categories of feature extraction and machine learning classification models used for hand gesture recognition systems: A review,” IEEE Access, vol. 11, pp. 1–7, April 2023. [Online]. Available: http://dx.doi.org/10.1109/I2CT57861. 2023.10126410
  20. F.-S. Chen, C.-M. Fu, and C.-L. Huang, “Hand gesture recognition using a real-time tracking method and hidden markov models,” Image and Vision Computing, vol. 21, no. 8, pp. 745–758, August 2003. [Online]. Available: http://dx.doi.org/10.1016/S0262-8856(03)00070-2
  21. J. Alon, V. Athitsos, Q. Yuan, and S. Sclaroff, “A unified framework for gesture recognition and spatiotemporal gesture segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1685–1699, 2009. [Online]. Available: https://doi.org/10. 1109/TPAMI.2008.203
  22. S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell, “Hidden conditional random fields for gesture recognition,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE, p. 1521 – 1527. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2006.132
  23. L. Gupta and S. Ma, “Gesture-based interaction and communication: automated classification of hand gesture contours,” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 1, pp. 114–120, 2001. [Online]. Available: http: //dx.doi.org/10.1109/5326.923274
  24. S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell, “Hidden conditional random fields for gesture recognition,” in Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR06), vol. 2. IEEE, 2006, pp. 1521–1527. [Online]. Available: http://dx.doi.org/10.1109/ CVPR.2006.132
  25. M. Kolsch and M. Turk, “Robust hand detection,” in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings. IEEE, pp. 614– 619. [Online]. Available: http://dx.doi.org/10.1109/AFGR. 2004.1301601
  26. L. Lee and W. Grimson, “Gait analysis for recognition and classification,” in Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, ser. AFGR-02. IEEE, p. 155–162. [Online]. Available: http://dx.doi.org/10.1109/AFGR.2002.1004148
  27. M.-H. Yang, N. Ahuja, and M. Tabb, “Extraction of 2d motion trajectories and its application to hand gesture recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1061–1074, 2002. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2002. 1023803
  28. H.-I. Suk, B.-K. Sin, and S.-W. Lee, “Hand gesture recognition based on dynamic bayesian network framework,” Pattern Recognition, vol. 43, no. 9, pp. 3059–3072, 2010. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0031320310001366
  29. M. Elmezain, A. Al-Hamadi, J. Appenrodt, and B. Michaelis, “A hidden markov model-based continuous gesture recognition system for hand motion trajectory,” in 2008 19th International Conference on Pattern Recognition, 2008, pp. 1–4. [Online]. Available: https://doi.org/10.1109/ICPR.2008.4761080
  30. D.-H. Lee and K.-S. Hong, “Game interface using hand gesture recognition,” in 5th International Conference on Computer Sciences and Convergence Information Technology. IEEE, 2010. [Online]. Available: http://dx. doi.org/10.1109/ICCIT.2010.5711226
  31. A. Amaravati, S. Xu, N. Cao, J. Romberg, and A. Raychowdhury, “A light-powered smart camera with compressed domain gesture detection,” IEEE Transactions on Circuits and Systems for Video Technology, 2022.
  32. A. Sen, S. Dombe, T. K. Mishra, and R. Dash, “Hgrfyolo: a robust hand gesture recognition system for the normal and physically impaired person using frozen yolov5,” Multimedia Tools and Applications, vol. 83, no. 30, pp. 73 797–73 815, Feb. 2024. [Online]. Available: http://dx.doi.org/10.1007/s11042-024-18464-w
  33. R. Shrivastava, “A hidden markov model based dynamic hand gesture recognition system using opencv,” in 2013 3rd IEEE International Advance Computing Conference (IACC). IEEE, Feb. 2013. [Online]. Available: http: //dx.doi.org/10.1109/IAdCC.2013.6514354
  34. K. M. Sagayam and D. J. Hemanth, “A probabilistic model for state sequence analysis in hidden markov model for hand gesture recognition,” Computational Intelligence, vol. 35, no. 1, pp. 59–81, 2019. [Online]. Available: https://doi.org/10.1111/coin.12188
  35. K. Bimbraw, A. Talele, and H. K. Zhang, “Hand gesture classification based on forearm ultrasound video snippets using 3d convolutional neural networks,” 2024. [Online]. Available: https://arxiv.org/abs/2409.16431
  36. M. S. A. Milu, A. Fathima, T. Talukder, I. Islam, and M. I. S. Emon, “Design and implementation of hand gesture detection system using hm model for sign language recognition development,” Journal of Data Analysis and Information Processing, vol. 12, no. 2, pp. 139–150, 2024. [Online]. Available: http://dx.doi.org/10.4236/jdaip. 2024.122008
  37. Q. Chen, N. D. Georganas, and E. M. Petriu, “Realtime vision-based hand gesture recognition using haar-like features,” in 2007 IEEE Instrumentation amp; Measurement Technology Conference IMTC 2007. IEEE, May 2007, pp. 1–6. [Online]. Available: http://dx.doi.org/10.1109/IMTC. 2007.379068
  38. K. Murakami and H. Taguchi, “Gesture recognition using recurrent neural networks,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - Reaching Through Technology (CHI’91). ACM Press, 1991, pp. 237–242. [Online]. Available: http: //dx.doi.org/10.1145/108844.108900
  39. T. Schl¨omer, B. Poppinga, N. Henze, and S. Boll, “Gesture recognition with a wii controller,” in Proceedings of the 2nd international conference on Tangible and embedded interaction, ser. TEI08. ACM, Feb. 2008. [Online]. Available: http://dx.doi.org/10.1145/1347390.1347395
  40. R. Kjeldsen and J. Kender, “Toward the use of gesture in traditional user interfaces,” in Proceedings of the Second International Conference on Automatic Face and Gesture Recognition (AFGR-96). IEEE Computer Society Press, 1996, pp. 151–156. [Online]. Available: http://dx.doi.org/10.1109/AFGR.1996.557257
  41. S. Waldherr, R. Romero, and S. Thrun, “A gesture-based interface for human-robot interaction,” Autonomous Robots, vol. 9, no. 2, pp. 151–173, 2000. [Online]. Available: http://dx.doi.org/10.1023/A:1008918401478
  42. T. Starner, J. Weaver, and A. Pentland, “Real-time american sign language recognition using desk and wearable computer based video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1371–1375, 1998. [Online]. Available: http: //dx.doi.org/10.1109/34.735811
  43. C. Vogler and D. Metaxas, “A framework for recognizing the simultaneous aspects of american sign language,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 358–384, Mar. 2001. [Online]. Available: http://dx.doi.org/10.1006/cviu.2000.0895
  44. Y. F. A. Gaus and F. Wong, “Hidden markov model-based gesture recognition with overlapping hand-head/handhand estimated using kalman filter,” in 2012 Third International Conference on Intelligent Systems Modelling and Simulation. IEEE, Feb. 2012, pp. 262–267. [Online]. Available: http://dx.doi.org/10.1109/ISMS.2012.67
  45. M. Elmezain, A. Al-Hamadi, J. Appenrodt, and B. Michaelis, “A hidden markov model-based isolated and meaningful hand gesture recognition,” International Journal of Electrical, Computer, and Systems Engineering, vol. 3, no. 3, pp. 156–163, 2009.
  46. M. Elmezain, A. Al-Hamadi, and B. Michaelis, “Hand trajectory-based gesture spotting and recognition using hmm,” in 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE, Nov. 2009, pp. 3577– 3580. [Online]. Available: http://dx.doi.org/10.1109/ICIP. 2009.5414322
  47. N. Liu, B. C. Lovell, and P. J. Kootsookos, “Evaluation of hmm training algorithms for letter hand gesture recognition,” in Proc. 3rd IEEE Int. Symp. Signal Process. Inf. Technol. (ISSPIT-03) (IEEE Cat. No.03EX795). IEEE, 2003, pp. 648–651. [Online]. Available: http://dx.doi.org/ 10.1109/ISSPIT.2003.1341204
  48. T. Hachaj and M. R. Ogiela, “Rule-based approach to recognizing human body poses and gestures in real time,” Multimedia Syst., vol. 20, no. 1, pp. 81–99, Sep. 2013. [Online]. Available: http://dx.doi.org/10.1007/ s00530-013-0332-2
  49. M.-C. Su, “A fuzzy rule-based approach to spatio-temporal hand gesture recognition,” IEEE Trans. Syst., Man, Cybern. Part C (Appl. Rev.), vol. 30, no. 2, pp. 276–281, May 2000. [Online]. Available: http://dx.doi.org/10.1109/5326.868448
  50. L. Billiet, J. Oramas, M. Hoffmann, W. Meert, and L. Antanas, “Rule-based hand posture recognition using qualitative finger configurations acquired with the kinect,” in Proc. 2nd Int. Conf. Pattern Recognit. Appl. Methods (ICPRAM), Vol. 1. Barcelona, Spain: SciTePress, 2013, pp. 539–542. [Online]. Available: http://dx.doi.org/10.5220/ 0004230805390542
  51. G. McGlaun, F. Althoff, M. Lang, and G. Rigoll, Robust Video-Based Recognition of Dynamic Head Gestures in Various Domains – Comparing a Rule-Based and a Stochastic Approach. Springer Berlin Heidelberg, 2004, p. 180–197. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-540-24598-8 18
  52. T. Hachaj and M. R. Ogiela, “Rule-based approach to recognizing human body poses and gestures in real time,” Multimedia Syst., vol. 20, no. 1, pp. 81–99, Sep. 2013. [Online]. Available: http://dx.doi.org/10.1007/ s00530-013-0332-2
  53. A. Riad, “Hand gesture recognition system based on a geometric model and rule based classifier,” British Journal of Applied Science & Technology, vol. 4, no. 9, pp. 1432–1444, jan 2014. [Online]. Available: http: //dx.doi.org/10.9734/BJAST/2014/7956
  54. M. P. Craven, K. M. Curtis, B. H. Hayes-Gill, and C. D. Thursfield, “A hybrid neural network rule-based technique for gesture recognition,” in Proceedings of the Fourth IEEE International Conference on Electronics, Circuits and Systems, 1997. [Online]. Available: https: //nottingham-repository.worktribe.com/output/1024332
  55. M. L. G. McGlaun, F. Althoff and G. Rigoll, “Robust video-based recognition of dynamic head gestures in various domains – comparing a rule-based and a stochastic approach,” in Gesture-Based Communication in Human- Computer Interaction, A. Camurri and G. Volpe, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, pp. 180–197. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-540-24598-8 18
  56. Z. Ren, J. Yuan, J. Meng, and Z. Zhang, “Robust partbased hand gesture recognition using kinect sensor,” IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1110–1120, Aug. 2013. [Online]. Available: http://dx.doi.org/10.1109/ TMM.2013.2246148
  57. H. Leon-Garza, H. Hagras, A. Pena-Rios, O. Bahceci, and A. Conway, “A hand-gesture recognition based interpretable type-2 fuzzy rule-based system for extended reality,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, Oct. 2022, pp. 2894–2899. [Online]. Available: http://dx.doi.org/10.1109/SMC53654. 2022.9945407
  58. Y. Cui and J. Weng, “Appearance-based hand sign recognition from intensity image sequences,” Computer Vision and Image Understanding, vol. 78, no. 2, pp.157–176, May 2000. [Online]. Available: http://dx.doi.org/ 10.1006/cviu.2000.0837
  59. B. Bauer and H. Hienz, “Relevant features for video-based continuous sign language recognition,” in Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), ser. AFGR- 00. IEEE Comput. Soc, 2000, pp. 440–445. [Online]. Available: http://dx.doi.org/10.1109/AFGR.2000.840672
  60. T. Hachaj and M. R. Ogiela, “Rule-based approach to recognizing human body poses and gestures in real time,” Multimedia Systems, vol. 20, no. 1, pp. 81–99, Sep. 2013. [Online]. Available: http://dx.doi.org/10.1007/ s00530-013-0332-2
  61. ——, “Rule-based approach to recognizing human body poses and gestures in real time,” Multimedia Systems, vol. 20, no. 1, pp. 81–99, Sep. 2013. [Online]. Available: http://dx.doi.org/10.1007/s00530-013-0332-2
  62. G. McGlaun, F. Althoff, M. Lang, and G. Rigoll, Robust Video-Based Recognition of Dynamic Head Gestures in Various Domains – Comparing a Rule-Based and a Stochastic Approach. Springer Berlin Heidelberg, 2004, pp. 180–197. [Online]. Available: http://dx.doi.org/10.1007/ 978-3-540-24598-8 18
  63. A. C. R. C. B. C. Bedregal and G. P. Dimuro, Fuzzy Rule-Based Hand Gesture Recognition. Springer US, 2007, pp. 285–294. [Online]. Available: http://dx.doi.org/ 10.1007/978-0-387-34747-9 30
  64. M. Lech and B. Kostek, “Hand gesture recognition supported by fuzzy rules and kalman filters,” International Journal of Intelligent Information and Database Systems, vol. 6, no. 5, p. 407, 2012. [Online]. Available: http: //dx.doi.org/10.1504/IJIIDS.2012.049304
  65. Y. Chen, W. Gao, and J. Ma, “Hand gesture recognition based on decision tree,” in Proc. International Symposium on Chinese Spoken Language Processing, 2000, pp. 299– 302.
  66. M. Lech and B. Kostek, “Hand gesture recognition supported by fuzzy rules and kalman filters,” vol. 6, no. 5, 2012, pp. 407–420, pMID: 49304. [Online]. Available: https://www.inderscienceonline.com/doi/abs/10. 1504/IJIIDS.2012.049304
  67. H.-I. Lin, M.-H. Hsu, and W.-K. Chen, “Human hand gesture recognition using a convolution neural network,” pp. 1038–1043, Aug. 2014. [Online]. Available: http: //dx.doi.org/10.1109/CoASE.2014.6899454
  68. K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Multihmm classification for hand gesture recognition using two differing modality sensors,” pp. 1–4, Oct. 2014. [Online]. Available: http://dx.doi.org/10.1109/DCAS.2014.6965338
  69. E. Ohn-Bar and M. M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, pp. 2368–2377, Dec. 2014. [Online]. Available: http: //dx.doi.org/10.1109/TITS.2014.2337331
  70. K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of inertial and depth sensor data for robust hand gesture recognition,” IEEE Sensors Journal, vol. 14, no. 6, pp. 1898–1903, Jun. 2014. [Online]. Available: http: //dx.doi.org/10.1109/JSEN.2014.2306094
  71. D. K. Ghosh and S. Ari, “Static hand gesture recognition using mixture of features and svm classifier,” pp. 1094 – 1099, Apr. 2015. [Online]. Available: http://dx.doi.org/10. 1109/CSNT.2015.18
  72. J.-W. Wang, N. T. Le, C.-C. Wang, and J.-S. Lee, “Hand posture recognition using a three-dimensional light field camera,” IEEE Sensors Journal, vol. 16, no. 11, pp. 4389–4396, Jun. 2016. [Online]. Available: http://dx.doi.org/10.1109/JSEN.2016.2546556
  73. Y. Chen, Z. Ding, Y.-L. Chen, and X. Wu, “Rapid recognition of dynamic hand gestures using leap motion,” pp. 1419–1424, Aug. 2015. [Online]. Available: http: //dx.doi.org/10.1109/ICInfA.2015.7279509
  74. J. M. Alvarez-Alvarado, J. G. Rios-Moreno, S. A. Obregon-Biosca, G. Ronquillo-Lomeli, E. Ventura-Ramos, and M. Trejo-Perea, “Hybrid techniques to predict solar radiation using support vector machine and search optimization algorithms: A review,” Applied Sciences, vol. 11, no. 3, p. 1044, Jan. 2021. [Online]. Available: http://dx.doi.org/10.3390/app11031044
  75. CORE, “Core: Aggregating the world’s open access research papers,” https://core.ac.uk/, 2023, accessed: [2-2- 2025]. [Online]. Available: https://core.ac.uk/
  76. O. B. University, “Oxford brookes university cms,” https: //cms.brookes.ac.uk/, 2023, accessed: [2-2-2025]. [Online]. Available: https://cms.brookes.ac.uk/
  77. K. V. M. Mohan and M. S. Babu, Eds., Disruptive Technologies in Computing and Communication Systems: Proceedings of the 1st International Conference on Disruptive Technologies in Computing and Communication Systems. Boca Raton, FL: CRC Press, 2024.
  78. P. Natarajan and R. Nevatia, “Hierarchical multi-channel hidden semi markov graphical models for activity recognition,” Computer Vision and Image Understanding, vol. 117, no. 10, pp. 1329–1344, Oct. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.cviu.2012.08.011
  79. X. Wang, M. Xia, H. Cai, Y. Gao, and C. Cattani, “Hidden markov models based dynamic hand gesture recognition,” Mathematical Problems in Engineering, vol. 2012, no. 1, Jan. 2012. [Online]. Available: http://dx.doi.org/10.1155/ 2012/986134
  80. F. Chen, Q. Zhong, F. Cannella, K. Sekiyama, and T. Fukuda, “Hand gesture modeling and recognition for human and robot interactive assembly using hidden markov models,” International Journal of Advanced Robotic Systems, vol. 12, no. 4, Jan. 2015. [Online]. Available: http://dx.doi.org/10.5772/60044
  81. Y. Dennemont, G. Bouyer, S. Otmane, and M. Mallem, “A discrete hidden markov models recognition module for temporal series: Application to real-time 3d hand gestures,” pp. 299–304, Oct. 2012. [Online]. Available: http://dx.doi.org/10.1109/IPTA.2012.6469509
  82. U. of Cagliari, “Iris: Institutional research information system,” https://iris.unica.it/, 2023, accessed: [2-2-2025]. [Online]. Available: https://iris.unica.it/
  83. M. K. Ahuja and A. Singh, “Static vision based hand gesture recognition using principal component analysis,” pp. 402 – 406, Oct. 2015. [Online]. Available: http: //dx.doi.org/10.1109/MITE.2015.7375353
  84. A. Barkoky and N. M. Charkari, “Static hand gesture recognition of persian sign numbers using thinning method,” pp. 6548 – 6551, Jul. 2011. [Online]. Available: http://dx.doi.org/10.1109/ICMT.2011.6002201
  85. H. Li, L. Yang, X. Wu, S. Xu, and Y. Wang, “Static hand gesture recognition based on hog with kinect,” pp. 271 – 273, Aug. 2012. [Online]. Available: http: //dx.doi.org/10.1109/IHMSC.2012.75
  86. R. Wang, Z. Yu, M. Liu, Y. Wang, and Y. Chang, “Realtime visual static hand gesture recognition system and its fpga-based hardware implementation,” pp. 434 – 439, Oct. 2014. [Online]. Available: http://dx.doi.org/10.1109/ICOSP. 2014.7015043
  87. M. M. Gharasuie and H. Seyedarabi, “Real-time dynamic hand gesture recognition using hidden markov models,” pp. 194–199, Sep. 2013. [Online]. Available: http://dx.doi.org/ 10.1109/IranianMVIP.2013.6779977
  88. H. Y. Lai and H. J. Lai, “Real-time dynamic hand gesture recognition,” pp. 658–661, Jun. 2014. [Online]. Available: http://dx.doi.org/10.1109/IS3C.2014.177
  89. A. Pradhan and B. Deepak, “Obtaining hand gesture parameters using image processing,” pp. 168–170, May 2015. [Online]. Available: http://dx.doi.org/10.1109/ICSTM.2015. 7225408
  90. H. M. Gamal, H. M. Abdul-Kader, and E. A. Sallam, “Hand gesture recognition using fourier descriptors,” pp. 274–279, Nov. 2013. [Online]. Available: http://dx.doi.org/10.1109/ ICCES.2013.6707218
  91. S. Ganapathyraju, “Hand gesture recognition using convexity hull defects to control an industrial robot,” pp. 63–67, Aug. 2013. [Online]. Available: http://dx.doi.org/10.1109/ICA.2013.6734047
  92. X. Li, J. ho An, J. hong Min, and K.-S. Hong, “Hand gesture recognition by stereo camera using the thinning method,” pp. 3077–3080, Jul. 2011. [Online]. Available: http://dx.doi.org/10.1109/ICMT.2011.6001670
  93. Z. Yang, Y. Li, W. Chen, and Y. Zheng, “Dynamic hand gesture recognition using hidden markov models,” pp. 360–365, Jul. 2012. [Online]. Available: http://dx.doi.org/ 10.1109/ICCSE.2012.6295092
  94. L. K. Phadtare, R. S. Kushalnagar, and N. D. Cahill, “Detecting hand-palm orientation and hand shapes for sign language gesture recognition using 3d images,” pp. 29–32, Nov. 2012. [Online]. Available: http://dx.doi.org/10.1109/ WNYIPW.2012.6466652
  95. D. Vishwakarma, R. Maheshwari, and R. Kapoor, “An efficient approach for the recognition of hand gestures from very low resolution images,” Apr. 2015. [Online]. Available: http://dx.doi.org/10.1109/CSNT.2015.84
  96. L. Prasuhn, Y. Oyamada, Y. Mochizuki, and H. Ishikawa, “A hog-based hand gesture recognition system on a mobile device,” pp. 3973–3977, Oct. 2014. [Online]. Available: http://dx.doi.org/10.1109/ICIP.2014.7025807
  97. R. Shrivastava, “A hidden markov model based dynamic hand gesture recognition system using opencv,” pp. 947– 950, Feb. 2013. [Online]. Available: http://dx.doi.org/10. 1109/IAdCC.2013.6514354
  98. B. Pathak, A. S. Jalal, S. C. Agrawal, and C. Bhatnagar, “A framework for dynamic hand gesture recognition using key frames extraction,” pp. 1–4, Dec. 2015. [Online]. Available: http://dx.doi.org/10.1109/NCVPRIPG.2015.7490038
  99. H.-M. Zhu and C.-M. Pun, “Real-time hand gesture recognition from depth image sequences,” pp. 49–52, Jul. 2012. [Online]. Available: http://dx.doi.org/10.1109/CGIV. 2012.13
  100. T. Wan, Y. Wang, and J. Li, “Hand gesture recognition system using depth data,” pp. 1063–1066, Apr. 2012. [Online]. Available: http://dx.doi.org/10.1109/CECNet.2012.6201837
  101. L. Li and S. Dai, “Bayesian neural network approach to hand gesture recognition system,” pp. 2019–2023, Aug. 2014. [Online]. Available: http://dx.doi.org/10.1109/ CGNCC.2014.7007487
  102. Y. Chen, B. Luo, Y.-L. Chen, G. Liang, and X. Wu, “A real-time dynamic hand gesture recognition system using kinect sensor,” Dec. 2015. [Online]. Available: http://dx.doi.org/10.1109/ROBIO.2015.7419071
  103. R. Harshitha, I. A. Syed, and S. Srivasthava, “Hci using hand gesture recognition for digital sand model,” pp. 453–457, Dec. 2013. [Online]. Available: http://dx.doi.org/ 10.1109/ICIIP.2013.6707633
  104. S. Shiravandi, M. Rahmati, and F. Mahmoudi, “Hand gestures recognition using dynamic bayesian networks,” pp. 1–6, Apr. 2013. [Online]. Available: http://dx.doi.org/10. 1109/RIOS.2013.6595318
  105. N. N. Bhat, Y. V. Venkatesh, U. Karn, and D. Vig, “Hand gesture recognition using self organizing map for human computer interaction,” pp. 734–738, Aug. 2013. [Online]. Available: http://dx.doi.org/10.1109/ICACCI.2013.6637265
  106. X. Wu, C. Yang, Y. Wang, H. Li, and S. Xu, “An intelligent interactive system based on hand gesture recognition algorithm and kinect,” pp. 294–298, Oct. 2012. [Online]. Available: http://dx.doi.org/10.1109/ISCID.2012.225
  107. A. R. Asif, A. Waris, S. O. Gilani, M. Jamil, H. Ashraf, M. Shafique, and I. K. Niazi, “Performance evaluation of convolutional neural network for hand gesture recognition using emg,” Sensors, vol. 20, no. 6, p. 1642, Mar. 2020. [Online]. Available: http://dx.doi.org/10.3390/s20061642
  108. R. Antonius and H. Tjahyadi, “Electromyography gesture identification using cnn-rnn neural network for controlling quadcopters,” Journal of Physics: Conference Series, vol. 1858, no. 1, p. 012075, Apr. 2021. [Online]. Available: http://dx.doi.org/10.1088/1742-6596/1858/1/012075
  109. V. Adithya and R. Rajesh, “Hand gestures for emergency situations: A video dataset based on words from indian sign language,” Data in Brief, vol. 31, p. 106016, Aug. 2020. [Online]. Available: http://dx.doi.org/10.1016/j.dib. 2020.106016
  110. G. Yuan, X. Liu, Q. Yan, S. Qiao, Z. Wang, and L. Yuan, “Hand gesture recognition using deep feature fusion network based on wearable sensors,” IEEE Sensors Journal, p. 1, 2020. [Online]. Available: http://dx.doi.org/ 10.1109/JSEN.2020.3014276
  111. H. Zhang, Z.-H. Bo, J.-H. Yong, and F. Xu, “Interactionfusion: real-time reconstruction of hand poses and deformable objects in hand-object interactions,” ACM Transactions on Graphics, vol. 38, no. 4, pp. 1-11, Jul. 2019. [Online]. Available: http://dx.doi.org/10.1145/3306346.3322998
  112. K. Lai and S. N. Yanushkevich, “Cnn+rnn depth and skeleton based dynamic hand gesture recognition,” pp. 3451-3456, Aug. 2018. [Online]. Available: http://dx.doi. org/10.1109/ICPR.2018.8545718
  113. J.Wan, Y. Zhao, S. Zhou, I. Guyon, S. Escalera, and S. Z. Li, “Chalearn looking at people rgb-d isolated and continuous datasets for gesture recognition,” June 2016.
  114. A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, and D. Modha, “A low power, fully event-based gesture recognition system,” July 2017.
  115. P. Molchanov, X. Yang, S. Gupta, K. Kim, S. Tyree, and J. Kautz, “Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural network,” June 2016.
  116. Z. Zhou, K.-S. Lui, V. W. Tam, and E. Y. Lam, “Applying (3+2+1)d residual neural network with frame selection for hong kong sign language recognition,” pp. 4296-4302, Jan. 2021. [Online]. Available: http: //dx.doi.org/10.1109/ICPR48806.2021.9412075
  117. U. von Agris, M. Knorr, and K.-F. Kraiss, “The significance of facial features for automatic sign language recognition,” pp. 1-6, Sep. 2008. [Online]. Available: http://dx.doi.org/10. 1109/AFGR.2008.4813472
  118. S. Albanie, G. Varol, L. Momeni, H. Bull, T. Afouras, H. Chowdhury, N. Fox, B. Woll, R. Cooper, A. McParland, and A. Zisserman, “Bbc-oxford british sign language dataset,” 2021. [Online]. Available: https://arxiv.org/abs/ 2111.03635
  119. J. Fink, B. Frenay, L. Meurant, and A. Cleve, “Lsfb-cont and lsfb-isol: Two new datasets for vision-based sign language recognition,” pp. 1-8, Jul. 2021. [Online]. Available: http://dx.doi.org/10.1109/IJCNN52387.2021.9534336
  120. C. C. de Amorim, D. Macˆedo, and C. Zanchettin, “Spatialtemporal graph convolutional networks for sign language recognition,” pp. 646-657, 2019. [Online]. Available: http://dx.doi.org/10.1007/978-3-030-30493-5 59
  121. O. M. Sincan and H. Y. Keles, “Autsl: A large scale multimodal turkish sign language dataset and baseline methods,” IEEE Access, vol. 8, pp. 181 340-181 355, 2020. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2020. 3028072
  122. J. P. Vasconez, L. I. Barona Lopez, L. Valdivieso Caraguay, and M. E. Benalcazar, “Hand gesture recognition using emg-imu signals and deep q-networks,” Sensors, vol. 22, no. 24, p. 9613, Dec. 2022. [Online]. Available: http: //dx.doi.org/10.3390/s22249613
  123. medRxiv, “medrxiv: The preprint server for health sciences,” https://www.medrxiv.org/, 2023, accessed: [2-2-2025]. [Online]. Available: https://www.medrxiv.org/
  124. S. Shaik and S. Saraswathi, “Enhance the ai virtual system accuracy with novel hand gesture recognition algorithm comparing to convolutional neural network,” vol. 491, p. 04022, 2024. [Online]. Available: https: //doi.org/10.1051/e3sconf/202449104022
  125. X.-H. Zhang, J.-J. Wang, X. Wang, and X.-L. Ma, “Improvement of dynamic hand gesture recognition based on hmm algorithm,” pp. 401-406, Jun. 2016. [Online]. Available: http://dx.doi.org/10.1109/ISAI.2016.0091
  126. Y. Li, X. Feng, Y. Xu, X. Dong, Z. Xu, J. Huang, and L. Lu, “A dynamic hand gesture recognition model based on the improved dynamic time warping algorithm,” pp. 1-6, Sep. 2019. [Online]. Available: http://dx.doi.org/10.23919/ IConAC.2019.8895002
  127. A. Mohanarathinam, K. Dharani, R. Sangeetha, G. Aravindh, and P. Sasikala, “Study on hand gesture recoginition by using machine learning,” pp. 1498-1501, Nov. 2020. [Online]. Available: http://dx.doi.org/10.1109/ ICECA49313.2020.9297513
  128. A. V. and R. R., “A deep convolutional neural network approach for static hand gesture recognition,” Procedia Computer Science, vol. 171, pp. 2353-2361, 2020. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2020.04.255
  129. O. Mazhar, S. Ramdani, and A. Cherubini, “A deep learning framework for recognizing both static and dynamic gestures,” Sensors, vol. 21, no. 6, p. 2227, Mar. 2021. [Online]. Available: http://dx.doi.org/10.3390/s21062227
  130. S.-H. Yang, W.-R. Chen, W.-J. Huang, and Y.-P. Chen, “Ddanet: Dual-path depth-aware attention network for fingerspelling recognition using rgb-d images,” IEEE Access, vol. 9, pp. 7306 – 7322, 2021. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2020.3046667
  131. P. Koch, M. Dreier, M. Maass, M. Bohme, H. Phan, and A. Mertins, “A recurrent neural network for hand gesture recognition based on accelerometer data,” pp. 5088-5091, Jul. 2019. [Online]. Available: http://dx.doi.org/10.1109/ EMBC.2019.8856844
  132. W. Zhang, J. Wang, and F. Lan, “Dynamic hand gesture recognition based on short-term sampling neural networks,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 1, pp. 110 – 120, Jan. 2021. [Online]. Available: http: //dx.doi.org/10.1109/JAS.2020.1003465
  133. W. Yu and E. N. Sanchez, Eds., Advances in Computational Intelligence: Proceedings of the IWACI’09 Workshop, 22nd- 23rd June 2009, Mexico City, ser. Advances in Intelligent and Soft Computing. Berlin, Heidelberg: Springer, 2009, vol. 61, presents latest research in Computational Intelligence.
  134. E. Tsironi, P. Barros, C. Weber, and S. Wermter, “An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition,” Neurocomputing, vol. 268, pp. 76-86, Dec. 2017. [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2016.12.088
  135. W. Tao, M. C. Leu, and Z. Yin, “American sign language alphabet recognition using convolutional neural networks with multiview augmentation and inference fusion,” Engineering Applications of Artificial Intelligence, vol. 76, pp. 202 – 213, Nov. 2018. [Online]. Available: http://dx.doi.org/10.1016/j.engappai.2018.09.006
  136. C. Bhuvaneshwari and A. Manjunathan, “Advanced gesture recognition system using long-term recurrent convolution network,” Materials Today: Proceedings, vol. 21, pp. 731 – 733, 2020. [Online]. Available: http://dx.doi.org/10.1016/j. matpr.2019.06.748
  137. N. B. Ibrahim, M. M. Selim, and H. H. Zayed, “An automatic arabic sign language recognition system (arslrs),” Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 4, pp. 470 – 477, Oct. 2018. [Online]. Available: http://dx.doi.org/10.1016/j.jksuci.2017.09.007
  138. M. Oliveira, H. Chatbri, Y. Ferstl, M. Farouk, S. Little, N. E. O’Connor, and A. Sutherland, “A dataset for irish sign language recognition,” Aug. 2017.
  139. Y. SHI, Y. LI, X. FU, M. Kaibin, and M. Qiguang, “Review of dynamic gesture recognition,” Virtual Reality amp; Intelligent Hardware, vol. 3, no. 3, pp. 183 – 206, Jun. 2021. [Online]. Available: http://dx.doi.org/10.1016/j. vrih.2021.05.001
  140. L. Pigou, A. van den Oord, S. Dieleman, M. Van Herreweghe, and J. Dambre, “Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video,” International Journal of Computer Vision, vol. 126, no. 24, pp. 430 – 439, Oct. 2016. [Online]. Available: http://dx.doi.org/10.1007/s11263-016-0957-7
  141. D. Jiang, G. Li, Y. Sun, J. Kong, B. Tao, and D. Chen, “Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using semg,” Personal and Ubiquitous Computing, vol. 26, no. 4, pp. 1215 – 1224, Jul. 2019. [Online]. Available: http: //dx.doi.org/10.1007/s00779-019-01268-3
  142. P. Sharma and R. S. Anand, “Depth data and fusion of feature descriptors for static gesture recognition,” IET Image Processing, vol. 14, no. 5, pp. 909 – 920, Mar. 2020. [Online]. Available: http://dx.doi.org/10.1049/iet-ipr.2019. 0230
  143. Q. Gao, U. E. Ogenyi, J. Liu, Z. Ju, and H. Liu, “A two-stream cnn framework for american sign language recognition based on multimodal data fusion,” pp. 107 – 118, Aug. 2019. [Online]. Available: http://dx.doi.org/10. 1007/978-3-030-29933-0 9
  144. Z. Gao, P. Wang, H. Wang, M. Xu, and W. Li, “A review of dynamic maps for 3d human motion recognition using convnets and its improvement,” Neural Processing Letters, vol. 52, no. 2, pp. 1501 – 1515, Jul. 2020. [Online]. Available: http://dx.doi.org/10.1007/s11063-020-10320-w
  145. K. Yin and J. Read, “Better sign language translation with stmc-transformer,” 2020. [Online]. Available: https: //arxiv.org/abs/2004.00588
  146. Q. De Smedt, H. Wannous, and J.-P. Vandeborre, “3d hand gesture recognition by analysing set-of-joints trajectories,” pp. 86 – 97, 2018. [Online]. Available: http://dx.doi.org/10. 1007/978-3-319-91863-1 7
  147. ——, “Skeleton-based dynamic hand gesture recognition,” June 2016.
  148. S. Y. Boulahia, E. Anquetil, F. Multon, and R. Kulpa, “Dynamic hand gesture recognition based on 3d pattern assembled trajectories,” pp. 1 – 6, Nov. 2017. [Online]. Available: http://dx.doi.org/10.1109/IPTA.2017.8310146
  149. J. Liu, A. Shahroudy, D. Xu, A. C. Kot, and G. Wang, “Skeleton-based action recognition using spatio-temporal lstm network with trust gates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 12, pp. 3007 – 3021, Dec. 2018. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2017.2771306
  150. D. Konstantinidis, K. Dimitropoulos, and P. Daras, “A deep learning approach for analyzing video and skeletal features in sign language recognition,” pp. 1 – 6, Oct. 2018. [Online]. Available: http://dx.doi.org/10.1109/IST.2018.8577085
  151. J. C. Nunez, R. Cabido, J. J. Pantrigo, A. S. Montemayor, and J. F. Velez, “Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition,” Pattern Recognition, vol. 76, pp. 80 – 94, Apr. 2018. [Online]. Available: http: //dx.doi.org/10.1016/j.patcog.2017.10.033
  152. S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, Apr. 2018. [Online]. Available: http://dx.doi.org/10.1609/aaai.v32i1.12328
  153. C. Ma, A. Wang, G. Chen, and C. Xu, “Hand jointsbased gesture recognition for noisy dataset using nested interval unscented kalman filter with lstm network,” The Visual Computer, vol. 34, no. 6-8, pp. 1053 – 1063, May 2018. [Online]. Available: http://dx.doi.org/10.1007/ s00371-018-1556-0
  154. W. Wei, Y. Wong, Y. Du, Y. Hu, M. Kankanhalli, and W. Geng, “A multi-stream convolutional neural network for semg-based gesture recognition in musclecomputer interface,” Pattern Recognition Letters, vol. 119, pp. 131 – 138, Mar. 2019. [Online]. Available: http://dx.doi.org/10.1016/j.patrec.2017.12.005
  155. C. Si,W. Chen,W.Wang, L.Wang, and T. Tan, “An attention enhanced graph convolutional lstm network for skeletonbased action recognition,” June 2019.
  156. S. Jiang, B. Sun, L. Wang, Y. Bai, K. Li, and Y. Fu, “Sign language recognition via skeleton-aware multi-model ensemble,” 2021. [Online]. Available: https://arxiv.org/abs/ 2110.06161
  157. ——, “Skeleton aware multi-modal sign language recognition,” pp. 3413 – 3423, June 2021.
  158. R. Rastgoo, K. Kiani, and S. Escalera, “Sign language recognition: A deep survey,” Expert Systems with Applications, vol. 164, p. 113794, Feb. 2021. [Online]. Available: http://dx.doi.org/10.1016/j.eswa.2020.113794
  159. H. Zhang, D. Liu, and Z. Xiong, “Two-stream action recognition-oriented video super-resolution,” October 2019.
  160. U. Cote-Allard, C. L. Fall, A. Drouin, A. Campeau-Lecours, C. Gosselin, K. Glette, F. Laviolette, and B. Gosselin, “Deep learning for electromyographic hand gesture signal classification using transfer learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 4, pp. 760 – 771, Apr. 2019. [Online]. Available: http://dx.doi.org/10.1109/TNSRE.2019.2896269
  161. H. Wei, R. Jafari, and N. Kehtarnavaz, “Fusion of video and inertial sensing for deep learning–based human action recognition,” Sensors, vol. 19, no. 17, p. 3680, Aug. 2019. [Online]. Available: http://dx.doi.org/10.3390/s19173680
  162. K. H. Lee, J. Y. Min, and S. Byun, “Electromyogram-based classification of hand and finger gestures using artificial neural networks,” Sensors, vol. 22, no. 1, p. 225, Dec. 2021. [Online]. Available: http://dx.doi.org/10.3390/s22010225
  163. G. Dougherty, Pattern Recognition and Classification: An Introduction. New York, NY: Springer, 2013.
  164. B.-A. Awaluddin, C.-T. Chao, and J.-S. Chiou, “A hybrid image augmentation technique for user- and environment-independent hand gesture recognition based on deep learning,” Mathematics, vol. 12, no. 9, p. 1393, May 2024. [Online]. Available: http://dx.doi.org/10.3390/ math12091393
  165. M. Garg, D. Ghosh, and P. M. Pradhan, “Gestformer: Multiscale wavelet pooling transformer network for dynamic hand gesture recognition,” 2024. [Online]. Available: https://arxiv.org/abs/2405.11180
  166. L. I. Barona Lopez, F. M. Ferri, J. Zea, L. Valdivieso Caraguay, and M. E. Benalcazar, “Cnnlstm and post-processing for emg-based hand gesture recognition,” Intelligent Systems with Applications, vol. 22, p. 200352, Jun. 2024. [Online]. Available: http://dx.doi.org/10.1016/j.iswa.2024.200352
  167. G. Zhou, Z. Cui, and J. Qi, “Fgdsnet: A lightweight hand gesture recognition network for human robot interaction,” IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3076 – 3083, Apr. 2024. [Online]. Available: http: //dx.doi.org/10.1109/LRA.2024.3362144
  168. P. Balaji and M. Ranjan Prusty, “Multimodal fusion hierarchical self-attention network for dynamic hand gesture recognition,” Journal of Visual Communication and Image Representation, vol. 98, p. 104019, Feb. 2024. [Online]. Available: http://dx.doi.org/10.1016/j.jvcir.2023.104019
  169. G. Bhaumik and M. C. Govil, “Spatnet: a spatial feature attention network for hand gesture recognition,” Multimedia Tools and Applications, vol. 83, no. 14, pp. 41 805 – 41 822, Oct. 2023. [Online]. Available: http://dx.doi.org/10.1007/s11042-023-16988-1
  170. H.-Q. Nguyen, T.-H. Le, T.-K. Tran, H.-N. Tran, T.-H. Tran, T.-L. Le, H. Vu, C. Pham, T. P. Nguyen, and H. T. Nguyen, “Hand gesture recognition from wrist-worn camera for human-machine interaction,” IEEE Access, vol. 11, pp. 53 262 – 53 274, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023.3279845
  171. Y. Zhang, W. Kang, and W. Song, “Robust and accurate hand gesture authentication with cross-modality local-global behavior analysis,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 8630 – 8643, 2024. [Online]. Available: http://dx.doi.org/10.1109/TIFS.2024. 3451367
  172. S. Birkeland, L. J. Fjeldvik, N. Noori, S. R. Yeduri, and L. R. Cenkeramaddi, “Thermal video-based hand gestures recognition using lightweight cnn,” Journal of Ambient Intelligence and Humanized Computing, vol. 15, no. 12, pp. 3849 – 3860, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1007/s12652-024-04851-6
  173. M. Garg, D. Ghosh, and P. M. Pradhan, “Mvtn: A multiscale video transformer network for hand gesture recognition,” 2024. [Online]. Available: https://arxiv.org/abs/2409.03890
  174. J. Shin, A. S. M. Miah, S. Konnai, I. Takahashi, and K. Hirooka, “Hand gesture recognition using semg signals with a multi-stream time-varying feature enhancement approach,” Scientific Reports, vol. 14, no. 1, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1038/s41598-024-72996-7
  175. A. S. M. Miah, N. Hassan, M. Maniruzzaman, N. Asai, and J. Shin, “Emg-based hand gesture recognition through diverse domain feature enhancement and machine learningbased approach,” 2024. [Online]. Available: https://arxiv. org/abs/2408.13723
  176. H. Mahmud, M. M. Morshed, and M. K. Hasan, “Quantized depth image and skeleton-based multimodal dynamic hand gesture recognition,” The Visual Computer, vol. 40, no. 1, pp. 11 – 25, Jan. 2023. [Online]. Available: http://dx.doi.org/10.1007/s00371-022-02762-1
  177. Z. Dozdor, Z. Kalafatic, Ban, and T. Hrkaz, “Ty-net: Transforming yolo for hand gesture recognition,” IEEE Access, vol. 11, pp. 140 382 – 140 394, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023.3341702
  178. Yaseen, O.-J. Kwon, J. Kim, S. Jamil, J. Lee, and F. Ullah, “Next-gen dynamic hand gesture recognition: Mediapipe, inception-v3 and lstm-based enhanced deep learning model,” Electronics, vol. 13, no. 16, p. 3233, Aug. 2024. [Online]. Available: http://dx.doi.org/10.3390/ electronics13163233
  179. J. W. Smith, S. Thiagarajan, R. Willis, Y. Makris, and M. Torlak, “Improved static hand gesture classification on deep convolutional neural networks using novel sterile training technique,” IEEE Access, vol. 9, pp. 10 893 – 10 902, 2023. [Online]. Available: http://dx.doi.org/10. 1109/ACCESS.2021.3051454
  180. D. Sarma, H. P. J. Dutta, K. S. Yadav, M. Bhuyan, and R. H. Laskar, “Attention-based hand semantic segmentation and gesture recognition using deep networks,” Evolving Systems, vol. 15, no. 1, pp. 185 – 201, Jul. 2023. [Online]. Available: http://dx.doi.org/10.1007/s12530-023-09512-1
  181. Z. Zhang, Q. Shen, and Y. Wang, “Electromyographic hand gesture recognition using convolutional neural network with multi-attention,” Biomedical Signal Processing and Control, vol. 91, p. 105935, May 2024.
  182. S. Zhang, H. Zhou, R. Tchantchane, and G. Alici, “A wearable human-machine-interface (hmi) system based on colocated emg-pfmg sensing for hand gesture recognition,” IEEE/ASME Transactions on Mechatronics, pp. 1 – 12, 2024. [Online]. Available: http://dx.doi.org/10.1109/ TMECH.2024.3386929
  183. N. Fadel and E. I. A. Kareem, “Real-time hand gesture recognition based on multi-connect architecture associative memory in human computer interaction,” vol. 3061, p. 030003, 2024. [Online]. Available: http://dx.doi.org/10. 1063/5.0203656
  184. A. Chandrabose, X. Fernando, and E.Mercier-Laurent, Eds., Computer, Communication, and Signal Processing. Smart Solutions Towards SDG: 8th IFIP TC 12 International Conference, ICCCSP 2024, Chennai, India, March 20-22, 2024, Revised Selected Papers, ser. IFIP Advances in Information and Communication Technology. Cham, Switzerland: Springer, 2024, vol. 723.
  185. Q. U. of Technology, “Qut eprints,” https://eprints.qut. edu.au/, 2023, accessed: [2-2-2025]. [Online]. Available: https://eprints.qut.edu.au/
  186. A. Kumar and R. Saini, “A comparative study of machine learning techniques for hand gesture recognition to guide multimedia operation,” COMPUTER, vol. 24, no. 8, pp. 145 – 160, 2024.
  187. T. Jawalkar, S. S. Khalate, S. A. Medhe, and K. S. Palaskar, “Hand gesture recognition using ai/ml,” International Journal of Advanced Engineering Application, vol. 10, pp. 95– 115, 2024.
  188. E. Andrei, T. Cornel, G. Culea, S. B. Constantin, and U. A. Gabriel, “Romanian sign language and mime-gesture recognition,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 8, 2024. [Online]. Available: http://dx.doi.org/10.14569/IJACSA.2024.0150888
  189. L. Zholshiyeva, Z. Manbetova, D. Kaibassova, A. Kassymova, Z. Tashenova, S. Baizhumanov, A. Yerzhanova, and K. Aikhynbay, “Human-machine interactions based on hand gesture recognition using deep learning methods,” International Journal of Electrical and Computer Engineering (IJECE), vol. 14, no. 1, p. 741, Feb. 2024. [Online]. Available: http://dx.doi.org/10.11591/ijece.v14i1.pp741-748
  190. X. Savarimuthu, S. Subramani, and A. N. J. Raj, Eds., Artificial Intelligence for Multimedia Information Processing: Tools and Applications. Boca Raton, FL: CRC Press, 2024, 69 B/W Illustrations.
  191. W. Han, M. Hao, Y. Yuan, and P. Liu, “Fusion enhancement of yolov5 and copula bayesian classifier for hand gesture recognition in smart sports venues,” IEEE Access, vol. 12, pp. 67 005 – 67 012, 2024. [Online]. Available: http: //dx.doi.org/10.1109/ACCESS.2024.3398142
  192. H. Ansar, N. A. Mudawi, S. S. Alotaibi, A. Alazeb, B. I. Alabdullah, M. Alonazi, and J. Park, “Hand gesture recognition for characters understanding using convex hull landmarks and geometric features,” IEEE Access, vol. 11, pp. 82 065 – 82 078, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023.3300712
  193. C. M. Suryateja, S. Boppu, L. R. Cenkeramaddi, and B. Ramkumar, “Hand gesture recognition system in the complex background for edge computing devices,” pp. 13 – 18, Dec. 2022. [Online]. Available: http: //dx.doi.org/10.1109/iSES54909.2022.00016
  194. C. Schuessler, W. Zhang, J. Br¨aunig, M. Hoffmann, M. Stelzig, and M. Vossiek, “Radar-based recognition of static hand gestures in american sign language,” 2024. [Online]. Available: https://arxiv.org/abs/2402.12800
  195. O. Y. Fadhil, B. S. Mahdi, and A. R. Abbas, “Using vgg models with intermediate layer feature maps for static hand gesture recognition,” Baghdad Science Journal, Feb. 2023. [Online]. Available: http://dx.doi.org/10.21123/bsj. 2023.7364
  196. J. P. Sahoo, S. P. Sahoo, S. Ari, and S. K. Patra, “Derefnet: Dual-stream dense residual fusion network for static hand gesture recognition,” Displays, vol. 77, p. 102388, Apr. 2023. [Online]. Available: http://dx.doi.org/ 10.1016/j.displa.2023.102388
  197. Y. Jiang, L. Song, J. Zhang, Y. Song, and M. Yan, “Multicategory gesture recognition modeling based on semg and imu signals,” Sensors, vol. 22, no. 15, p. 5855, Aug. 2022. [Online]. Available: http://dx.doi.org/10.3390/s22155855
  198. M. Al-Hammadi, M. A. Bencherif, M. Alsulaiman, G. Muhammad, M. A. Mekhtiche, W. Abdul, Y. A. Alohali, T. S. Alrayes, H. Mathkour, M. Faisal, M. Algabri, H. Altaheri, T. Alfakih, and H. Ghaleb, “Spatial attentionbased 3d graph convolutional neural network for sign language recognition,” Sensors, vol. 22, no. 12, p. 4558, Jun. 2022. [Online]. Available: http://dx.doi.org/10.3390/ s22124558
  199. A. S. M. Miah, M. A. M. Hasan, Y. Tomioka, and J. Shin, “Hand gesture recognition for multi-culture sign language using graph and general deep learning network,” IEEE Open Journal of the Computer Society, vol. 5, pp. 144 – 155, 2024. [Online]. Available: http://dx.doi.org/10.1109/OJCS.2024.3370971
  200. Q. Li, Z. Luo, R. Qi, and J. Zheng, “Deeptpa-net: A deep triple attention network for semg-based hand gesture recognition,” IEEE Access, vol. 11, pp. 96 797 – 96 807, 2023. [Online]. Available: http://dx.doi.org/10. 1109/ACCESS.2023.3312219
  201. U. Imran, A. Waris, S. O. Gilani, J. Iqbal, M. Jamil, G. E. Eldesoky, and S. Mushtaq, “Patient-specific movement regime: Investigating the potential of upper-extremity motions for intelligent myoelectric prosthetic control,” IEEE Access, vol. 12, pp. 35 663–35 682, 2024. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2024.3365639
  202. arXiv, “arxiv.org e-print archive: Computer science,” 2025, accessed: 2025-02-02. [Online]. Available: https: //arxiv.org/search/cs
  203. V. Sharmila, S. Kannadhasan, A. R. Kannan, P. Sivakumar, and V. Vennila, Eds., Challenges in Information, Communication, and Computing Technology: Proceedings of the 2nd International Conference on Challenges in Information, Communication, and Computing Technology (ICCICCT 2024), April 26th & 27th, 2024, Namakkal, Tamil Nadu, India. Boca Raton, FL: CRC Press, 2024.
  204. V. Mottini, “Precision neural interfaces through intrinsically stretchable electronics,” Ph.D. dissertation, Michigan State University, 2024.
  205. I. Society, “Insight society,” https://insightsociety.org/, 2023, accessed: [2-2-2025]. [Online]. Available: https: //insightsociety.org/
  206. S. Manoharan, A. Tugui, and Z. Baig, Eds., Proceedings of the 4th International Conference on Artificial Intelligence and Smart Energy (ICAIS 2024), Volume 2. Springer Nature, 2024.
  207. T. N. S. R. L. M. (TNSRLM), “Tamil nadu state rural livelihoods mission,” https://www.tnsroindia.org.in/, 2023, accessed: [2-2-2025]. [Online]. Available: https: //www.tnsroindia.org.in/
  208. J. Shin, A. S. M. Miah, S. Konnai, I. Takahashi, and K. Hirooka, “Hand gesture recognition using semg signals with a multi-stream time-varying feature enhancement approach,” Scientific Reports, vol. 14, no. 1, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1038/s41598-024-72996-7
  209. Sunanda, A. Balmik, and A. Nandy, “A novel feature fusion technique for robust hand gesture recognition,” Multimedia Tools and Applications, vol. 83, no. 25, pp. 65 815 – 65 831, Jan. 2024. [Online]. Available: http://dx.doi.org/10.1007/s11042-024-18173-4
  210. F. A. Farid, N. Hashim, J. B. Abdullah, M. R. Bhuiyan, M. Kairanbay, Z. Yusoff, H. A. Karim, S. Mansor, M. T. Sarker, and G. Ramasamy, “Single shot detector cnn and deep dilated masks for vision-based hand gesture recognition from video sequences,” IEEE Access, vol. 12, pp. 28 564 – 28 574, 2024. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2024.3360857
  211. M. Jaiswal, V. Sharma, A. Sharma, S. Saini, and R. Tomar, “Quantized cnn-based efficient hardware architecture for real-time hand gesture recognition,” Microelectronics Journal, vol. 151, p. 106345, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1016/j.mejo.2024.106345
  212. J. Tang, K. Gou, C. Wang, M. Wei, Q. Tan, and G. Weng, “Self-powered and 3d printable soft sensor for human health monitoring, object recognition, and contactless hand gesture recognition,” Advanced Functional Materials, vol. 34, no. 52, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1002/adfm.202411172
  213. Y. Wang, Y. Tian, J. Zhu, H. She, Y. Jiang, Z. Jiang, and H. Yokoi, “A hand gesture recognition strategy based on virtual-dimension increase of emg,” Cyborg and Bionic Systems, vol. 5, Jan. 2024. [Online]. Available: http://dx.doi.org/10.34133/cbsystems.0066
  214. A. Sen, S. Dombe, T. K. Mishra, and R. Dash, “Hgr-fyolo: a robust hand gesture recognition system for the normal and physically impaired person using frozen yolov5,” Multimedia Tools and Applications, vol. 83, no. 30, pp. 73 797 – 73 815, Feb. 2024. [Online]. Available: http://dx.doi.org/10.1007/s11042-024-18464-w
  215. Z. Mohammadi, A. Akhavanpour, R. Rastgoo, and M. Sabokrou, “Diverse hand gesture recognition dataset,” Multimedia Tools and Applications, vol. 83, no. 17, pp. 50 245 – 50 267, Nov. 2023. [Online]. Available: http://dx.doi.org/10.1007/s11042-023-17268-8
  216. E. Bamani, E. Nissinman, I. Meir, L. Koenigsberg, and A. Sintov, “Ultra-range gesture recognition using a web-camera in human-robot interaction,” Engineering Applications of Artificial Intelligence, vol. 132, p. 108443, Jun. 2024. [Online]. Available: http://dx.doi.org/10.1016/j. engappai.2024.108443
  217. Sundaram and B. C. Sahana, “Multivariate emg signal based automated hand gestures recognition framework for elder care,” International Journal of Precision Engineering and Manufacturing, Aug. 2024. [Online]. Available: http: //dx.doi.org/10.1007/s12541-024-01116-2
  218. A. Shaaban, M. Strobel, W. Furtner, R. Weigel, and F. Lurz, “Rt-scnns: real-time spiking convolutional neural networks for a novel hand gesture recognition using time-domain mm-wave radar data,” International Journal of Microwave and Wireless Technologies, vol. 16, no. 5, pp. 783 – 795, Jan. 2024. [Online]. Available: http: //dx.doi.org/10.1017/S1759078723001575
  219. T.-H. Tsai, Y.-C. Ho, P.-T. Chi, and T.-J. Chen, “A deep neural network for hand gesture recognition from rgb image in complex background,” Signal, Image and Video Processing, vol. 18, no. S1, pp. 861 – 872, May 2024. [Online]. Available: http://dx.doi.org/10.1007/ s11760-024-03198-x
  220. S. Padmakala, S. O. Husain, E. Poornima, P. Dutta, and M. Soni, “Hyperparameter tuning of deep convolutional neural network for hand gesture recognition,” p. 1–4, Aug. 2024. [Online]. Available: http://dx.doi.org/10.1109/ NMITCON62075.2024.10698984
  221. S. N. Uke and A. Zade, “Optimal video processing and soft computing algorithms for human hand gesture recognition from real-time video,” Multimedia Tools and Applications, vol. 83, no. 17, pp. 50 425 – 50 447, Nov. 2023. [Online]. Available: http://dx.doi.org/10.1007/s11042-023-17608-8
  222. K. Bimbraw, A. Talele, and H. K. Zhang, “Hand gesture classification based on forearm ultrasound video snippets using 3d convolutional neural networks,” 2024. [Online]. Available: https://arxiv.org/abs/2409.16431
  223. S. Misal, “Hand gesture recognition using deep learning,” International Journal of Innovative Science and Research Technology (IJISRT), pp. 69 – 72, Aug. 2024. [Online]. Available: http://dx.doi.org/10.38124/ijisrt/ IJISRT24AUG154
  224. Q. Gao, Y. Chen, Z. Ju, and Y. Liang, “Dynamic hand gesture recognition based on 3d hand pose estimation for human-robot interaction,” IEEE Sensors Journal, vol. 22, no. 18, pp. 17 421 – 17 430, Sep. 2022. [Online]. Available: http://dx.doi.org/10.1109/JSEN.2021.3059685
  225. M. Alonazi, H. Ansar, N. A. Mudawi, S. S. Alotaibi, N. A. Almujally, A. Alazeb, A. Jalal, J. Kim, and M. Min, “Smart healthcare hand gesture recognition using cnn-based detector and deep belief network,” IEEE Access, vol. 11, pp. 84 922 – 84 933, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023.3289389
  226. G. Park, V. K. Chandrasegar, and J. Koh, “Accuracy enhancement of hand gesture recognition using cnn,” IEEE Access, vol. 11, pp. 26 496 – 26 501, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023.3254537
  227. M. Zhang, Z. Zhou, T. Wang, and W. Zhou, “A lightweight network deployed on arm devices for hand gesture recognition,” IEEE Access, vol. 11, pp. 45 493 – 45 503, 2023. [Online]. Available: http://dx.doi.org/10. 1109/ACCESS.2023.3273713
  228. S. Savas and A. Erg¨uzen, “Hand gesture recognition with two stage approach using transfer learning and deep ensemble learning,” 2023. [Online]. Available: https: //arxiv.org/abs/2309.11610
  229. F. Jafari and A. Basu, “Two-dimensional parallel spatiotemporal pyramid pooling for hand gesture recognition,” IEEE Access, vol. 11, pp. 133 755 – 133 766, 2023. [Online]. Available: http://dx.doi.org/10.1109/ACCESS.2023. 3336591
  230. T. L. Dang, S. D. Tran, T. H. Nguyen, S. Kim, and N. Monet, “An improved hand gesture recognition system using keypoints and hand bounding boxes,” ScienceDirect, vol. 16, p. 100251, Dec. 2022. [Online]. Available: http://dx.doi.org/10.1016/j.array.2022.100251
  231. X. Pan, T. Jiang, X. Li, X. Ding, Y. Wang, and Y. Li, “Dynamic hand gesture detection and recognition with wifi signal based on 1d-cnn,” pp. 1 – 6, May 2019. [Online]. Available: http://dx.doi.org/10.1109/ICCW.2019.8756690
  232. V. Chang, R. O. Eniola, L. Golightly, and Q. A. Xu, “An exploration into human-computer interaction: Hand gesture recognition management in a challenging environment,” SN Computer Science, vol. 4, no. 5, Jun. 2023. [Online]. Available: http://dx.doi.org/10.1007/s42979-023-01751-y
  233. J. Shin, A. S. M. Miah, S. Konnai, I. Takahashi, and K. Hirooka, “Hand gesture recognition using semg signals with a multi-stream time-varying feature enhancement approach,” Scientific Reports, vol. 14, no. 1, Sep. 2024. [Online]. Available: http://dx.doi.org/10.1038/s41598-024-72996-7
  234. R. K. Pathan, M. Biswas, S. Yasmin, M. U. Khandaker, M. Salman, and A. A. F. Youssef, “Retracted article: Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network,” Scientific Reports, vol. 13, no. 1, Oct. 2023. [Online]. Available: http://dx.doi.org/10.1038/ s41598-023-43852-x
  235. J. Yu, M. Qin, and S. Zhou, “Dynamic gesture recognition based on 2d convolutional neural network and feature fusion,” Scientific Reports, vol. 12, no. 1, Mar. 2022. [Online]. Available: http://dx.doi.org/10.1038/s41598-022-08133-z
  236. H. Li and H. Guo, “Design of bionic robotic hand gesture recognition system based on machine vision,” pp. 960 – 964, May 2022. [Online]. Available: http: //dx.doi.org/10.1109/CVIDLICCEA56201.2022.9825148
  237. A. Kaur and S. Bansal, “Deep learning for dynamic hand gesture recognition: Applications, challenges and future scope,” pp. 1–6, Nov. 2022. [Online]. Available: http://dx.doi.org/10.1109/IMPACT55510.2022.10029100
  238. H. Arwoko, E. M. Yuniarno, and M. H. Purnomo, “Hand gesture recognition based on keypoint vector,” pp. 530 – 533, Aug. 2022. [Online]. Available: http: //dx.doi.org/10.1109/IES55876.2022.9888333
  239. R. A. Muchtar, R. Yuniarti, and A. Komarudin, “Hand gesture recognition for controlling game objects using two-stream faster region convolutional neural networks methods,” pp. 59 – 64, Nov. 2022. [Online]. Available: http://dx.doi.org/10.1109/ICITRI56423.2022.9970207
  240. Y. Wang, J. Zhang, and X. Zhao, “Research on hand gesture recognition based on millimeter wave radar,” pp. 205 – 209, Jan. 2023. [Online]. Available: http: //dx.doi.org/10.1109/ICCECE58074.2023.10135494
  241. G. Beneke, T. B. Winkler, K. Raab, M. A. Brems, F. Kammerbauer, P. Gerhards, K. Knobloch, S. Krishnia, J. H. Mentink, and M. Kl¨aui, “Gesture recognition with brownian reservoir computing using geometrically confined skyrmion dynamics,” Nature Communications, vol. 15, no. 1, Sep. 2024. [Online]. Available: http: //dx.doi.org/10.1038/s41467-024-52345-y
  242. L. Zhao, Z. Yu, and J. Cai, “A novel method for semg-based hand gesture recognition,” pp. 1668 – 1672, Apr. 2023. [Online]. Available: http://dx.doi.org/10.1109/ICSP58490. 2023.10248486
  243. S. Mesdaghi, R. P. Hasanzadeh, and F. Janabi- Sharifi, “Finger-hand rehabilitation using dnn-based gesture recognition of low-cost webcam images,” pp. 1 – 6, Mar. 2024. [Online]. Available: http: //dx.doi.org/10.1109/MVIP62238.2024.10491167
  244. R. Kushwaha, G. Kaur, and M. Kumar, “Hand gesture based sign language recognition using deep learning,” pp. 293 – 297, May 2023. [Online]. Available: http: //dx.doi.org/10.1109/ICSCCC58608.2023.10176912
  245. F. Roumiassa, S. E. Agab, and F. Z. Chelali, “Hand gesture recognition system based on textural features,” pp. 1 – 6, Oct. 2022. [Online]. Available: http://dx.doi.org/10.1109/ ICAEE53772.2022.9962080
  246. B. Sharma and J. Panda, “Hand gesture recognition using emd and vmd techniques,” pp. 1 – 5, Dec. 2022. [Online]. Available: http://dx.doi.org/10.1109/IATMSI56455. 2022.10119304
  247. O. Ikne, B. Allaert, and H. Wannous, “Skeleton-based selfsupervised feature extraction for improved dynamic hand gesture recognition,” pp. 1 – 10, May 2024. [Online]. Available: http://dx.doi.org/10.1109/FG59268.2024.10581975
  248. A. Al-Zebari, N. Omar, and A. Sengur, “Vision transformers-based hand gesture classification,” pp. 1 – 3, Dec. 2022. [Online]. Available: http: //dx.doi.org/10.1109/IISEC56263.2022.9998295
  249. F. Noble, M. Xu, and F. Alam, “Static hand gesture recognition using capacitive sensing and machine learning,” Sensors, vol. 23, no. 7, p. 3419, Mar. 2023. [Online]. Available: http://dx.doi.org/10.3390/s23073419
  250. L. I. Khalaf, S. A. Aswad, S. R. Ahmed, B. Makki, and M. R. Ahmed, “Survey on recognition hand gesture by using data mining algorithms,” pp. 1 – 4, Jun. 2022. [Online]. Available: http://dx.doi.org/10.1109/HORA55278. 2022.9800090
  251. D. G. Leon, J. Groli, S. R. Yeduri, D. Rossier, R. Mosqueron, O. J. Pandey, and L. R. Cenkeramaddi, “Video hand gestures recognition using depth camera and lightweight cnn,” IEEE Sensors Journal, vol. 22, no. 14, pp. 14 610 – 14 619, Jul. 2022. [Online]. Available: http: //dx.doi.org/10.1109/JSEN.2022.3181518
  252. H. Quinti´an, E. Corchado, A. T. Lora, H. P. Garcia, E. J. Perez, J. L. C. Rolle, F. J. M. de Pison, P. G. Bringas, F. M. Alvarez, A. Herrero, and P. Fosci, Eds., Hybrid Artificial Intelligent Systems: 19th International Conference, HAIS 2024, Salamanca, Spain, October 9–11, 2024, Proceedings, Part I, ser. Lecture Notes in Computer Science. Springer Nature Switzerland, 2024.
Index Terms

Computer Science
Information Sciences
Human-Computer Interaction
Machine Learning

Keywords

Hand Gesture Recognition Deep Learning LSTM Multimodal Fusion Lightweight Architectures

Powered by PhDFocusTM