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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 01004 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 01004
Number of page(s) 11
Section Robotics Design and Control
DOI https://doi.org/10.1051/epjconf/202636701004
Published online 29 April 2026
  1. Rautaray SS, Agrawal A. Vision based hand gesture recognition for human- computer interaction: a survey. Artif Intell Rev. 43(1):1–54. (2015) https://doi.org/10.1007/s10462-012-9356-9 [Google Scholar]
  2. Chen Y, Zhang L, Zhou H. Deep learning for hand gesture recognition: techniques and applications. IEEE Trans Pattern Anal Mach Intell. 42(8):1941–1960. (2020) https://doi.org/10.1109/ICDATE58146.2023.10248500 [Google Scholar]
  3. Oyedotun OK, Khashman A. Real-time hand gesture detection and recognition using deep learning. Appl Syst Innov. 1(3):21–31. (2017) A. Osman Hashi, S. Zaiton Mohd Hashim and A. Bte Asamah, “A Systematic Review of Hand Gesture Recognition: An Update From 2018 to 2024,” in IEEE Access, 12:143599-143626. (2024) https://doi.org/10.1109/ACCESS.2024.3421992 [Google Scholar]
  4. Christen S, Steiner W, Siegwart R. Learning human-to-robot handovers from point clouds. IEEE Robot Autom Lett. 6(2):3839–3846. (2021) https://doi.org/10.48550/arXiv.2303.17592 [Google Scholar]
  5. Molchanov P, Yang X, Gupta S, Kim K, Tyree S, Kautz J. Online Deetction and Classification of Dynamic hand gesture recognition using 3D convolutional neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 4207-4215. (2016) https://doi.org/10.1109/CVPR.2016.456 [Google Scholar]
  6. Mahmoud Tavakoli, Carlo Benussi, Joao Luis Lourenco, Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach, Expert Systems with Applications, 79:322–332, 2017. https://doi.org/10.1016/j.eswa.2017.03.012 [Google Scholar]
  7. Hongyi Liu, Lihui Wang, Gesture recognition for human-robot collaboration: A review, International Journal of Industrial Ergonomics, 68:355–367 (2018) https://doi.org/10.1016/j.ergon.2017.02.004 [Google Scholar]
  8. Natalia Neverova, Christian Wolf, Graham Taylor, and Florian Nebout. 2016. ModDrop: Adaptive Multi-Modal Gesture Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(8): 1692–1706. (2016). https://doi.org/10.1109/TPAMI.2015.2461544 [Google Scholar]
  9. Liang Zhang, Guangming Zhu, Lin Mei, Peiyi Shen, Syed Afaq Ali Shah, and Mohammed Bennamoun. 2018. Attention in convolutional LSTM for gesture recognition. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 1957–1966. [Google Scholar]
  10. R. Raffik, V. J. Balamurugan and N. G. Pandian, Edge Computing for Real-Time Decision-Making in Industrial Automation Systems - A Comprehensive Review, 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2025, pp. 1–6, https://doi.org/10.1109/ICAECA63854.2025.11012458 [Google Scholar]
  11. Martinez-Martin E, del Pobil AP. Human-inspired control strategies for collaborative robots using gesture recognition. Robot Auton Syst. 136:103713. (2021) https://doi.org/10.1016/j.robot.2020.103713 [Google Scholar]
  12. Hernandez J, Lopez A, Lopez R. Adaptive filtering techniques for gesture recognition in dynamic environments. Sensors. 20(8):2284–2300. (2020) https://doi.org/10.3390/s20082284 [Google Scholar]
  13. Aws Saood Mohamed, Nidaa Flaih Hassan, and Abeer Salim Jamil. 2024. Real- Time Hand Gesture Recognition: A Comprehensive Review of Techniques, Applications, and Challenges. Cybern. Inf. Technol. 24(3):163–181. (2024). https://doi.org/10.2478/cait-2024-0031 [Google Scholar]
  14. Martinez J, Garcia A, Wang T. Quantifying user fatigue in gesture-controlled interfaces. Int J Hum Comput Stud. 169:102956. (2023) https://doi.org/10.1016/j.ijhcs.2022.102956 [Google Scholar]
  15. Neto P, Pires JN, Moreira AP. Robotic arm control through human arm movement using accelerometers. Ind Robot. 40(3):213–221. (2013) https://doi.org/10.1108/01439911311309924 [Google Scholar]

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