Open Access
| Issue |
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 7 | |
| Section | Electronics Design | |
| DOI | https://doi.org/10.1051/itmconf/20268201011 | |
| Published online | 04 February 2026 | |
- J.W. Chen, R. Wang, F. Ding, B. Liu, L. Jiao, and J. Zhang, “A convolutional neural network with parallel multi-scale spatial pooling to detect temporal changes in SAR images,”Remote Sens., 12, 1619 (2020). https://doi.org/10.3390/rs12101619. [Google Scholar]
- M. Patel and V. Sharma, “Hand gesture recognition via lightweight VGG16 and ensemble classifier,” Appl. Sci., 12, 7643 (2022). https://doi.org/10.3390/app12157643 [Google Scholar]
- Y. Chen, H. Yuqing, and J. Zhao, “Lightweight neural network hand gesture recognition for embedded platforms,”High Power Laser Part. Beams, 34, 031023 (2022). https://doi.org/10.11884/HPLPB202234.210335 [Google Scholar]
- W. Jung and H. G. Lee, “Energy–accuracy aware finger gesture recognition for wearable IoT devices,” Sensors, 22, 4801 (2022).https://doi.org/10.3390/s22134801. [Google Scholar]
- L. Zhang, Q. Chen, and R. Hsu, “Real-time gesture recognition for human–computer interaction using deep keypoint models,” IEEE Access, 10, 22911–22922 (2022).https://doi.org/10.1109/ACCESS.2022.3148123 [Google Scholar]
- Althubiti, J. Zhao, and L. Wang, “Dynamic hand gesture recognition using MediaPipe and transformer networks,” Sensors, 23, 5567 (2023).https://doi.org/10.3390/s23125567 [Google Scholar]
- K. Santos and P. Rivera, “Gesture-controlled smart-home systems with AI-driven hand tracking,” Smart Home Technol.J.,9, 150–162 (2023).https://doi.org/10.1109/SHTJ.2023.0090150 [Google Scholar]
- C. Park, J. Lee, and B. Chung, “Robust hand pose estimation using keypoint-based CNN architectures,” Pattern Recognit. Lett., 180, 40–48(2023).https://doi.org/10.1016/j.patrec.2023.02.009 [Google Scholar]
- T. Huang, S. Wei, and P. Li, “Edge-friendly gesture recognition using quantized neural networks,” IEEE Internet Things J., 9, 14522–14533 (2022). https://doi.org/10.1109/JIOT.2022.3187112 [Google Scholar]
- R. Mehta and S. Pillai, “AI-based landmark extraction for real-time human–machine interaction,”IEEE Trans. Multimed., 26, 1452–1464 (2024). https://doi.org/10.1109/TMM.2024.0123456 [Google Scholar]
- L. Nguyen and F. Torres, “Optimized neural networks for edge-based gesture recognition,” IEEE Access, 11, 98723–98734 (2023). https://doi.org/10.1109/ACCESS.2023.3387654 [Google Scholar]
- G. Rahman and P. Singh, “Offline AI models for gesture-based IoT control,” J. Intell. IoT Syst., 6, 25–36 (2023).https://doi.org/10.1109/JIOTS.2023.0045632 [Google Scholar]
- H. Matsumoto, K. Ito, and K. Sato, “Efficient hand landmark estimation for low-power robotics,” Robot. Auton. Syst., 168, 104528(2023).https://doi.org/10.1016/j.robot.2023.1045 28 [Google Scholar]
- E. Wilson and F. Carter, “Vision-based human interaction for assistive technologies using lightweight neural models,” Assist. Technol., 33, 320–332 (2024). https://doi.org/10.1080/10400435.2023.2201559 [Google Scholar]
- P. Kumar and R. Shah, “Real-time offline hand gesture recognition for HCI applications,” IEEE Access, 12, 11234_11245(2024). https://doi.org/10.1109/ACCES S.2024.0156723 [Google Scholar]
- M. Fernandes, J. Lopes, and T. Almeida, “Efficient hand landmark tracking for low-power AI systems,” Int. J. Comput. Vis. Appl., 9, 210–221 (2023). https://doi.org/10.1007/s10044-023-01153-y [Google Scholar]
- S. Banerjee and A. Rao, “Gesture-controlled IoT systems using lightweight neural networks,” IEEE Internet Things J., 8, 987–998 (2022). https://doi.org/10.1109/JIOT.2022.3141258 [Google Scholar]
- L. Zhao and M. Wen, “Improved keypoint-based hand gesture detection using hybrid deep learning models,” Pattern Anal. Appl., 26, 1121–1134 (2023). https://doi.org/10.1007/s10044-023-01042-3 [Google Scholar]
- J. Lee and M. Hong, “High-speed gesture recognition for embedded AR systems,” IEEE Signal Process. Lett., 30, 144_148(2023).https://doi.org/10.1109/LSP.2023.32455 67 [Google Scholar]
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