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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63363
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
E&TC
Vishwakarma Institute of Technology, Pune
E&TC
Vishwakarma Institute of Technology, Pune
E&TC
Vishwakarma Institute of Technology, Pune
This paper presents the design and implementation of a smart robotic arm capable of replicating human finger movements in real time using computer vision. The proposed system employs OpenCV-based hand-tracking algorithms to detect and interpret finger joint positions from live camera input. The extracted motion data is mapped to a 3-DoF finger model and actuated through 9g micro-servomotors, enabling dynamic gesture imitation. The hardware architecture integrates low-cost servos, a microcontroller, and a vision-processing unit to create an affordable yet responsive biomimetic platform. The system is developed to provide a natural, touchless mode of control without requiring wearable sensors, making it suitable for intuitive human–robot interaction. Experimental evaluation demonstrates that the robotic fingers follow human finger bending patterns with acceptable accuracy and latency for interactive applications. The system highlights the potential of vision-based control for assistive robotics, rehabilitation, remote manipulation in hazardous environments, and educational prototyping.
KEYWORDS
The Smart Robotic Hand using OpenCV provides an effective and intuitive solution for natural human–robot interaction. By using camera-based gesture recognition instead of wearable sensors, the system offers a contactless, user-friendly interface that enhances accessibility. Real-time landmark detection combined with servo-based actuation enables accurate finger movement replication with minimal latency. The use of low-cost servos, an arduino uno controller, and a lightweight 3D-printed structure ensures both affordability and efficiency, making the system suitable for educational, assistive, and research applications. Overall, advancements in compact vision-processing frameworks, lightweight 3D-printed mechanics, and efficient microcontroller-based actuation collectively contribute to a robust and user-friendly gesture-controlled robotic hand. The technology demonstrates strong potential for future developments in assistive robotics, rehabilitation devices, remote manipulation, and educational platforms where natural human–robot interaction is essential.
[1] J. Rane, O. Wadikar, A. S. Deshpande, and R. Sawant, “Robotic arm using hand gesture with AI,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 10, no. 5, pp. 460–463, May 2023. 2] B. Changela and A. Kataria, “Real-time robotic arm control using static hand gestures: An approach towards natural human machine interaction,” GRD Journals – Global Research and Development Journal for Engineering, vol. 5, no. 12, pp. 8–15, Nov. 2020. [3] N. N. Kumar, P. S. Tharun, K. Prakash, L. M. N. Mythresh, and N. Jyoshna, “Robot arm with gesture control,” Journal of Physics: Conference Series, vol. 2325, p. 012012, 2022, doi: 10.1088/1742-6596/2325/1/012012. [4] K. Krithika et al., “Computer vision-based gesture recognition systems: A technical review,” Journal of Engineering Science and Technology. [5] J. L. Raheja, R. Shyam, G. A. Rajsekhar, and P. B. Prasad, “Real-time robotic hand control using hand gestures,” in Robotic Systems: Applications, Control and Programming. InTech, 2012 [6] S. Mitra and T. Acharya, “Gesture recognition: A survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 37, no. 3, pp. 311–324, 2007. [7] L. Ge, S. Liang, J. Yuan, and N. Thalmann, “3D convolutional neural networks for real-time hand gesture recognition,” IEEE Transactions on Cybernetics, vol. 48, no. 9, pp. 2481–2493, 2018. [8] G. Bradski, “The OpenCV library,” Dr. Dobb’s Journal of Software Tools, 2000. [9] H. Zhu and C. Wang, “Development of a 3D printed prosthetic robotic hand for gesture control,” International Journal of Advanced Robotic Systems, vol. 16, no. 4, 2019.
Publication Date
5/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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