Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{199883,
author = {Samarth Kadam and Om Kadam and Mayuresh Shelke and Vilas Khedekar},
title = {Real-Time Monitor Controller Using Hand Gestures},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {12},
number = {11},
pages = {14772-14779},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=199883},
abstract = {Human-computer interaction has evolved significantly with the need for faster, more intuitive, and contactless control systems. Traditional input devices such as mouse and keyboard can be limiting, inconvenient, and unsuitable in situations requiring touch-free operation. This research focuses on the design and implementation of a Real-Time Monitor Controller using Hand Gestures, a system that allows users to control a computer using simple hand movements.
The proposed system uses computer vision and machine learning techniques to detect and interpret hand gestures in real time. It integrates technologies such as MediaPipe for hand landmark detection, OpenCV for image processing, and PyAutoGUI for translating gestures into system actions like cursor movement, clicking, and scrolling. By enabling gesture-based control through a webcam, the system eliminates the need for physical interaction and provides a natural and efficient user experience.
This study presents the working architecture, implementation methodology, and performance analysis of the system. It also highlights key challenges such as gesture accuracy, lighting conditions, and system responsiveness, while suggesting improvements for better usability. The solution is cost-effective, easy to implement, and useful in applications like accessibility, presentations, gaming, and hygiene-sensitive environments.},
keywords = {Hand Gesture Recognition, Computer Vision, Human-Computer Interaction, Real-Time Control, MediaPipe, OpenCV, PyAutoGUI, Image Processing, Machine Learning, Touchless Interface, Gesture-Based Control, Automation, Webcam Input, Cursor Control},
month = {April},
}
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