International Journal of Science, Engineering and TechnologyAuthors: Mr.P. Loganathan, Beula C, Mathumitha M, Kavipriya K, Vishnupriya K
Abstract: The rapid advancement of computer vision and machine learning has opened transformative avenues for Human-Computer Interaction (HCI). Traditional input devices such as the mouse and keyboard impose physical constraints that make them inaccessible to users with motor impairments and impractical in sterile, hazardous, or presentation contexts. This paper presents the design and implementation of a Gesture Controlled Virtual Mouse system that enables touchless, real-time cursor control through hand gestures captured by a standard RGB webcam. The proposed system leverages Google's MediaPipe Hands framework for 21-point three-dimensional hand landmark detection, OpenCV for video acquisition and visual feedback rendering, and PyAutoGUI for system-level mouse event execution. A lightweight, rule-based gesture classifier interprets finger configuration vectors to map specific hand postures to mouse actions including cursor movement, left- click, right-click, double-click, scroll, and drag operations. Coordinate smoothing via an exponential moving average filter mitigates cursor jitter, and a time-based cooldown mechanism prevents unintended repeated click events. Experimental evaluation conducted across three lighting conditions on 100 trials per gesture demonstrates an overall gesture recognition accuracy of 92.8%, with cursor movement achieving 96% accuracy. The system operates at 26 frames per second on commodity hardware, yielding an end-to-end processing latency of approximately 38 milliseconds. A System Usability Scale evaluation with 15 participants yielded a score of 76.3 out of 100, indicating good usability. The proposed system presents a cost- effective, hardware-independent alternative to conventional mouse input and holds significant potential for accessibility, healthcare, education, and industrial applications.
DOI: http://doi.org/10.5281/zenodo.20438891
