ENHANCING AI-TOUCH FREE HAND GESTURE BASED HUMAN COMPUTER INTERACTION

Abstract

In the era of touch-based Human-Computer Interaction (HCI) systems, there is a growing need to develop touch-free
methodologies that improve user interaction. This research leverages rapid advancements in Artificial Intelligence (AI)
technology, with the primary goal of enhancing touch-free HCI through the integration of AI-based hand gestures. The system
aims to facilitate control without the need for traditional peripherals, such as a keyboard or mouse. Our system marks a significant
advancement in reducing reliance on conventional input devices, utilizing a webcam as the primary input device. The approach
combines several Python packages, including OpenCV, MediaPy, AutoGUI, PyTorch, and TensorFlow, to track hand movements
and execute system actions. This paper introduces a camera-based vision control system that uses a sophisticated hand gesture
algorithm, powered by machine learning models, to interpret finger gestures for managing various system functions. The system
enables users to control the system cursor using natural hand and finger gestures, allowing precise control over cursor movements,
clicks, scrolling, application launches, and the execution of keyboard shortcuts. This technological advancement significantly
enhances human-computer interaction, providing a more interactive and user-friendly computing experience. By seamlessly
integrating OpenCV, MediaPy, AutoGUI, PyTorch, TensorFlow, and a robust machine learning model, our approach
demonstrates innovation in touch-free HCI. It also establishes a comprehensive framework for the development of future
interactive systems. The utilization of these Python tools enhances the efficiency and scalability of the proposed system, making
it a promising step toward the evolution of intuitive and accessible computing interfaces