From Motion to Action: Hand Gesture Recognition for Virtual Mouse and Keyboard

The rising need for intuitive and user-friendly digital interactions has shown hand gesture detection systems' potential as a novel solution in the field of human-computer interaction (HCI). This study presents a real-time, contactless, and transparent virtual mouse and keyboard system that converts hand gestures into functional input commands, replacing existing peripherals. Using expanded computer vision methods via frames such as OpenCV and Mediapipe, as well as libraries such as Pyautogui and Pynput, the system monitors and treats gestures with general calculation tasks with precision. The proposed approach solves critical problems, including the differences in hand forms, various environmental conditions, and the need to deal with low delay to ensure reactivity and precision. The system is designed to improve accessibility, reduce the physical strain associated with prolonged use of traditional input devices, and promote ergonomic interaction. It also demonstrates applicability across a variety of fields, including assistive technologies, intelligent environments, and immersive systems, thereby redefining the standards of digital interaction. This research contributes to the continuing evolution of HCI with a focus on improving accessibility, adaptability, and user experience in modern computing applications.