Deep Learning-Based Real-Time AI Virtual Mouse System Using Computer Vision to Avoid COVID-19 Spread.

Area of Science:

• Computer Science
• Human-Computer Interaction (HCI)
• Artificial Intelligence (AI)

Background:

• Traditional computer mice, including wireless and Bluetooth models, still require physical devices, batteries, and dongles.
• Existing Human-Computer Interaction (HCI) methods often rely on physical input devices, posing limitations in certain environments.

Purpose of the Study:

• To develop an AI virtual mouse system that enables computer control through hand gestures.
• To eliminate the dependency on physical mice and reduce the need for device interaction.

Main Methods:

• Utilizes a webcam or built-in camera for capturing hand gestures and hand tip detection.
• Employs computer vision and machine learning algorithms, specifically deep learning, for accurate hand detection.
• Implements virtual control for cursor movement, left-click, right-click, and scrolling functions.

Main Results:

• The AI virtual mouse system successfully performs essential computer control functions (cursor movement, clicks, scrolling) using only hand gestures.
• Demonstrates the feasibility of a device-free computer interaction method.
• The system effectively detects hand gestures and tip points for virtual mouse operations.

Conclusions:

• The proposed AI virtual mouse system offers a novel, touch-free alternative to physical mice.
• This technology has the potential to enhance user interaction and reduce the spread of infections by minimizing physical contact.
• Further development in AI and computer vision can lead to more intuitive and accessible human-computer interfaces.