AI Vision for Health Care: Virtual Keyboard and Mouse Empowering Partially Disabled Patients
Keywords:
Virtual Keyboard, Virtual Mouse, YOLOv8, PyAutoGUI, Gesture Recognition, Computer Vision, CNN, OpenCV, TkinterAbstract
This paper introduces a machine-learning-based virtual keyboard and mouse system designed to assist individuals with physical disabilities. The system recognizes hand gestures using computer vision techniques and translates them into keyboard inputs and mouse controls. By utilizing Convolutional Neural Networks (CNNs) and the YOLOv8 model, the system achieves real-time performance with an average accuracy of 92%, enabling touchless interaction with computers. The solution uses widely available hardware like standard webcams, making it accessible, affordable, and easy to deploy. This system improves the usability of computing devices for people with motor impairments, offering an innovative, touchless alternative to traditional input methods. It also supports essential tasks such as scrolling, clicking, and zooming through simple gestures. The framework is adaptable to various environments, ensuring it is easy to use in different settings. Our system offers a complete virtual keyboard and mouse solution using a common webcam and real-time gesture recognition, making computer use easier and more affordable for users with motor impairment.
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