In recent years, hand gesture recognition has been used in a variety of fields, especially in the area of man-machine interaction (MMI), where it is regarded as a more natural and versatile input than conventional input devices such as mice and keyboards. Since there is a high distance between the user and the machine, using a physical controlling device such as a keyboard and mouse for human interaction with the computer hinders the normal interface. Our goal is to solve this problem by developing an application that uses hand movements to monitor some of the basic computer functions through an integrated webcam. To make our tasks easy, a Hand Gesture Recognition device senses gestures and converts them to specific actions. With the aid of the Jester dataset, a model can be created using a 3D convolutional neural network and deep learning, which will be interfaced using Django, React.JS, and Electron. The key outcome predicted is that the user, using hand gestures, would be able to monitor the system's basic functions, providing them with the ultimate convenience.

Keywords : Human-Computer Interaction, Jester Dataset, 3D Convolutional Neural Network, Deep Learning