Human-machine interactions based on hand gesture recognition using deep learning methods

Abstract

Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and recurrent neural networks (RNN), which show excellent results in gesture recognition tasks. Next, the development and implementation of a human-machine interaction system based on hand gesture recognition is discussed. System architectures are described, as well as technical and practical aspects of their application. In conclusion, the article summarizes the research results and outlines the prospects for the development of hand gesture recognition technology to improve human-machine interaction. The advantages and limitations of the technology are analyzed, as well as possible areas of its application in the future.