AI-BASED REAL-TIME HAND GESTURE RECOGNITION SYSTEM

Abstract

The present invention discloses an innovative approach to human-computer interaction by enabling accurate and responsive gesture recognition through advanced artificial intelligence techniques. The system utilizes a computer vision module to capture real-time images of the user’s hand, coupled with a hand detection algorithm and finger segmentation module for precise identification of gestures. Employing machine learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the system recognizes and classifies dynamic hand gestures with high accuracy. It also integrates depth-sensing technology to enhance recognition in three-dimensional space, offering versatile feedback mechanisms, such as visual or auditory responses. Designed for various applications, including gaming, virtual reality, and assistive technologies, this system significantly improves user interaction and accessibility by leveraging adaptive learning capabilities for personalized gesture recognition. Ac

Specification

Description:[001] The present invention relates to the field of human-computer interaction (HCI), specifically to an artificial intelligence (AI)-based real-time hand gesture recognition system. The invention provides a novel approach for recognizing hand motions, which are critical for enabling intuitive communication between humans and computers. This invention has applications in gesture-based control systems, virtual reality, and assistive technologies.
BACKGROUND OF THE INVENTION
[002] Human-computer interaction (HCI) has become increasingly important as technological advancements demand more intuitive and seamless ways of communication between users and machines. Hand gesture recognition, a natural form of human interaction, offers significant potential in this area. It allows users to control and communicate with computers, smartphones, or other devices without physical contact, making it useful in a variety of applications such as gaming, virtual reality, robotics, sign language interpretation, and assistive tech , Claims:1. A system for real-time hand gesture recognition, comprising:
a) a computer vision module configured to capture images of a user's hand using a camera;
b) a hand detection algorithm that identifies and isolates the hand from the captured images;
c) a finger segmentation module that recognizes individual fingers and their positions;
d) a gesture recognition engine employing a machine learning model to classify and predict hand gestures based on the detected features; and
e) a feedback mechanism that provides real-time responses to the recognized gestures.
2. The system as claimed in Claim 1, wherein the machine learning model utilizes Convolutional Neural Networks (CNNs) for feature extraction from the captured images.
3. The system as claimed in Claim 1, further includes depth-sensing technology integrated with the camera to enhance gesture recognition accuracy in three-dimensional space.
4. A method for recognizing hand gestures in real time, comprising the steps of:
i. capturing video data of a user's han

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