Authors: Dr. Kalyana Rajasekhar Babu
Abstract: Deaf and dumb individuals often face significant barriers in learning and engaging with team sports such as volleyball, primarily due to challenges in communication and instruction. Recent advancements in computer vision and machine learning have enabled the development of hand gesture recognition systems that can bridge this gap. This paper proposes a self-assistive tool that leverages hand gesture recognition for facilitating the learning of volleyball among deaf and dumb beginners. By integrating gesture interpretation, real-time feedback, and interactive instruction, this approach aims to foster inclusivity within sports education. Drawing upon recent studies in gesture recognition, human-computer interaction, and assistive technologies, this research outlines the system’s architecture, underlying algorithms, and potential impact on accessibility in sports training. The findings indicate that such tools, grounded in deep learning and computer vision frameworks, can empower deaf and dumb learners, enhance communication, and foster greater participation in athletic activities.
