GESTURE BASED SIGN LANGUAGE RECOGNIZATION USING NINTENDO POWER GLOVE DATA

Abstract

Gesture sign language recognition is a critical area of research that aims to bridge communication gaps between individuals with hearing impairments and the rest of the world. It is a cutting-edge technology that bridges the communication gap between individuals with hearing impairments and those who do not understand traditional sign languages. Sign languages are rich and complex visual-spatial languages used by deaf and hard-of-hearing individuals to communicate ideas, emotions, and information. Recognizing and interpreting these gestures accurately is crucial for facilitating effective communication and social inclusion for people with hearing disabilities. Traditional sign language recognition systems often relied on rule-based methods and limited datasets, leading to challenges in accurately capturing the nuances of sign language gestures. These systems lacked the ability to adapt to different sign language variations and individual signing styles. The integration of machine learning techniques allows for the development of more adaptive and accurate sign language recognition systems. Machine learning algorithms can be trained on this dataset to recognize a wide range of sign language gestures. Machine learning, especially when applied to datasets like Nintendo Power Glove Data, offers a promising avenue for more accurate and real-time recognition of sign language gestures. Therefore, this research aims to build a machine learning-based approach utilizing Nintendo Power Glove Data holds immense potential in revolutionizing gesture sign language recognition. By leveraging the power of machine learning, the proposed system creates a more accurate, adaptable, and real-time sign language recognition systems, significantly improving the lives of individuals within the deaf and hard-of-hearing communities.