Breaking Barriers: Hand Gesture Vocalizer for the Deaf and Mute

Abstract

This study addresses communication challenges faced by deaf and mute individuals by exploring the feasibility of utilizing flex sensors and the Random Forest algorithm for hand gesture vocalization. The background underscores the significance of accessible communication tools in enhancing the lives of those with hearing and speech impairments. The study's purpose is to assess the effectiveness of flex sensors in detecting hand gestures and the Random Forest algorithm's potential to generate vocalized speech corresponding to these gestures. The methodology involves data collection from flex sensors through Arduino, Random Forest model training, and accuracy evaluation in gesture recognition. Promising results indicate the model's high accuracy in classifying diverse hand gestures. The study emphasizes the technology-driven solution's importance in bridging communication gaps for those with impairments. Combining flex sensors and the Random Forest algorithm offers an intuitive communication tool, transforming interactions for deaf and mute individuals. Consideration for real-world scenarios and user diversity during system development is highlighted, crucial for practical accuracy. Beyond individual communication, the study's implications span education, employment, and social integration for people with disabilities. Implementing this technology in education fosters inclusive environments, empowering deaf and mute students to engage actively. The integration of flex sensors and the Random Forest algorithm holds immense promise, revolutionizing communication, and life quality. As an accessible gesture-based vocalization tool, it can reshape societal perspectives, fostering inclusivity and empathy. The study advocates continuous research and development, urging widespread technology adoption to create an inclusive society valuing diversity.