Synergistic Integration of Self-Powered, Flexible Wearables with Advanced Gesture Recognition for Deaf-Mute Communication

Abstract

This research confronts two pivotal challenges in advancing assistive communication devices for the deaf and mute community: enhancing the longevity of device operation through self-powered sensor technology and improving gesture recognition accuracy with sophisticated algorithms. The study introduces a novel design for flexible, wearable devices that harness kinetic and thermal energy, converting them into electrical energy to ensure continuous operation. This approach eliminates the need for frequent recharging, thereby increasing the autonomy of these assistive devices. In addressing the second challenge, the research refines deep learning algorithms to significantly boost the precision and accuracy of gesture recognition. These algorithms are trained on an extensive dataset that includes a broad spectrum of sign language gestures, captured by sensors on the wearable device. This methodology not only promises a breakthrough in the self-sufficiency of wearable assistive devices but also improves the effectiveness and usability of sign language to speech translation. Such advancements facilitate more seamless, barrier-free communication for individuals with hearing and speech impairments. Preliminary results demonstrate the potential of integrating self-powering capabilities with advanced gesture recognition technology, marking a considerable advancement in assistive technology.