Design and Implementation of an EMG-Based Communication Interface for Enhancing Interaction with Deaf Individuals

This paper presents the development of an Electromyography (EMG)-based communication interface aimed at improving interactions with deaf and hard of hearing people. Traditional communication methods often fall short in providing efficient and seamless interaction with the deaf community. To address this, our interface utilizes EMG technology to interpret muscle movements and facial expressions, translating them into text and speech in real-time. The core of this system lies in its advanced EMG sensors coupled with a robust machine learning algorithm, which is trained on a dataset of EMG signals corresponding to specific domains. This allows the system to accurately interpret subtle muscle movements and convert them into corresponding textual and auditory outputs. The interface also integrates a feedback loop, where the user receives visual or tactile feedback, ensuring communication accuracy. Our system is designed to be non-invasive and user-friendly, making it accessible to a broad range of users within the deaf community. Preliminary tests have shown promising results in terms of accuracy and ease of use, indicating that this EMG-based interface could be a significant step forward in bridging the communication gap experienced by deaf individuals. Future work will focus on enhancing the system's portability and expanding its vocabulary and expression recognition capabilities to include a broader range of languages and dialects. This paper details the design considerations, development process, and initial testing phases of the EMG-based communication interface, providing insights into its potential impact on enhancing day-to-day interactions for the deaf community. Keywords: Sign language recognition, electromyography, Arabic sign language, Dynamic gestures recognition