Arduino-Based Gesture Recognition Glove: Bridging Communication Gaps in Sign Language
Keywords:
Accelerometers, Arduino-glove-based hardware system, Artificial intelligence, Communication, Flex sensors, Interconnected society, Machine learning algorithm, Microcontrollers, Mobile or computer screen movements, Predefined gesture mappings sign language, Real-time audio, Recognizing hand gestures, Sensor-integrated glove, Speech and text, Textual outputAbstract
Communication barriers frequently exist between individuals with disabilities and those without. A significant challenge arises from the lack of knowledge of sign language among many individuals, which hinders effective communication for those who experience difficulties in speaking and hearing.
To address this challenge, we introduce an Arduino-glove-based hardware system designed to translate sign language into both speech and text. This innovative system is capable of recognizing hand gestures, interpreting their meanings, and providing real-time audio and textual outputs to facilitate communication.
The system features a sensor-integrated glove equipped with flex sensors, accelerometers, and microcontrollers to detect finger and hand movements. The gestures are analyzed using a machine learning algorithm or through predefined gesture mappings, allowing for conversion into corresponding text and synthesized speech. The text is displayed on a mobile or computer screen, while the speech output enables immediate verbal communication, thereby enhancing interaction and accessibility.
This advancement aims to assist individuals with speech and hearing impairments in communicating more effectively with others in their communities. By leveraging wearable technology and artificial intelligence, the system promotes greater accessibility, encourages inclusion, and contributes to a more interconnected society.
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