Communication is a fundamental human right. However, millions of deaf individuals worldwide continue to face significant barriers in their daily interactions due to the lack of accessible communication tools. This paper presents an IoT-enabled system, implemented as a wearable glove-based architecture, designed to improve communication between the deaf and hearing communities by translating Greek Sign Language (GSL) into text. The proposed system integrates wearable sensor technology with pattern-matching algorithms to accurately capture and interpret the Greek finger alphabet and gesture-based signs. Based on experimental evaluations, the system achieved an average recognition accuracy of 95.63%, while sensor faults were identified as the primary cause of misclassification when hand positions were altered. Through careful parameter tuning, such as threshold calibration, and algorithmic optimization for comparing sensor values against predefined thresholds, the translator demonstrated accurate and reliable performance in converting sign language gestures into written text, marking an important advancement in inclusive communication technologies. Despite notable progress in IoT-based wearable systems, a significant gap remains in achieving real-time, accurate, and language-specific translation of sign languages. Most existing approaches focus on generic gestures or widely used languages such as ASL, leaving Greek Sign Language (GSL) largely unexplored. Furthermore, limitations in expressivity, contextual understanding, and communication stability hinder real-world deployment. The proposed SpeakWithSigns+ system addresses these challenges by introducing an IoT-based wearable architecture that captures synchronized multisensor data, transmits it to a cloud server, and performs real-time pattern recognition for precise translation of GSL gestures. This development contributes to the advancement of inclusive, adaptive, and human-centered interaction technologies for the deaf and hard-of-hearing community.
Papatsimouli et al. (Mon,) studied this question.