Augmentative and Alternative Communication Using Eye Tracking and Word Recommendation Using Language Models

The production, storage, and dissemination of information have evolved from ancient communication methods to modern digital technologies, with digital media playing a key role in connecting individuals. While keyboards are common tools for interaction, they present challenges for individuals with motor impairments. Augmentative and Alternative Communication (AAC) techniques, including gesture input, voice commands, and sensor-based systems, have emerged to address these limitations. Eye tracking, used in accessibility systems, offers both opportunities and challenges, such as visual fatigue and inaccuracies that lead to slower typing. To address these challenges, this study proposes an interaction approach integrating eye movement tracking with a virtual keyboard, utilizing an artificial neural network to interpret gaze data and translate intentions within the interface at a low cost for the user. Additionally, a Language Model (LM) aids in predicting next-word suggestions. This research will assess the impact of these technologies on typing speed, error rate, and linguistic predictability, contributing both scientifically and societally to the advancement of accessible communication systems.