A text entry interface using smooth pursuit movements and language model

Abstract

Nowadays, with the development of eye tracking technology, the gaze-interaction applications demonstrate great potential. Smooth pursuit based gaze typing is an intuitive text entry system with low learning effort. In this study, we provide a language-prediction function for a smooth-pursuit based gaze-typing system. Since the state-of-the-art neural network models have been successfully applied in language modeling, this study uses a pretrained model based on convolutional neural networks (CNNs) and develops a prediction function, which can predict both next possible letters and word. The results of a pilot experiment have shown that the next possible letters or word can be well predicted and selected. The mean typing speed can achieve 4.5 words per minute. The participants consider that the word prediction is helpful for reducing the visual search time.