Abstract: This paper presents an eye movement input technology that dynamically adjusts key dwell time based on character prediction results.In this study,a character-level language model named TCN-AttnRNN is designed.In this model,TCN is responsible for extracting global spatial features and long-term dependencies of sequences,RNN enhances the long-term memory performance of time series,and the multi-head self-attention mechanism optimizes the network by allocating weights through probability distribution to enhance the role of key features.Experimental results show that the BPC values of the TCN-AttnRNN model on the PTB and DailyDialog datasets are 1.26 and 1.22 respectively,which are superior to the current mainstream TCN,LSTM,and Transformer models.Based on the TCN-AttnRNN model,a dynamic adjustment technology for eye movement input is designed.By using the TCN-AttnRNN model for character prediction,this technology adjusts the dwell time of keys according to the pro-bability of users' next key selection.Experimental results confirm the effectiveness of this technology,compared with the traditional fixed dwell time method,it increases users' input speed by 22.31% and reduces the correction error rate by 19.57%.

Key words: Human-computer interaction, Deep learning, Language modeling, Character-level language model, Eye movement input