Enhancing Hybrid Eye Typing Interfaces with Word and Letter Prediction: A Comprehensive Evaluation

Abstract

Eye typing interfaces enable a person to enter text into an interface using only their own eyes. But despite the inherent advantages of touchless operation and intuitive design, such eye-typing interfaces often suffer from slow typing speeds, resulting in slow words per minute (WPM) counts. In this study, we add word and letter prediction to the eye-typing interface and investigate users’ typing performance as well as their subjective experience while using the interface. In experiment 1, we compared three typing interfaces with letter prediction (LP), letter + word prediction (L + WP), and no prediction (NoP), respectively. We found that the interface with L + WP achieved the highest average text entry speed (5.48 WPM), followed by the interface with LP (3.42 WPM), and the interface with NoP (3.39 WPM). Participants were able to quickly understand the procedural design for word prediction and perceived this function as very helpful. Compared to LP and NoP, participants needed more time to familiarize themselves with L + WP in order to reach a plateau regarding text entry speed. Experiment 2 explored training effects in L + WP interfaces. Two moving speeds were implemented: slow (6.4${}^{°}$/s same speed as in experiment 1) and fast (10${}^{°}$/s). The study employed a mixed experimental design, incorporating moving speeds as a between-subjects factor, to evaluate its influence on typing performance throughout 10 consecutive training sessions. The results showed that the typing speed reached 6.17 WPM for the slow group and 7.35 WPM for the fast group after practice. Overall, the two experiments show that adding letter and word prediction to eye-typing interfaces increases typing speeds. We also find that more extended training is required to achieve these high typing speeds.

Keywords:

• Eye movement
• eye tracking
• smooth pursuit
• gaze interaction
• eye typing
• calibration-free
• word prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Zhe Zeng

Zhe Zeng received her M.S. and Ph.D. degrees from the Technical University of Berlin, Germany. Her research interests include eye tracking, natural gaze interaction, and human-robot interaction.

Xiao Wang

Xiao Wang received his M.S. degree from the Technical University of Berlin. His interests include human-machine interface.

Felix Wilhelm Siebert

Felix Wilhelm Siebert received his PhD degree in psychology from the Leuphana University of Lüeneburg, Germany. He holds an assistant professor position for transport psychology at the Department of Technology, Management and Economics at the Technical University of Denmark.

Hailong Liu

Hailong Liu is an Assistant Professor at Graduate School of Science and Technology, NAIST, Japan. He received his Ph.D. degree in Engineering from Ritsumeikan University, Japan in 2018. His research interests include HMI designs, driving behavior modeling, motion sickness modeling, and trust calibration for human-autonomous vehicle interactions.