GazePinch: Text Entry for MR Using Any Hand with Pinch Gestures and Gaze
Xizhu Miao, LV Meng-ya, Boyang Li, Qianyun Wang, Lina Ban, Jing Zhao · International Journal of Human-Computer Interaction · 2025
This paper presents GazePinch, a one-handed text input technique that integrates gaze tracking and finger pinch gestures for efficient word-level text entry. It uses a dictionary tree with a Bayesian algorithm for dynamic word prediction, prioritizing words based on frequency. Four studies optimized GazePinch: Pilot Study 1 showed the QWERTY layout outperformed alphabetical for gaze input; Pilot Study 2 identified medium gaze block spacing as optimal; Study 3 assessed GazePinch’s learnability and performance, demonstrating its advantages over traditional input methods; and Study 4 showed that GazePinch achieved 47.37% faster text entry (14.87 WPM) and a 2.52% lower Text Entry Rate (3.57%) compared to the HoloLens2 Keyboard. Additionally, based on the Borg CR10 scale, GazePinch reduced perceived fatigue by 71.79%, significantly improving comfort during prolonged use. These results demonstrate GazePinch’s effectiveness for one-handed text input in MR environments.