BlindType: Eyes-Free Text Entry on Handheld Touchpad by Leveraging Thumb’s Muscle Memory

BlindType: Eyes-Free Text Entry on Handheld Touchpad by Leveraging Thumb’s Muscle Memory


Yiqin Lu, Chun Yu, Xin Yi, Yuanchun Shi, Shengdong Zhao



Eyes-free input is desirable for ubiquitous computing, since interacting with mobile and wearable devices often competes for visual attention with other devices and tasks. In this paper, we explore eyes-free typing on a touchpad using one thumb, wherein a user taps on an imaginary QWERTY keyboard while receiving text feedback on a separate screen. Our hypothesis is that users can transfer their typing ability obtained from visible keyboards to eyes-free use. We propose two statistical decoding algorithms to infer users’ eyes-free input: the absolute algorithm and the relative algorithm. The absolute algorithm infers user input based on the absolute position of touch endpoints, while the relative algorithm infers based on the vectors between successive touch endpoints. Evaluation results showed users could achieve satisfying performance with both algorithms. Text entry rate was 17-23 WPM (words per minute) depending on the algorithm used. In comparison, a baseline cursor-based text entry method yielded only 7.66 WPM. In conclusion, our research demonstrates for the first time the feasibility of thumb-based eyes-free typing, which provides a new possibility for text entry on ubiquitous computing platforms such as smart TVs and HMDs.


Shen is an HCI professor at the National University of Singapore working on realizing his vision of HeadsUp Computing, a new Interaction paradigm that can transform the way we live and interact with computers. In his free time, Shen loves to read, run, spend time with family and friends, and explore nature.

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