KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens

要旨

Existing touchscreen software keyboards prevent users from resting their hands, forcing slow and fatiguing index-finger tapping (“chicken typing”) instead of familiar hands-down ten-finger typing. We present KeySense, a purely software solution that preserves physical keyboard motor skills. KeySense isolates intentional taps from resting-finger noise with cognitive–motor timing patterns, and then uses a fine-tuned LLM decoder to turn the resulting noisy letter sequence into the intended word. In controlled component tests, this decoder substantially outperforms 2 statistical baselines (top-1 accuracy 84.8% vs 75.7% and 79.3%). A 12-participant study shows clear ergonomic and performance benefits: compared with the conventional hover-style keyboard, users rated KeySense as markedly less physically demanding (NASA-TLX median 1.5 vs 4.0), and after brief practice, typed significantly faster (WPM 28.3 vs 26.2, p <0.01). These results indicate that KeySense enables accurate, efficient and comfortable ten-finger text entry on commodity touchscreens, without any extra hardware.

著者
Tony Li
Stony Brook University, Stony Brook, New York, United States
Yan Ma
Kean University, Union, New Jersey, United States
Zhuojun Li
Tsinghua University, Beijing, China
Chun Yu
Tsinghua University, Beijing, China
IV Ramakrishnan
Stony Brook University, Stony Brook, New York, United States
Xiaojun Bi
Stony Brook University, Stony Brook, New York, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Modeling Human Performance & Motion

P1 - Room 116
7 件の発表
2026-04-15 18:00:00
2026-04-15 19:30:00