KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911444728020992 |
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| author | Li, Tony Ma, Yan Li, Zhuojun Yu, Chun Ramakrishnan, IV Bi, Xiaojun |
| author_facet | Li, Tony Ma, Yan Li, Zhuojun Yu, Chun Ramakrishnan, IV Bi, Xiaojun |
| contents | 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 using cognitive-motor timing patterns, and then uses a fine-tuned LLM decoder to convert the resulting noisy letter sequence into the intended word. In controlled component tests, the decoder substantially outperforms two 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12432 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens Li, Tony Ma, Yan Li, Zhuojun Yu, Chun Ramakrishnan, IV Bi, Xiaojun Human-Computer Interaction 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 using cognitive-motor timing patterns, and then uses a fine-tuned LLM decoder to convert the resulting noisy letter sequence into the intended word. In controlled component tests, the decoder substantially outperforms two 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. |
| title | KeySense: LLM-Powered Hands-Down, Ten-Finger Typing on Commodity Touchscreens |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.12432 |