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

2026-02-12

Technology Category

• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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.

Problem

• touchscreen keyboard
• ten-finger typing
• hand resting
• text entry
• ergonomics

Innovation

• LLM-based decoding
• touchscreen typing
• motor skill preservation
• cognitive-motor timing
• hands-down input