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Word-Level Motion Learning for Contactless QWERTY Typing with a Single Camera.

Sung-Sic Yoo1, Heung-Shik Lee1

  • 1Department of Smart Mobility Engineering, Joongbu University, 305 Dongheon-ro, Deogyang-gu, Goyang-si 21713, Gyeonggi-do, Republic of Korea.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
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This study shows that contactless typing can be recognized at the word level using a single camera by analyzing finger motion patterns. This approach learns and adapts, offering a new method for camera-based text input.

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Vision-based text entry is crucial for immersive and constrained environments.
  • Current methods often rely on fragile character-level recognition or key localization with single cameras.
  • Monocular sensing presents challenges for accurate contactless text input.

Purpose of the Study:

  • To investigate the feasibility of word-level contactless typing recognition using a single RGB camera.
  • To develop a framework for recognizing typing motions directly as word-level patterns.
  • To assess the robustness and adaptability of the proposed method.

Main Methods:

  • A word-level contactless typing framework was developed, modeling words as spatiotemporal finger motion patterns from hand joint trajectories.
Keywords:
QWERTY keyboardcontactless text entryhand motion learningmonocular RGB cameravision-based typingword-level typing

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  • Typing motions were temporally segmented, and direction-aware finger displacements were accumulated for motion representation.
  • Online learning with a trial-delayed adaptation protocol was used to update word motion prototypes.

    • Main Results:

    • Experiments with up to 200 words demonstrated progressive learning and recall of word-level motion patterns.
    • Stable recognition performance was achieved at realistic typing speeds within the tested single-user, single-camera setup.
    • Learned motion representations transferred from physical keyboards to flat-surface typing, even with reduced tactile and visual cues.

    Conclusions:

    • Contactless typing can be effectively reframed as a word-level motion recall problem.
    • The proposed method shows potential as a complementary input method for constrained monocular sensing.
    • Word-level motion pattern recognition offers a robust alternative to character-centric approaches in specific scenarios.