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RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework.

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    IEEE Transactions on Visualization and Computer Graphics
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    PubMed
    Summary
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    RingGesture, a novel ring-based system, enables efficient mid-air gesture typing for augmented reality (AR) glasses. It achieves high text entry speeds and improved accuracy through its advanced Score Fusion word prediction framework.

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    Area of Science:

    • Human-Computer Interaction
    • Augmented Reality
    • Wearable Technology

    Background:

    • Lightweight AR glasses require efficient text entry solutions due to limited camera capabilities for hand tracking.
    • Existing input methods may not be suitable for all-day wearable AR devices.

    Purpose of the Study:

    • To introduce RingGesture, a ring-based mid-air gesture typing system for AR glasses.
    • To develop and evaluate the Score Fusion word prediction framework for enhanced accuracy and speed.

    Main Methods:

    • RingGesture utilizes electrodes and inertial measurement units (IMU) for gesture recognition and hand tracking.
    • The Score Fusion framework integrates a word-gesture decoding model, spatial spelling correction, and a contextual language model.

    Main Results:

    • RingGesture achieved an average text entry speed of 27.3 words per minute (WPM), with a peak of 47.9 WPM.
    • The Score Fusion framework improved uncorrected Character Error Rate by 28.2% and text entry speed by 55.2% compared to Naive Correction.
    • RingGesture received a System Usability Score of 83, indicating excellent usability.

    Conclusions:

    • RingGesture offers an effective and usable solution for text entry in lightweight AR glasses.
    • The Score Fusion framework significantly enhances the performance of mid-air gesture typing systems.