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Hand gesture recognition based on an optical fiber specklegram sensor.

Xinxin Wang, Tianhuan Li, Yi Zheng

    Optics Express
    |August 13, 2025
    PubMed
    Summary

    This study presents a novel optical fiber sensing system for accurate hand gesture recognition. Deep learning analysis of fiber specklegrams enables efficient human-computer interaction in wearable devices.

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    Area of Science:

    • Optoelectronics
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Traditional human-computer interaction methods can be cumbersome.
    • Optical fiber sensing offers potential for novel interaction modalities.
    • Gesture recognition is key to intuitive and efficient interfaces.

    Purpose of the Study:

    • To develop a novel hand gesture recognition system using optical fiber sensing.
    • To demonstrate the efficacy of deep learning for analyzing fiber specklegrams.
    • To create a stable, compact, and efficient gesture recognition solution.

    Main Methods:

    • A sensing structure based on single-mode, multimode, multicore optical fiber was designed.
    • Fiber specklegrams generated by hand gestures were captured.
    • A simplified six-layer ResNet deep learning network was trained to analyze the specklegrams.

    Main Results:

    • Accurate and efficient hand gesture recognition was achieved.
    • The system demonstrated high stability with minimal hardware requirements.
    • The compact design facilitates integration into wearable devices.

    Conclusions:

    • The proposed optical fiber sensing system offers a promising approach for advanced human-computer interaction.
    • Deep learning effectively decodes complex gesture information from fiber specklegrams.
    • The system's characteristics make it suitable for practical applications, particularly in wearables.