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Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces

Author

Listed:
  • Taemin Kim

    (Yonsei University)

  • Yejee Shin

    (Yonsei University)

  • Kyowon Kang

    (Yonsei University)

  • Kiho Kim

    (Yonsei University)

  • Gwanho Kim

    (Yonsei University)

  • Yunsu Byeon

    (Yonsei University)

  • Hwayeon Kim

    (Yonsei University)

  • Yuyan Gao

    (The Pennsylvania State University)

  • Jeong Ryong Lee

    (Yonsei University)

  • Geonhui Son

    (Yonsei University)

  • Taeseong Kim

    (Yonsei University)

  • Yohan Jun

    (Yonsei University
    Massachusetts General Hospital
    Harvard Medical School)

  • Jihyun Kim

    (Yonsei University)

  • Jinyoung Lee

    (Yonsei University)

  • Seyun Um

    (Yonsei University)

  • Yoohwan Kwon

    (Yonsei University)

  • Byung Gwan Son

    (Yonsei University)

  • Myeongki Cho

    (Yonsei University)

  • Mingyu Sang

    (Yonsei University)

  • Jongwoon Shin

    (Yonsei University)

  • Kyubeen Kim

    (Yonsei University)

  • Jungmin Suh

    (Yonsei University)

  • Heekyeong Choi

    (Yonsei University)

  • Seokjun Hong

    (Yonsei University)

  • Huanyu Cheng

    (The Pennsylvania State University)

  • Hong-Goo Kang

    (Yonsei University)

  • Dosik Hwang

    (Yonsei University
    Yonsei University)

  • Ki Jun Yu

    (Yonsei University
    Yonsei University)

Abstract

A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (

Suggested Citation

  • Taemin Kim & Yejee Shin & Kyowon Kang & Kiho Kim & Gwanho Kim & Yunsu Byeon & Hwayeon Kim & Yuyan Gao & Jeong Ryong Lee & Geonhui Son & Taeseong Kim & Yohan Jun & Jihyun Kim & Jinyoung Lee & Seyun Um , 2022. "Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33457-9
    DOI: 10.1038/s41467-022-33457-9
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    References listed on IDEAS

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    Cited by:

    1. Shuya Zhu & Dingshun Yan & Yong Zhang & Liuliu Han & Dierk Raabe & Zhiming Li, 2024. "Strong and ductile Resinvar alloys with temperature- and time-independent resistivity," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    2. Kyowon Kang & Seongryeol Ye & Chanho Jeong & Jinmo Jeong & Yeong-sinn Ye & Jin-Young Jeong & Yu-Jin Kim & Selin Lim & Tae Hee Kim & Kyung Yeun Kim & Jong Uk Kim & Gwan In Kim & Do Hoon Chun & Kiho Kim, 2024. "Bionic artificial skin with a fully implantable wireless tactile sensory system for wound healing and restoring skin tactile function," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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