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A deep-learned skin sensor decoding the epicentral human motions

Author

Listed:
  • Kyun Kyu Kim

    (Seoul National University)

  • InHo Ha

    (Seoul National University)

  • Min Kim

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Joonhwa Choi

    (Seoul National University)

  • Phillip Won

    (Seoul National University)

  • Sungho Jo

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Seung Hwan Ko

    (Seoul National University
    Seoul National University
    University of California)

Abstract

State monitoring of the complex system needs a large number of sensors. Especially, studies in soft electronics aim to attain complete measurement of the body, mapping various stimulations like temperature, electrophysiological signals, and mechanical strains. However, conventional approach requires many sensor networks that cover the entire curvilinear surfaces of the target area. We introduce a new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network. The device detects minute deformations from the unique laser-induced crack structures. A single skin sensor decodes the complex motion of five finger motions in real-time, and the rapid situation learning (RSL) ensures stable operation regardless of its position on the wrist. The sensor is also capable of extracting gait motions from pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.

Suggested Citation

  • Kyun Kyu Kim & InHo Ha & Min Kim & Joonhwa Choi & Phillip Won & Sungho Jo & Seung Hwan Ko, 2020. "A deep-learned skin sensor decoding the epicentral human motions," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16040-y
    DOI: 10.1038/s41467-020-16040-y
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    Cited by:

    1. Yeongju Jung & Kangkyu Kwon & Jinwoo Lee & Seung Hwan Ko, 2024. "Untethered soft actuators for soft standalone robotics," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Zhongda Sun & Minglu Zhu & Xuechuan Shan & Chengkuo Lee, 2022. "Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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