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Deep learning enabled smart mats as a scalable floor monitoring system

Author

Listed:
  • Qiongfeng Shi

    (National University of Singapore
    National University of Singapore
    National University of Singapore
    National University of Singapore Suzhou Research Institute (NUSRI))

  • Zixuan Zhang

    (National University of Singapore
    National University of Singapore
    National University of Singapore Suzhou Research Institute (NUSRI))

  • Tianyiyi He

    (National University of Singapore
    National University of Singapore
    National University of Singapore Suzhou Research Institute (NUSRI))

  • Zhongda Sun

    (National University of Singapore
    National University of Singapore
    National University of Singapore
    National University of Singapore Suzhou Research Institute (NUSRI))

  • Bingjie Wang

    (National University of Singapore
    National University of Singapore)

  • Yuqin Feng

    (National University of Singapore
    National University of Singapore)

  • Xuechuan Shan

    (National University of Singapore
    Agency for Science, Technology and Research (A*STAR))

  • Budiman Salam

    (National University of Singapore
    Agency for Science, Technology and Research (A*STAR))

  • Chengkuo Lee

    (National University of Singapore
    National University of Singapore
    National University of Singapore
    National University of Singapore Suzhou Research Institute (NUSRI))

Abstract

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

Suggested Citation

  • Qiongfeng Shi & Zixuan Zhang & Tianyiyi He & Zhongda Sun & Bingjie Wang & Yuqin Feng & Xuechuan Shan & Budiman Salam & Chengkuo Lee, 2020. "Deep learning enabled smart mats as a scalable floor monitoring system," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18471-z
    DOI: 10.1038/s41467-020-18471-z
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    Cited by:

    1. Yijia Lu & Han Tian & Jia Cheng & Fei Zhu & Bin Liu & Shanshan Wei & Linhong Ji & Zhong Lin Wang, 2022. "Decoding lip language using triboelectric sensors with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-12, 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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