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A disposable cup inspired smart floor for trajectory recognition and human-interactive sensing

Author

Listed:
  • Zhao, Lin-Chuan
  • Zhou, Teng
  • Chang, Si-Deng
  • Zou, Hong-Xiang
  • Gao, Qiu-Hua
  • Wu, Zhi-Yuan
  • Yan, Ge
  • Wei, Ke-Xiang
  • Yeatman, Eric M.
  • Meng, Guang
  • Zhang, Wen-Ming

Abstract

Smart floor is an indispensable component of future smart buildings, it is urgent to develop a low-cost, self-powered, and high reliability smart floor. Herein, we propose a disposable cups inspired self-powered smart floor (DCIS-floor) for trajectory recognition and human-interactive sensing. The conical surface of the cup-shaped triboelectric nanogenerator (TENG) is greater than the projected area, resulting in an increased working area of functional materials on a limited floor. This enables more power generation units to be arranged on the limited floor while ensuring that each unit can generate sufficient electricity. Both pressure and shear force are applied as two conical surfaces contact, increasing the degree of contact between functional materials while avoiding excessive frictional force and wear during working process. Compared to cylindrical structures, conical structures offer greater flexibility in contact-separation without intricate machining and assembly, which is ideal for efficient large-area manufacturing. In the experiments, DCIS-floor achieves object motion trajectory recognition, visual recognition based trajectory wireless sensing, and pressure distribution sensing functions. Utilizing a convolutional neural network for data analysis, DCIS-floor realizes personnel identification. This work provides an effective method for smart floors in the safety monitoring, intelligent identification, and emergency rescue of future smart buildings.

Suggested Citation

  • Zhao, Lin-Chuan & Zhou, Teng & Chang, Si-Deng & Zou, Hong-Xiang & Gao, Qiu-Hua & Wu, Zhi-Yuan & Yan, Ge & Wei, Ke-Xiang & Yeatman, Eric M. & Meng, Guang & Zhang, Wen-Ming, 2024. "A disposable cup inspired smart floor for trajectory recognition and human-interactive sensing," Applied Energy, Elsevier, vol. 357(C).
    • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018883
    DOI: 10.1016/j.apenergy.2023.122524
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