Deep learning enabled smart mats as a scalable floor monitoring system

Qiongfeng Shi, Zixuan Zhang, Tianyiyi He, Zhongda Sun, Bingjie Wang, Yuqin Feng, Xuechuan Shan, Budiman Salam and Chengkuo Lee ()
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Qiongfeng Shi: National University of Singapore
Zixuan Zhang: National University of Singapore
Tianyiyi He: National University of Singapore
Zhongda Sun: National University of Singapore
Bingjie Wang: National University of Singapore
Yuqin Feng: National University of Singapore
Xuechuan Shan: National University of Singapore
Budiman Salam: National University of Singapore
Chengkuo Lee: National University of Singapore

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18471-z

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