Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-020-18471-z
Title: Deep learning enabled smart mats as a scalable floor monitoring system
Authors: Shi, Qiongfeng 
Zhang, Zixuan 
He, Tianyiyi 
Sun, Zhongda
Wang, Bingjie
Feng, Yuqin
Shan, Xuechuan
Salam, Budiman
Lee, Chengkuo 
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
TRIBOELECTRIC NANOGENERATOR
SENSOR
MINIMALIST
INTERNET
THINGS
Issue Date: 14-Sep-2020
Publisher: NATURE RESEARCH
Citation: Shi, Qiongfeng, Zhang, Zixuan, He, Tianyiyi, Sun, Zhongda, Wang, Bingjie, Feng, Yuqin, Shan, Xuechuan, Salam, Budiman, Lee, Chengkuo (2020-09-14). Deep learning enabled smart mats as a scalable floor monitoring system. NATURE COMMUNICATIONS 11 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-020-18471-z
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.
Source Title: NATURE COMMUNICATIONS
URI: https://scholarbank.nus.edu.sg/handle/10635/189800
ISSN: 20411723
DOI: 10.1038/s41467-020-18471-z
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