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      Deep learning enabled smart mats as a scalable floor monitoring system

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          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.

          Abstract

          Designing efficient and fast monitoring and response systems for smart building/home applications remains a challenge. Here, the authors propose a smart floor monitoring system developed through the integration of self-powered triboelectric sensing mechanism and deep learning data analytics.

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          Most cited references36

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          Triboelectric Nanogenerator: A Foundation of the Energy for the New Era

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            On the origin of contact-electrification

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              Learning the signatures of the human grasp using a scalable tactile glove

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                Author and article information

                Contributors
                elelc@nus.edu.sg
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 September 2020
                14 September 2020
                2020
                : 11
                : 4609
                Affiliations
                [1 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Department of Electrical and Computer Engineering, , National University of Singapore, ; 4 Engineering Drive 3, Singapore, 117576 Singapore
                [2 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Singapore Institute of Manufacturing Technology and National University of Singapore (SIMTech-NUS) Joint Lab on Large-area Flexible Hybrid Electronics, , National University of Singapore, ; 4 Engineering Drive 3, Singapore, 117576 Singapore
                [3 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, Center for Intelligent Sensors and MEMS (CISM), , National University of Singapore, ; 5 Engineering Drive 1, Singapore, 117608 Singapore
                [4 ]GRID grid.452673.1, National University of Singapore Suzhou Research Institute (NUSRI), ; Suzhou Industrial Park, Suzhou, 215123 China
                [5 ]GRID grid.185448.4, ISNI 0000 0004 0637 0221, Printed Intelligent Device Group, Singapore Institute of Manufacturing Technology, , Agency for Science, Technology and Research (A*STAR), ; Singapore, 637662 Singapore
                [6 ]GRID grid.4280.e, ISNI 0000 0001 2180 6431, NUS Graduate School for Integrative Science and Engineering (NGS), , National University of Singapore, ; Singapore, 117456 Singapore
                Author information
                http://orcid.org/0000-0002-5979-1420
                http://orcid.org/0000-0001-7365-1945
                http://orcid.org/0000-0001-7731-2115
                http://orcid.org/0000-0002-8886-3649
                Article
                18471
                10.1038/s41467-020-18471-z
                7490371
                32929087
                93c34b13-fd26-40ab-86b7-e4cb2f577e56
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 March 2020
                : 25 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001348, Agency for Science, Technology and Research (A*STAR);
                Award ID: SIMTech-NUS Joint Lab
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001381, National Research Foundation Singapore (National Research Foundation-Prime Minister’s office, Republic of Singapore);
                Award ID: AISG-GC-2019-002
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                devices for energy harvesting,electrical and electronic engineering
                Uncategorized
                devices for energy harvesting, electrical and electronic engineering

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