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Performing indoor localization with electric potential sensing

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Abstract

Location-based services or smart home applications all depend on an accurate indoor positioning system. Basically one divides these systems into token-based and token-free localization systems. In this work, we focus on the token-free system based on smart floor technology. Smart floors can typically be built using pressure sensors or capacitive sensors. However, these set-ups are often hard to deploy as mechanical or electrical features are required below the surface and even harder to replace when detected a sensor malfunctioning. Therefore we present a novel indoor positioning system using an uncommon form of passive electric field sensing (EPS), which detects the electric potential variation caused by body movement. The EPS-based smart floor set-up is easy to install by deploying a grid of passive electrode wires underneath any non-conductive surfaces. Easy maintenance is also ensured by the fact that the sensors are not placed underneath the surface, but on the side. Due to the passive measuring nature, low power consumption is achieved as opposed to active capacitive measurement. Since we do not collect image data as in visual-based systems and all sensor data is processed locally, we preserve the user’s privacy. The proposed architecture achieves a high position accuracy and an excellent spatial resolution. Based on our evaluation conducted in our living lab, we measure a mean positioning error of only 12.7 cm.

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Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Research IGD, Fraunhoferstrasse 5, 64283, Darmstadt, Germany

    Biying Fu, Florian Kirchbuchner, Julian von Wilmsdorff, Tobias Grosse-Puppendahl, Andreas Braun & Arjan Kuijper

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  1. Biying Fu
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  2. Florian Kirchbuchner
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  3. Julian von Wilmsdorff
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  4. Tobias Grosse-Puppendahl
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  5. Andreas Braun
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  6. Arjan Kuijper
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Correspondence to Biying Fu.

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Fu, B., Kirchbuchner, F., von Wilmsdorff, J. et al. Performing indoor localization with electric potential sensing. J Ambient Intell Human Comput 10, 731–746 (2019). https://doi.org/10.1007/s12652-018-0879-z

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  • DOI: https://doi.org/10.1007/s12652-018-0879-z

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