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Capacitive 3D user tracking with a mobile demonstration platform

Published: 03 October 2012 Publication History

Abstract

In this paper, we describe a 3D mobile demonstration platform that can passively track the location and posture of multiple persons walking on a segmented floor. The presented tracking system is fully unobtrusive and requires the user to neither wear any tags or perform any special action, such as talk, to be tracked. Thus, it promotes the concept of calm technology by pushing the sensing actions to the background. The system is based on capacitive measurements in transmit-mode which are performed using a commercial capacitance-to-digital converter. The user position on the floor level is calculated by measuring the placement of feet on different transmitting floor segments. The vertical head position is measured using a large receiver electrode placed above the tracking area and the result is used to determine the user posture. The implemented system is provided for hands-on evaluation for conference attendees with a 1.8x3.0 m floor space which enables the tracking of five persons at the same time. The position and the three-state posture of the persons walking on the floor are displayed to the demo attendees in real-time.

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Cited By

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  • (2020)Neural Networks for Indoor Human Activity ReconstructionsIEEE Sensors Journal10.1109/JSEN.2020.300600920:22(13571-13584)Online publication date: 15-Nov-2020
  • (2017)Performance of Machine Learning Classifiers for Indoor Person Localization With Capacitive SensorsIEEE Access10.1109/ACCESS.2017.27215385(12913-12926)Online publication date: 2017
  • (2016)A Tagless Indoor Localization System Based on Capacitive Sensing TechnologySensors10.3390/s1609144816:9(1448)Online publication date: 7-Sep-2016
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    cover image ACM Other conferences
    MindTrek '12: Proceeding of the 16th International Academic MindTrek Conference
    October 2012
    278 pages
    ISBN:9781450316378
    DOI:10.1145/2393132
    • Conference Chair:
    • Artur Lugmayr
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 October 2012

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    Author Tags

    1. 3D
    2. capacitive
    3. demo
    4. indoor positioning
    5. location
    6. user tracking

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    MindTrek '12 Paper Acceptance Rate 19 of 43 submissions, 44%;
    Overall Acceptance Rate 110 of 207 submissions, 53%

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    View all
    • (2020)Neural Networks for Indoor Human Activity ReconstructionsIEEE Sensors Journal10.1109/JSEN.2020.300600920:22(13571-13584)Online publication date: 15-Nov-2020
    • (2017)Performance of Machine Learning Classifiers for Indoor Person Localization With Capacitive SensorsIEEE Access10.1109/ACCESS.2017.27215385(12913-12926)Online publication date: 2017
    • (2016)A Tagless Indoor Localization System Based on Capacitive Sensing TechnologySensors10.3390/s1609144816:9(1448)Online publication date: 7-Sep-2016
    • (2013)Gesture Control System for Smart EnvironmentsProceedings of the 2013 9th International Conference on Intelligent Environments10.1109/IE.2013.16(232-235)Online publication date: 16-Jul-2013

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