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A Framework for Occupancy Tracking in a Building via Structural Dynamics Sensing of Footstep Vibrations

Jeffrey D. Poston,R. Buehrer,P. Tarazaga

Published 2017 in Frontiers in Built Environment

ABSTRACT

Counting the number of occupants in building areas over time---occupancy tracking---provides valuable information for responding to emergencies, optimizing thermal conditions or managing personnel. This capability is distinct from tracking individual building occupants as they move within a building, has lower complexity than conventional tracking algorithms require and avoids privacy concerns that tracking individuals may pose. The approach proposed here is a novel combination of data analytics applied to measurements from a building's structural dynamics sensors (e.g., accelerometers or geophones). Specifically, measurements of footstep-generated structural waves provide evidence of occupancy in a building area. These footstep vibrations can be distinguished from other vibrations, and, once identified, the footsteps can be located. These locations, in turn, form the starting point of estimating occupancy in an area. In order to provide a meaningful occupancy count, however, it is first necessary to associate discrete footsteps with individuals. The proposed framework incorporates a tractable algorithm for this association task. The proposed algorithms operate on-line, updating occupancy count over time as new footsteps are detected. Experiments with measurements from a public building illustrate the operation of the proposed framework. This approach offers an advantage over others based on conventional technologies by avoiding the cost of a separate sensor system devoted to occupancy tracking.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    Frontiers in Built Environment

  • Publication date

    2017-11-06

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.3389/fbuil.2017.00065
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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