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.
Highlights
Several research groups reported that this kind of instrumentation, namely, accelerometer or geophone sensors, could detect footstep-generated structural waves produced by building occupants (Dobbler et al, 2014; Hamilton et al, 2014; Pan et al, 2017)
With footstep-based localization as a starting point, this paper proposes an algorithmic framework for occupancy tracking
The aim of this research is to introduce an algorithmic framework for occupancy tracking derived from measurements of footstepgenerated vibrations
Summary
Several research groups reported that this kind of instrumentation, namely, accelerometer or geophone sensors, could detect footstep-generated structural waves produced by building occupants (Dobbler et al, 2014; Hamilton et al, 2014; Pan et al, 2017). This understanding enabled a number of independent approaches to locating occupants by means of their footstep vibrations [e.g., Bahroun et al (2014), Pan et al (2014), Mirshekari et al (2016), Poston et al (2017)]. With footstep-based localization as a starting point, this paper proposes an algorithmic framework for occupancy tracking This is valuable information for several applications. Occupancy tracking could augment current technology for personnel management and building security
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