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

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 online, 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.

Fine-Grained Activity of Daily Living (ADL) Recognition Through Heterogeneous Sensing Systems With Complementary Spatiotemporal Characteristics

“…It has been explored to obtain occupant information (Poston et al, 2017) including identity (Pan et al, 2017b), location (Mirshekari et al, 2018), heart rate (Jia et al, 2017), hand washing activities (Fagert et al, 2017), office activities (Bonde et al, 2020), etc. When the occupant interacts with ambient objects, such as floor, table, walls, bed, sink, etc., the interactions induce the structure to vibrate in a unique way such that their frequency components reflect the mode excited by different excitation sources (Fagert et al, 2017).…”

“…The structural vibration sensing captures the human interaction with the ambient environment, which is mostly the (Fagert et al, 2017;Han et al, 2017;Pan et al, 2017b;Poston et al, 2017;Mirshekari et al, 2018). It also captures the appliance machinery vibration, such as a motor or a compressor, as well as appliance usage induced vibration, such as water or food boiling.…”

“…The structural vibration can be used to infer the occupants' actions causing the vibration. When people interact with objects or structures around them, the interaction causes the surface of the object or structure to deform (Pan et al, 2017b;Poston et al, 2017;Mirshekari et al, 2018). The surface deformation causes mechanical waves dominated by the Rayleigh-Lamb wave.…”

A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination

“…Henceforth, the terms vibro-localization and vibro-measurements will refer to such localization techniques and the measurements used in these techniques, respectively. Vibro-localization techniques facilitate a myriad of applications ranging from smart home monitoring and event classification [1][2][3][4][5] to human gait assessment [6][7][8][9][10][11][12] and occupant identification and tracking [13][14][15][16][17][18][19][20].…”

Sensor Data Interpretation in Bridge Monitoring—A Case Study

“…Traditionally, measurements are taken during load tests to compare as-built performance with that expected from the design. Recently, structural responses have been used for other purposes as well, such as, construction monitoring and occupancy tracking (Pan et al, 2017;Poston et al, 2017;Harichandran et al, 2019). Conventionally, strain gauges and accelerometers are fixed on structures with wired connections to data acquisition systems.…”