Model-Based Occupant Tracking Using Slab-Vibration Measurements

Abstract

Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement, and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies are based on data-driven techniques that do not include a physics-based model of the structural behavior of the floor slab. These techniques suffer from ambiguous interpretations when signals are affected by complex configurations of structural and non-structural elements such as beams and walls. Using a physics-based model for data-interpretation enables deployment of sparse number of sensors in contexts of non-uniform structural configurations. In this paper, an application of physics-based data interpretation using error-domain model falsification (EDMF) is presented to track an occupant within an office environment through footstep-induced floor vibrations. EDMF is a population-based approach that incorporates various sources of uncertainty, including bias, arising from measurements and modeling. EDMF involves the rejection of simulated model responses that contradict footstep-induced floor vibration measurements. Thus, EDMF provides a set of candidate locations from an initial population of possible occupant locations. A sequential analysis that accommodates information from previous footsteps is then used to enhance candidate locations and identify trajectories among candidates. In this way, incorporating structural behavior in interpreting vibration measurements induced by occupant footsteps has the potential to identify accurately the trajectory of an occupant in buildings with complex configurations, thereby providing tracking information without undermining privacy.

Model-Class Selection Using Clustering and Classification for Structural Identification and Prediction

“…The EDMF methodology has been applied to more than 20 full-scale systems since 1998 (Smith 2016). Recent applications include: model identification (Pai et al 2018); leak detection in water supply (Moser et al 2015); wind simulation (Vernay et al 2015); fatigue life evaluation (Pasquier et al 2014;; Pai and Smith 2017); measurement-system design (Papadopoulou et al 2015;Papadopoulou et al 2016); post-earthquake assessment (Reuland et al 2019); damage localization in tensegrity structures (Sychterz and Smith 2018); and occupant localization (Reuland et al 2017b;Drira et al 2019).…”

Occupant Activity Detection in Smart Buildings: A Review

“…With this new source of information and by extending conventional localization algorithms, that allow to correct signal distortions caused by vibrations of building structures, it will be possible to locate one person [54] or multiple persons moving within a building [53,55]. A little different work is presented in [56], which is able to tracking a person, based on the knowledge of the structural behaviour of the floor slab.…”

Uncertainties in Structural Behavior for Model-Based Occupant Localization Using Floor Vibrations

“…Alternative sensing approaches such as CO 2 sensors (Candanedo and Feldheim 2016;Jiang et al, 2016), acoustic devices (Bian et al, 2005;Desai et al, 2011), smart-flooring systems (Yun, 2011;Serra et al, 2016) and vibration sensors (Richman et al, 2001;Mirshekari et al, 2018;Drira et al, 2019b) protect the privacy of occupants. A limitation of CO 2 -based approaches has been related to the slow spreading of CO 2 within an indoor space.…”

“…Triangulation-based techniques are affected by the low signal-to-noize ratio (SNR) vibrations that result from the dispersive nature of typical floor slabs. Also, more complex structural configurations (upper floors) and the presence of elements (such as beams and walls) have limited their applicability (Bahroun et al, 2014;Mirshekari et al, 2018;Drira et al, 2019b). This has led to ambiguous localization results.…”