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

Abstract

In sensed buildings, information related to occupant movement helps optimize functions such as security, energy management, and caregiving. Due to privacy needs, non-intrusive sensing approaches for tracking occupants inside buildings, such as vibration sensors, are often preferred over intrusive strategies that involve use of cameras and wearable devices. Current sensor-based occupant-localization approaches are data-driven techniques that do not account for structural behavior and limited to slabs on grade. Varying-rigidity floors and inherent variability in walking gaits lead to ambiguous interpretations of floor vibrations when performing model-free occupant localization. In this paper, an extensive analysis of vibrations induced by a range of occupants is described. Firstly, the need for a structural-behavior model for occupant localization is assessed using two full-scale case studies. Structural behavior is found to significantly influence floor vibrations induced by footstep impacts. Since a simple relationship between distances from footstep-impact to sensor locations cannot be assured, the use of physics-based models is necessary for accurate occupant localization. Secondly, measured data are interpreted using physics-based models and information related to uncertainties from multiple sources. There are two types of uncertainties: modelling uncertainties and measurement uncertainties, including variability in walking gaits. Error-domain model falsification (EDMF) and residual minimization (RM) are model-based approaches for data interpretation. Unlike RM, EDMF explicitly accounts for the presence of systematic errors in parameters and overall model bias. In this paper, model-based occupant localization is carried out using EDMF and RM on a full-scale case study. By explicitly accounting for the presence of uncertainties and the influence of structural behavior, EDMF, unlike RM, accurately reveals possible occupant locations on floor slabs.

AutoLoc: Autonomous Sensor Location Configuration via Cross Modal Sensing

“…Physical vibration signals induced by people in the buildings are used to indirectly infer human information for both physical and physiology information, include and not limited to identity (Pan et al, 2017 ), location (Mirshekari et al, 2018 ; Drira et al, 2021 ), activity (Hu et al, 2020 ; Sun et al, 2020 ), heart beat (Jia et al, 2016 ), and gait (Fagert et al, 2019 ). The intuition is that people induce physical vibrations all the time, such as stepping on the floor, heart-pounding in the chest, etc.…”