Increasing occupant localization precision through identification of footstep-contact dynamics

Abstract

Information regarding occupants inside buildings has the potential to improve security, energy management, and caregiving. Typical sensing approaches for occupant localization rely on mobile devices and cameras. These systems compromise privacy. Occupant localization using floor-vibration measurements, induced by footsteps, is a non-intrusive sensing method that requires few sensors (one per ~35 m2). Current occupant-localization methodologies that rely on vibration measurements are data-driven techniques. These techniques do not account for the structural behavior of floor slabs leading to ambiguous interpretations of vibrations measurement in the presence of obstructions and varying floor rigidities. In this paper, a model-based approach using error-domain model falsification (EDMF) is used to overcome these limitations. EDMF incorporates information related to physics-based models in the interpretation of vibration measurements to identify a population of possible occupant locations. EDMF accommodates systematic errors and model bias to reject models that contradict measurement data. Uncertainties from multiple sources such as modeling imperfection and walking-gait variability are included explicitly while estimating occupant locations using EDMF. A unique approach to identify footstep-contact dynamics is proposed and evaluated for its ability to improve the precision of occupant localization. The approach involves dividing the floor-slab into zones using knowledge of structural behavior. Clustering measured vibrations to define several footstep-contact severity levels helps reduce uncertainty in walking gait thus improving the accuracy of footstep-contact dynamics to use as loading input into model simulations. The utility of occupant localization using this approach is evaluated using a full-scale case study. Localization precision increased by more than 50% compared with non-zone-based strategies.