Occupant Localization in Obstructive Indoor Environments Using Footstep-Induced Floor Vibrations

Abstract

In this paper, we characterize the effects of obstructions on footstep-induced floor vibrations to enable obstruction-invariant indoor occupant localization. Occupant localization is important in smart building applications such as healthcare and energy management. In prior works, footstep-based vibration sensing has been introduced for non-intrusive occupant localization in open areas. However, real buildings have various types of obstructions (e.g., walls, furniture, etc.) which affect the wave propagation characteristics and significantly reduce localization accuracy. Requiring unobstructed paths between footsteps and sensors results in higher instrumentation and maintenance cost for these prior works. We have observed that the obstruction mass is one of the key factors affecting localization accuracy by altering wave propagation velocity. Therefore, to overcome the obstruction challenge, we (1) detect and estimate the mass of the obstruction by characterizing the attenuation rate, and (2) model the velocity-mass relationship to find the propagation velocities which in turn are used for occupant localization through non-isotropic multilateration. In field experiments, we achieved an average localization error of 0.61 m, which is a 1.6X improvement compared to the baseline approach.