Occupant localization using footstep-induced structural vibration

Abstract

Abstract In this paper, we present an occupant localization approach through sensing footstep-induced floor vibrations. Occupant location information is an important part of many smart building applications, such as energy and space management in a personal home or patient tracking in a hospital room. Adoption of current occupant location sensing approaches in smart buildings (e.g., camera, radio frequency (RF), mobile devices, etc.) is often limited due to the maintenance, installment, and calibration requirements of these sensing systems. To overcome these limitations, we introduce a new approach to use footstep-induced structural vibration for step-level occupant localization. The intuition behind this approach is that footsteps induce floor vibrations which are received in different vibration sensor locations at different times. This paper focuses on localizing a single occupant within each sensing range. To localize the footsteps, we utilize the time differences of arrival (TDoA) of the footstep-induced vibrations. However, this approach involves two main challenges: (1) the vibration wave propagation in the floor is of dispersive nature (i.e., different frequency components travel at different velocities) and (2) due to floor heterogeneity, these wave propagation velocities vary in different structures as well as in different locations in a structure. These issues lead to large localization inaccuracies or calibration requirements. To address dispersion challenge, we present a decomposition-based dispersion mitigation technique which extracts specific components (which have similar propagation characteristics) for localization. To address velocity variations, we introduce an adaptive multilateration approach that employs heuristics to constrain the search space and robustly locate the footsteps when the propagation velocity is unknown. Constraining the search space overcomes the additional complexity which is resulted from adding an unknown variable (propagation velocity). We evaluated our approach using the field experiments in 3 different types of buildings (both commercial and residential) with human participants. The results show an average localization error of 0.34 m, which corresponds to a 6X reduction in error compared to a baseline method. Furthermore, our approach resulted in standard deviation of as low as 0.18 m, which corresponds to a 8.7X improvement in precision compared to the baseline approach.