Step-Level Occupant Detection across Different Structures through Footstep-Induced Floor Vibration Using Model Transfer

Abstract

This paper presents a floor-vibration-based step-level occupant-detection approach that enables detection across different structures through model transfer. Detecting the occupants through detecting their footsteps (i.e., step-level occupant detection) is useful in various smart building applications such as senior/healthcare and energy management. Current sensing approaches (e.g., vision-based, pressure-based, radio frequency–based, and mobile-based) for step-level occupant detection are limited due to installation and maintenance requirements such as dense deployment and requiring the occupants to carry a device. To overcome these requirements, previous research used ambient structural vibration sensing for footstep modeling and step-level occupant detection together with supervised learning to train a footstep model to distinguish footsteps from nonfootsteps using a set of labeled data. However, floor-vibration-based footstep models are influenced by the structural properties, which may vary from structure to structure. Consequently, a footstep model in one structure does not accurately capture the responses in another structure, which leads to high detection errors and the costly need for acquiring labeled data in every structure. To address this challenge, the effect of the structure on the footstep-induced floor vibration responses is here characterized to develop a physics-driven model transfer approach that enables step-level occupant detection across structures. Specifically, the proposed model transfer approach projects the data into a feature space in which the structural effects are minimized. By minimizing the structure effect in this projected feature space, the footstep models mainly represent the differences in the excitation types and therefore are transferable across structures. To this end, it is analytically shown that the structural effects are correlated to the maximum-mean-discrepancy (MMD) distance between the source and target marginal data distributions. Therefore, to reduce the structural effect, the MMD between the distributions in the source and target structures is minimized. The robustness of the proposed approach was evaluated through field experiments in three types of structures. The evaluation consists of training a footstep model in a set of structures and testing it in a different structure. Across the three structures, the evaluation results show footstep detection F1 score of up to 99% for the proposed approach, corresponding to a 29-fold improvement compared to the baseline approach, which do not transfer the model.