Stranger Detection and Occupant Identification Using Structural Vibrations

Abstract

Human-induced structural vibration provides valuable information about humans who interact with the structure, including people’s identity, characteristics, activities and health status. Among them, person identification is crucial because it is the premise for any personalized services. Our prior work using footstep-induced structural vibration has been promising in identifying a fixed group of people, but it is restricted to registered occupants, leading to errors whenever a stranger appears. Therefore, we introduce a stranger detection and occupant identification system using footstep-induced structural vibrations. There are two main challenges in developing this system: 1) the strangers’ walking patterns are unknown before being observed, and 2) the probability of the presence of a stranger varies at different times and locations. To overcome the first challenge, we model the occupants’ and strangers’ footstep-induced floor vibration data as a mixture of Gaussian distributions with varying number of components, representing the changing number of registered people. To address the second challenge, we reformulated the prior estimation in the mixture model by introducing an interpretable parameter that represents the expected probability of observing a stranger. Through an experiment conducted on a wood-framed structural platform, our method achieves an average accuracy of 89.2% in person identification among 10 people.