Mr. Haochen Sun Profile

Haochen Sun

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Proceedings Article | 9 May 2024 Presentation + Paper

Robust person identification across various shoe types using footstep-induced structural vibrations

Yiwen Dong, Haochen Sun, Ruizhi Wang, Hae Young Noh

Proceedings Volume 12949, 129491B (2024) https://doi.org/10.1117/12.3010554

KEYWORDS: Fourier transforms, Vibration, Gait analysis, Sensors, Feature extraction, Buildings, Signal to noise ratio, Structural monitoring, Structural dynamics, Signal processing

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Person identification is important in smart buildings to enable personalized services, such as monitoring individuals’ gait health. Existing studies found that the structural vibrations induced by human footsteps provide both identity and gait health information of individuals, such as a person’s walking speed, balance, and symmetry, enabling personalized gait health monitoring in smart buildings. However, footstep-induced structural vibrations not only depend on human walking patterns but also on a person’s footwear as the footstep force transmits from the foot to the floor. This co-dependency leads to difficulty in identifying the owner of the footsteps when multiple people share the same space and each person has multiple pairs of footwear. In this study, we characterize the effect of footwear on footstep-induced structural vibrations to recognize individuals even when they wear different pairs of shoes (or barefoot). We develop a new metric named Force Transmissibility (FT) that measures the proportion of forces transmitting from the foot to the floor through the footwear. This metric unifies the effect of diverse shoe types, and we utilize this metric to enable robust person identification among various shoe types. We evaluated our approach through real-world walking experiments with eight shoe types shared by four participants. Our method achieves a 22% improvement in identifying the owner of the footsteps when compared to a baseline without footwear considerations.

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