We present ShoeVibe, a dataset of footstep-induced floor vibrations from 9 people, each walks with 8 different types of shoes including barefoot. Floor vibrations induced by human footsteps contain rich information, such as a person’s identity and gait, characterized by walking speed, balance, symmetry, and so on, enabling personalized health monitoring in smart buildings. However, footstep-induced structural vibrations not only depend on human walking patterns but also on a person’s shoes 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. To address this challenge, the ShoeVibe dataset aims to study the effect of shoes on floor vibrations induced by human walking. Please cite this dataset as: Yiwen Dong, Haochen Sun, Ruizhi Wang, and Hae Young Noh. ShoeVibe: A Human-Induced Floor Vibration Dataset with 8 Different Shoe Types. Zenodo. 2024. https://zenodo.org/records/14575148 The associated paper published using this dataset is: Yiwen Dong, Haochen Sun, Ruizhi Wang, and Hae Young Noh "Robust person identification across various shoe types using footstep-induced structural vibrations", Proc. SPIE 12949, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024, 129491B (9 May 2024); https://doi.org/10.1117/12.3010554