We present the ReVibe dataset, a floor vibration dataset induced by 11 people, each walk on 2 different floor types: 1) wooden floor, and 2) concrete floor. Aligning the data from the same person across various locations is important to enable ubiquitous human activity recognition (HAR) and personalized health monitoring. However, it is challenging to align floor vibrations from the same person across two locations, mainly due to the difference in structural properties and footsteps over these locations. First, the structural properties at different locations vary, resulting in distinct patterns in footstep-induced vibration signals for each location (see Figure 1 of Instruction.pdf). It is difficult to match the same person across dissimilar signal patterns. Secondly, the same person's footstep forces and the number of recorded footsteps also vary across locations. Specifically, the same person's footsteps' characteristics vary due to psychological and physical variations when walking around in the building. To this end, the ReVibe dataset aims to enable vibration data alignment across different structures. Please cite this dataset as: Yiwen Dong, and Hae Young Noh. ReVibe: A Footstep-Induced Floor Vibration Dataset from 11 People Walking on 2 Different Structures - Wooden and Concrete Floor. Zenodo. 2024. https://zenodo.org/uploads/14575371 The publication associated with the dataset is: Yiwen Dong, Jiacheng Zhu, and Hae Young Noh. 2022. Re-vibe: vibration-based indoor person re-identification through cross-structure optimal transport. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '22). Association for Computing Machinery, New York, NY, USA, 348–352. https://doi.org/10.1145/3563357.3566134