Footstep-Induced Floor Vibration Dataset: Reusability and Transferability Analysis

Abstract

Footstep-induced floor vibration sensing has been used in many smart home applications, such as elderly/patient monitoring. These systems often leverage data-driven models to infer human information. Therefore, characterizing datasets is crucial for the generalization of this new modality. This dataset contains 144-minute floor vibration signals from two pedestrians in eight environments. We analyze the reusability of this dataset in three different research areas, including vibration-based information inference, knowledge transferring, and multimodal learning. We further characterize the dataset transferability on the occupant identification task, to provide quantitative insights for the transfer learning problems in the real-world floor vibration sensing applications. The characterization is conducted with three metrics, including distribution distance, information dependency, and influencing factor bias. Analysis results depict that the dataset covers different levels of transferability caused by multiple influencing factors. As a result, there are multiple future directions in which the dataset can be reused.