Characterizing the variability of footstep-induced structural vibrations for open-world person identification

Person identification is important in providing personalized services in smart buildings. Many existing studies focus on closed-world person identification, which only identifies a fixed group of people who have training data; however, they assume everyone has pre-collected data, which is not practical in real-world scenarios when newcomers are present. To overcome this drawback, open-world person identification recognizes both newcomers and registered people, which opens up new opportunities for smart building applications that involve newcomers, such as smart visitor management, customized retail, personalized health monitoring, and public emergency assistance. To achieve this, structural vibration sensing has various advantages when compared with the existing sensing modalities (e.g., cameras, wearables, and pressure sensors) because it only needs sparsely deployed sensors mounted on the floor, does not require people to carry devices, and is perceived as more privacy-friendly. However, one fundamental challenge in analyzing footstep-induced structural vibration data is its high variability due to the structural heterogeneity and the footstep variations. Therefore, it is difficult to distinguish different people given this high variability within each person, and it is more challenging to recognize a new person as that data is unobserved before. In this paper, we characterize the variability in footstep-induced structural vibration to develop an open-world person identification framework. Specifically, we address three variability challenges in developing our method. First, the high variability within each person comes from multiple sources that are entangled in the vibration signals, and thus is difficult to be decomposed and reduced. Secondly, the distribution of features extracted from the vibration signals is irregularly shaped, and therefore is difficult to model. Moreover, the identity of the next person is correlated with the previous observations, which makes the identification process more complicated. To overcome these challenges, we first characterize multiple variability sources and design a transformation function that results in signal features that are less variable within one person and more separable between different people. We then develop a modified Chinese Restaurant Process (mCRP) for nonparametric Bayesian modeling to capture the irregularly shaped feature patterns both from local and global perspectives. Finally, we design an adaptive hyperparameter α that represents the prior probability of newcomers at each observation, which keeps updating depending on the time, location, and previous predictions. We evaluate our approach through walking experiments with 20 people across 2 different structures. With only 1 pre-recorded person at each structure, our method achieves up to 92.3% average accuracy with randomly appearing newcomers.