SCSV2: physics-informed self-configuration sensing through vision and vibration context modeling

Structural vibration sensing has been explored to acquire indoor human information. This non-intrusive sensing modality enables various smart building applications such as long-term in-home elderly monitoring, ubiquitous gait analysis, etc. However, for applications that utilize multiple sensors to collaboratively infer this information (e.g., localization, activities of daily living recognition), the system configuration requires the location of the anchor sensor, which are usually acquired manually. This labor-intensive manual system configuration limited the scalability of the system. In this paper, we propose SCSV2, a self-configuration scheme to compute these vibration sensor locations utilizing shared context information acquired from complementary sensing modalities - vibration sensor itself and co-located cameras. SCSV2 combines 1) the physics models of wave propagation together with structural element effects and 2) the data-driven model from the multimodal data to infer the vibration sensor's location. We conducted real-world experiments to verify our proposed method and achieved an up to 7cm anchor sensor localization accuracy.