Indoor Human Information Acquisition from Physical Vibrations
- 14:00 17th September 2019 ( Michaelmas Term 2019 )Lecture Theatre B, Wolfson Building
The number of everyday smart devices (e.g., Samsung SmartThings, Nest, Notion) is projected to grow to the billions in the coming decade. The Cyber-Physical Systems or Internet of Things systems that consist of these devices are used to obtain human information for various smart building applications. From the system perspective, my research focuses on non-intrusive indoor human information acquisition through ambient structural vibration, which is referred to as ’structures as sensors’. People’s interaction with structures in the ambient environment (e.g., floor, table, door) induces those structures to vibrate. By capturing and analyzing the vibration response of structures, we can indirectly infer information about the people and their actions that cause it. However, due to the complexity of the physical world, sensing data distributions can change significantly under different sensing conditions. Therefore, from the data perspective, accurate information learning through a pure data-driven approach requires a large amount of labeled data, which is costly and difficult to obtain in real-world applications. My research addresses these challenges by combining physical and data-driven knowledge and iteratively expanding the labeled dataset. With insights into the relationship between changes of sensing data distributions and measurable physical attributes, the iterative algorithm guides the expansion order by measured physical attributes to ensure a high learning accuracy in each iteration.