The number of everyday smart devices is projected to grow to billions in the coming decade, which enables various smart building applications. These applications, especially in-home long-term occupant monitoring, rely on emerging device-free human sensing techniques. From the system perspective, we introduce an alternative non-intrusive sensing modality through ambient structural vibration to indirectly infer fine-grained occupant information. However, due to the complexity of the physical world, sensing data distributions face severe domain variances. Therefore, from the data perspective, accurate information learning through pure data-driven approaches requires a large amount of labeled data, which is costly and difficult to obtain in practice. We address these challenges by combining physical and data-driven knowledge in learning.
Speaker: Shijia Pan, UC Merced
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