A Domain Adaptation Framework for Human Activity Monitoring Using Passive Wi-Fi Sensing

Due to the omnipresence of radio frequency signals, the Channel State Information (CSI) can offer an alternate source to image, video, and other high-dimensional streams in a great many Internet-of-Things (IoT) applications. As a result, an ever increasing number of researchers are advocating for the use of passive CSI data for ranging, tracking, perception and automation across many domains such as robotics, healthcare, and surveillance. Specifically, in indoor applications where movements cause classifiable effects on the CSI, this resource from the existing infrastructure can be leveraged in order to provide a high-dimensional signal source for activity recognition. However, the task remains a challenge on two accounts. On the one hand, the radio frequency channel is highly susceptible to environment changes and artifacts. On the other hand, there is a lack of robust models that allow for practical deployment. In this work, we focus on tackling these issues for practical IoT applications through a layered architecture proposal. In the first layer, the task of inference for activity recognition is performed by a robust cross-modal deep learning network. In the next layer, a domain adaptation framework is proposed in order to achieve high deployment-time performance in target indoor environments. Our experiments verify that through few-shot training, using the proposed framework the activity classification performance increases substantially in practical settings over the state-of-the-art approaches.