  Nowadays, Coronavirus disease (COVID-19) has become a global pandemic because
of its fast spread in various countries. To build an anti-epidemic barrier,
self-isolation is required for people who have been to any at-risk places or
have been in close contact with infected people. However, existing camera or
wearable device-based monitoring systems may present privacy leakage risks or
cause user inconvenience in some cases. In this paper, we propose a Wi-Fi-based
device-free self-quarantine monitoring system. Specifically, we exploit channel
state information (CSI) derived from Wi-Fi signals as human activity features.
We collect CSI data in a simulated self-quarantine scenario and present
BranchyGhostNet, a lightweight convolution neural network (CNN) with an early
exit prediction branch, for the efficient joint task of room occupancy
detection (ROD) and human activity recognition (HAR). The early exiting branch
is used for ROD, and the final one is used for HAR. Our experimental results
indicate that the proposed model can achieve an average accuracy of 98.19% for
classifying five different human activities. They also confirm that after
leveraging the early exit prediction mechanism, the inference latency for ROD
can be significantly reduced by 54.04% when compared with the final exiting
branch while guaranteeing the accuracy of ROD.

Computer Vision and Pattern Recognition;Machine Learning