An Unsupervised Approach to Motion Detection Using WiFi Signals

WiFi signals have been demonstrated to facilitate non-intrusive detection of a range of activities and behaviors in the physical environments they permeate. Different activities affect both phase and magnitude of channel state information (CSI) in
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iFi networks in a complex yet predictable way, and machine learning models can be trained to classify activities from such information. While constructing such WiFi-sensing systems is generally convenient and cost-effective, acquiring labeled data for a particular task can be time and labor-intensive. In this paper, we seek to remedy this issue in the context of human motion detection using deep unsupervised learning. Our proposed method uses a deep clustering model trained on appropriately-preprocessed CSI magnitude-only data to detect human motion with over 99 % accuracy in the absence of any ground labels. Removing the need for labeled samples significantly reduces the training overhead, making it a promising alternative to existing methods for motion detection.