Deep-learning system detects human presence by harvesting RF signals
Feb 2020, phys.org
The presence of humans in a room or in other indoor environments can alter the propagation of RF signals in several ways. By pre-processing RF channel measurements, the researchers were able to create 'images' summarizing the signals, which could in turn be analyzed to detect the presence of humans in a given environment.
They then trained a CNN on a large amount of data containing both magnitude and phase information, two key properties of RF signals. Over time, the deep learning algorithm learned to distinguish when an environment is populated by humans and when it is free from them by analyzing what is known as channel state information (CSI).
"Exploiting the ubiquity of ambient RF signals such as WiFi, Bluetooth or cellular signals for situational awareness information provides added value to existing RF infrastructure," Chen said. "Occupancy detection, for example, is an application where RF sensing can be a low-cost and infrastructure-free alternative or complement to existing approaches."
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