Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals

A method of operating a classification engine, comprising:

• during a meta-learning phase, training an embedding network and a classifier jointly;
• during an optimization phase, training the classifier independent from the embedding network via a training dataset;
• and
• during an operational phase:
• via the embedding network, mapping an input tensor to a latent vector, the input tensor corresponding to an input dataset;
• and
• via the classifier, identifying a classification corresponding to the input tensor by mapping the latent vector to a label.

A method of sensing an environment, comprising:

• transmitting a wireless signal through the environment;
• receiving, via a plurality of wireless receivers, the wireless signal at the distinct location within the environment via at least one respective antenna, each of the wireless receivers positioned at a distinct location within the environment, each of the plurality of wireless receivers being located closest to a respective subject of a plurality of subjects;
• generating, via the plurality of wireless receivers, a channel state information (CSI) packet indicating a state of a wireless communications channel associated with the wireless signal;
• processing the CSI packets from the plurality of wireless receivers to generate a CSI dataset as a function of the CSI packets;
• identifying a target subject of the plurality of subjects based on the CSI dataset via a subject machine learning (ML) model trained on a first training dataset;
• and
• identifying an activity exhibited by the target subject based on the CSI dataset via an activity ML model trained on a second training dataset.