  This paper presents an end-to-end deep learning framework using passive WiFi
sensing to classify and estimate human respiration activity. A passive radar
test-bed is used with two channels where the first channel provides the
reference WiFi signal, whereas the other channel provides a surveillance signal
that contains reflections from the human target. Adaptive filtering is
performed to make the surveillance signal source-data invariant by eliminating
the echoes of the direct transmitted signal. We propose a novel convolutional
neural network to classify the complex time series data and determine if it
corresponds to a breathing activity, followed by a random forest estimator to
determine breathing rate. We collect an extensive dataset to train the learning
models and develop reference benchmarks for the future studies in the field.
Based on the results, we conclude that deep learning techniques coupled with
passive radars offer great potential for end-to-end human activity recognition.
