A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring
This work addresses non-invasive health monitoring for individuals, but it is incremental as it builds on existing passive sensing and deep learning methods.
The paper tackles the problem of monitoring human respiration by using passive WiFi sensing and deep learning, achieving classification of breathing activity and estimation of breathing rate with a novel framework.
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.