A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring

TL;DR

This paper introduces a deep learning framework utilizing passive WiFi sensing with a novel CNN and random forest to classify and estimate human respiration, demonstrating promising potential for non-invasive activity monitoring.

Contribution

It presents an end-to-end deep learning approach with a new CNN architecture and extensive dataset for passive WiFi-based respiration monitoring, establishing reference benchmarks.

Findings

Deep learning effectively classifies respiration activity

The framework accurately estimates breathing rate

Passive WiFi sensing shows potential for non-invasive monitoring

Abstract

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.