# A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring

arXiv: https://arxiv.org/abs/1704.05708 · HTML: https://mlsanity.com/paper/1704.05708/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot](https://mlsanity.com/paper/2405.01882/) (2024)
- [WiSleep: Inferring Sleep Duration at Scale Using Passive WiFi Sensing](https://mlsanity.com/paper/2102.03690/) (2021)
- [AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification](https://mlsanity.com/paper/2402.17808/) (2024)

## Abstract (arXiv)

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.