BReML: A Breathing Rate Estimator Using Wi-Fi Channel State Information and Machine Learning

Breathing rate is one vital sign that might help identifying pathological conditions by its monitoring. This paper presents a novel breathing rate estimator that combines conventional Channel State Information (CSI) approaches with machine learning classifiers to provide a breathing rate estimation...

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Bibliographic Details
Main Authors: Armenta-Garcia, Albany, Gonzalez-Navarro, Felix F., Caro-Gutierrez, Jesus, Flores-Rios, Brenda L., Ibarra-Esquer, Jorge E.
Format: Conference Proceeding
Language:English
Subjects:
Breathing Rate Estimator
Channel State Information
Estimation
Feature extraction
Machine learning
Measurement
Signal processing
Signal processing algorithms
Support vector machines
Wi-Fi
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Summary:Breathing rate is one vital sign that might help identifying pathological conditions by its monitoring. This paper presents a novel breathing rate estimator that combines conventional Channel State Information (CSI) approaches with machine learning classifiers to provide a breathing rate estimation in a controlled environment. Results show that by extracting time and frequency domain features of CSI amplitude, as well as using a first estimation obtained with Fast Fourier Transform as a feature for feeding machine learning classifiers, the breathing rate can be accurately estimated.
ISSN:2332-5712
DOI:10.1109/ENC53357.2021.9534797