Ashleibta, A. M., Abbasi, Q. H. , Shah, S. A. , Khalid, A., AbuAli, N. A. and Imran, M. A. (2021) Non-invasive RF sensing for detecting breathing abnormalities using software defined radios. IEEE Sensors Journal, 21(4), pp. 5111-5118. (doi: 10.1109/JSEN.2020.3035960)
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Abstract
The non-contact continuous monitoring of biomarkers comprising breathing detection and heart rate are essential vital signs to evaluate the general physical health of a patient. As compared to existing methods that need dedicated equipment (such as wearable sensors), the radio frequency (RF) signals can be synthesised to continuously monitor breathing rate in a contact-less setting. In this paper, we proposed the contact less breathing rate detection using universal software radio peripheral (USRP) platform without any wearable sensor. Our system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. We presented a comparison of our breathing rate detection with wearable sensor (ground truth) results for single human subject. In this paper, we used wireless data to train, validate and test different machine learning (ML) algorithms to classify USRP data into normal, shallow and elevated breathing depending on the breathing rate. Although different ML models were developed using the K-Nearest Neighbor (KNN), Discriminant Analysis (DA), Naive Bayes (NB) and Decision Tree (DT) algorithms, however results showed KNN based model provided the highest accuracy for our data ( 91%) each time the trial was made. DT (17.131%), DA (59.72%) and NB (48.99%). Results presented in this paper showed that USRP based breathing rate is comparable to the wearable sensor demonstrating the potential application of our method to accurately monitor breathing rate of patients in primary or acute setting.
Item Type: | Articles |
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Additional Information: | Aboajeila Milad studentship is funded by Libyan Govern- ment. This work is supported in parts by Zayed Health Center at UAE University under Fund code G00003476, EPSRC EP/T021020/1 and EP/T021063/1. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Abbasi, Professor Qammer and Imran, Professor Muhammad and Shah, Mr Syed and Ashleibta, Aboajeila Milad Abdulhadi |
Authors: | Ashleibta, A. M., Abbasi, Q. H., Shah, S. A., Khalid, A., AbuAli, N. A., and Imran, M. A. |
College/School: | College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
Journal Name: | IEEE Sensors Journal |
Publisher: | IEEE |
ISSN: | 1530-437X |
ISSN (Online): | 1558-1748 |
Published Online: | 04 November 2020 |
Copyright Holders: | Copyright © 2020 IEEE |
First Published: | First published in IEEE Sensors Journal 21(4): 5111-5118 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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