Enhancing System Performance through Objective Feature Scoring of Multiple Persons’ Breathing Using Non-Contact RF Approach

Author:

Rehman Mubashir12,Shah Raza Ali1,Ali Najah Abed Abu3,Khan Muhammad Bilal23,Shah Syed Aziz4ORCID,Alomainy Akram5,Hayajneh Mohammad3ORCID,Yang Xiaodong6,Imran Muhammad Ali7ORCID,Abbasi Qammer H.7ORCID

Affiliation:

1. Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan

2. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan

3. College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates

4. Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK

5. School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

6. School of Electronic Engineering, Xidian University, Xi’an 710071, China

7. School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Abstract

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Funder

Zayed Health Center at UAE University

EPSRC

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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1. Video-based deception detection using wrapper-based feature selection;2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA);2024-06-14

2. Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal;Sensors;2024-03-26

3. Contactless Breathing Waveform Detection Through RF Sensing: Radar vs. Wi-Fi Techniques;2023 IEEE Tenth International Conference on Communications and Networking (ComNet);2023-11-01

4. Advanced Sensing Techniques for Intelligent Human Activity Recognition Using Machine Learning;Electronics;2023-09-22

5. Acute Inhalation Injury Signatures in Breathing Rate Abnormalities in Domestic Environment using RF Sensing;2023 International Wireless Communications and Mobile Computing (IWCMC);2023-06-19

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