Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach

Rehman, M., Shah, R. A., Ali, N. A. A., Khan, M. B., Shah, S. A. , Alomainy, A., Hayajneh, M., Yang, X., Imran, M. A. and Abbasi, Q. H. (2023) Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach. Sensors, 23(3), 1251. (doi: 10.3390/s23031251)

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.

• Item Type: Articles
• Additional Information: Zayed Health Center at UAE University supports this work in parts under Fund code G00003476, EPSRC grant numbers EP/T021063/1 and EP/T021020/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Abbasi, Professor Qammer and Shah, Mr Syed

Creator Roles

Imran, M.: Project administration
Abbasi, Q.: Writing – review and editing, Funding acquisition

• Authors: Rehman, M., Shah, R. A., Ali, N. A. A., Khan, M. B., Shah, S. A., Alomainy, A., Hayajneh, M., Yang, X., Imran, M. A., and Abbasi, Q. H.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; 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: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 21 January 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Sensors 23(3): 1251
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307829		Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community Healthcare	Jonathan Cooper	Engineering and Physical Sciences Research Council (EPSRC)	EP/T021020/1	ENG - Biomedical Engineering