Intelligent reflecting surface-based non-LOS human activity recognition for next-generation 6G-enabled healthcare system

Saeed, U., Shah, S. A. , Khan, M. Z., Alotaibi, A. A., Ramzan, N. and Abbasi, Q. H. (2022) Intelligent reflecting surface-based non-LOS human activity recognition for next-generation 6G-enabled healthcare system. Sensors, 22(19), 7175. (doi: 10.3390/s22197175) (PMID:36236272) (PMCID:PMC9572609)

Abstract

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored.

• Item Type: Articles
• Additional Information: Funding: This work is supported in part by the Coventry University Internal Funding Scheme. The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (IF_2020_NBU_229).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Khan, Muhammad Zakir and Abbasi, Professor Qammer and Shah, Mr Syed
• Authors: Saeed, U., Shah, S. A., Khan, M. Z., Alotaibi, A. A., Ramzan, N., and Abbasi, Q. H.
• 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: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 21 September 2022
• Copyright Holders: Copyright © 2022 by the authors
• First Published: First published Sensors 22(19): 7175
• Publisher Policy: Reproduced under a Creative Commons License