Usman, M., Rains, J., Cui, T. J., Khan, M. Z., Kazim, J. U. R. , Imran, M. A. and Abbasi, Q. H. (2022) Intelligent wireless walls for contactless in-home monitoring. Light: Science and Applications, 11, 212. (doi: 10.1038/s41377-022-00906-5) (PMID:35798702) (PMCID:PMC9262883)
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Abstract
Human activity monitoring is an exciting research area to assist independent living among disabled and elderly population. Various techniques have been proposed to recognise human activities, such as exploiting sensors, cameras, wearables, and contactless microwave sensing. Among these, the microwave sensing has recently gained significant attention due to its merit to solve the privacy concerns of cameras and discomfort caused by wearables. However, the existing microwave sensing techniques have a basic disadvantage of requiring controlled and ideal settings for high-accuracy activity detections, which restricts its wide adoptions in non-line-of-sight (Non-LOS) environments. Here, we propose a concept of intelligent wireless walls (IWW) to ensure high-precision activity monitoring in complex environments wherein the conventional microwave sensing is invalid. The IWW is composed of a reconfigurable intelligent surface (RIS) that can perform beam steering and beamforming, and machine learning algorithms that can automatically detect the human activities with high accuracy. Two complex environments are considered: one is a corridor junction scenario with transmitter and receiver in separate corridor sections and the other is a multi-floor scenario wherein the transmitter and receiver are placed on two different floors of a building. In each of the aforementioned environments, three distinct body movements are considered namely, sitting, standing, and walking. Two subjects, one male and one female perform these activities in both environments. It is demonstrated that IWW provide a maximum detection gain of 28% in multi-floor scenario and 25% in corridor junction scenario as compared to traditional microwave sensing without RIS.
| Item Type: | Articles |
|---|---|
| Additional Information: | This work was supported in parts by Engineering and Physical Sciences Research Council (EPSRC) grants: EP/T021020/1 and EP/T021063/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Khan, Muhammad Zakir and Kazim, Mr Jalil and Imran, Professor Muhammad and Usman, Dr Muhammad and Rains, Mr James and Abbasi, Professor Qammer |
| Creator Roles: | Usman, M.Conceptualization, Methodology, Validation, Formal analysis, Software, Data curation, Writing – original draft, Writing – review and editing Rains, J.Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing – review and editing, Visualization Khan, M. Z.Methodology, Validation, Formal analysis, Software, Data curation, Writing – original draft, Visualization Kazim, J. U. R.Conceptualization, Methodology, Investigation, Software, Data curation Imran, M.Validation, Resources, Writing – review and editing, Supervision, Project administration, Funding acquisition Abbasi, Q.Conceptualization, Methodology, Validation, Resources, Writing – review and editing, Supervision, Project administration, Funding acquisition |
| Authors: | Usman, M., Rains, J., Cui, T. J., Khan, M. Z., Kazim, J. U. R., Imran, M. A., and Abbasi, Q. H. |
| College/School: | College of Science and Engineering College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity |
| Journal Name: | Light: Science and Applications |
| Publisher: | Nature Research |
| ISSN: | 2047-7538 |
| ISSN (Online): | 2047-7538 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Light: Science and Applications 11:212 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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