Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System
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 a...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7175 |
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| author | Umer Saeed Syed Aziz Shah Muhammad Zakir Khan Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Qammer H. Abbasi |
| author_facet | Umer Saeed Syed Aziz Shah Muhammad Zakir Khan Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Qammer H. Abbasi |
| author_sort | Umer Saeed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:12:25Z |
| format | Article |
| id | doaj.art-c6c4e91fda9b4ee09f6e02a11335d8b9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:12:25Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c6c4e91fda9b4ee09f6e02a11335d8b92023-11-23T21:44:39ZengMDPI AGSensors1424-82202022-09-012219717510.3390/s22197175Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare SystemUmer Saeed0Syed Aziz Shah1Muhammad Zakir Khan2Abdullah Alhumaidi Alotaibi3Turke Althobaiti4Naeem Ramzan5Qammer H. Abbasi6Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UKResearch Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKDepartment of Science and Technology, College of Ranyah, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaFaculty of Science, Northern Border University, Arar 91431, Saudi ArabiaSchool of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UKJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKHuman 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.https://www.mdpi.com/1424-8220/22/19/71756Gnext-generation healthcareintelligent reflecting surfacesoftware-defined radioRF sensingmachine learning |
| spellingShingle | Umer Saeed Syed Aziz Shah Muhammad Zakir Khan Abdullah Alhumaidi Alotaibi Turke Althobaiti Naeem Ramzan Qammer H. Abbasi Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System Sensors 6G next-generation healthcare intelligent reflecting surface software-defined radio RF sensing machine learning |
| title | Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System |
| title_full | Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System |
| title_fullStr | Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System |
| title_full_unstemmed | Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System |
| title_short | Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System |
| title_sort | intelligent reflecting surface based non los human activity recognition for next generation 6g enabled healthcare system |
| topic | 6G next-generation healthcare intelligent reflecting surface software-defined radio RF sensing machine learning |
| url | https://www.mdpi.com/1424-8220/22/19/7175 |
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