Towards robust, unobtrusive sensing of respiration using UWB impulse Radar for the care of people living with dementia
File(s)NER21.pdf (1.19 MB)
Accepted version
Author(s)
Chen, Ziwei
Bannon, Alan
Rapeaux, Adrien
Constandinou, Timothy G
Type
Conference Paper
Abstract
The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the person's wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.5%. The target localisation results match to the radar-to-chest distances with a mean error rate of 5.8%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.
Date Issued
2021-06-02
Date Acceptance
2021-02-01
Citation
2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021, pp.866-871
ISSN
1948-3546
Publisher
IEEE
Start Page
866
End Page
871
Journal / Book Title
2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000681358200171&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
10th International IEEE-EMBS Conference on Neural Engineering (NER)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Theory & Methods
Engineering, Biomedical
Neurosciences
Computer Science
Engineering
Neurosciences & Neurology
SLEEP
Publication Status
Published
Start Date
2021-05-04
Finish Date
2021-05-06
Coverage Spatial
Virtual