UWB and WiFi Systems as Passive Opportunistic Activity Sensing Radars

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Human Activity Recognition (HAR) is becoming increasingly important in smart homes and healthcare applications such as assisted-living and remote health monitoring. In this paper, we use Ultra-Wideband (UWB) and commodity WiFi systems for the passive sensing of human activities. These systems are based on a receiver-only radar network that detects reflections of ambient Radio-Frequency (RF) signals from humans in the form of Channel Impulse Response (CIR) and Channel State Information (CSI). An experiment was performed whereby the transmitter and receiver were separated by a fixed distance in a Line-of-Sight (LoS) setting. Five activities were performed in between them, namely, sitting, standing, lying down, standing from the floor and walking. We use the high-resolution CIRs provided by the UWB modules as features in machine and deep learning algorithms for classifying the activities. Experimental results show that a classification performance with an F1-score as high as 95.53% is achieved using processed UWB CIR data as features. Furthermore, we analysed the classification performance in the same physical layout using CSI data extracted from a dedicated WiFi Network Interface Card (NIC). In this case, maximum F1-scores of 92.24% and 80.89% are obtained when amplitude CSI data and spectrograms are used as features, respectively.
Original languageEnglish
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationProceedings of a meeting held 10-14 May 2021, Atlanta, GA, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-7281-7609-3
ISBN (Print)978-1-7281-7610-9
Publication statusE-pub ahead of print - 18 Jun 2021
Event2021 IEEE Radar Conference: RadarConf '21 - virtual format
Duration: 10 May 202114 May 2021

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2021 IEEE Radar Conference
Internet address

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS This work was funded under the OPERA Project, the UK Engineering and Physical Sciences Research Council (EP-SRC), Grant EP/R018677/1.

Publisher Copyright:
© 2021 IEEE.


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