CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments
IEEE Sensors Journal
; 23(2):969-976, 2023.
Article
in English
| Scopus | ID: covidwho-2244030
ABSTRACT
The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.
Channel state information; Cost benefit analysis; Cost estimating; Internet of things; Job analysis; Location; Long short-term memory; Viruses; Wireless sensor networks; Activity recognition; Channel-state information; Embeddings; Features extraction; Joint activity; Localisation; Location awareness; Neural embedding; Siamese network; Task analysis; COVID-19; channel state information (CSI); localization; neural embeddings
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Sensors Journal
Year:
2023
Document Type:
Article
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