CSI-based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments
IEEE Sensors Journal
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-2018957
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 non-wearable/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 (1D-CNN) 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 HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE
Activity recognition; Costs; COVID-19; CSI; Feature extraction; Internet of things; Localization; Location awareness; Neural embeddings; Siamese Network; Task analysis; Training; Wireless sensor networks; Channel state information; Cost benefit analysis; Cost estimating; Job analysis; Location; Long short-term memory; Viruses; Channel-state information; Embeddings; Features extraction; Joint activity; Localisation; Neural embedding
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Sensors Journal
Year:
2022
Document Type:
Article
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