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Title: SpaceBeat: Identity-aware Multi-person Vital Signs Monitoring Using Commodity WiFi
Vital signs monitoring has gained increasing attention due to its ability to indicate various human health and well-being conditions. The development of WiFi sensing technologies has made it possible to monitor vital signs using ubiquitous WiFi signals and devices. However, most existing approaches are dedicated to single-person scenarios. A few WiFi sensing approaches can achieve multi-person vital signs monitoring, whereas they are not identity-aware and sensitive to interferences in the environment. In this paper, we propose SpaceBeat, an identity-aware and interference-robust multi-person vital sign monitoring system using commodity WiFi. In particular, our system separates multiple people and locates each person in the spatial domain by leveraging multiple antennas. We analyze the change of signals at the location of each person to achieve identity-aware vital signs monitoring. We also design a contrastive principal component analysis-contrastive learning framework to mitigate interferences caused by other moving people. We evaluate SpaceBeat in various challenging environments, including interference scenarios, non-line-of-sight scenarios, different distances, etc. Our system achieves an average accuracy of 99.1% for breathing monitoring and 97.9% for heartbeat monitoring.  more » « less
Award ID(s):
2131143
PAR ID:
10554426
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume:
8
Issue:
3
ISSN:
2474-9567
Page Range / eLocation ID:
1 to 23
Subject(s) / Keyword(s):
WiFi sensing, Vital signs monitoring, Multi-person, Identity-aware, Interference robust, Deep contrastive learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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