WiFi-Sleep: Sleep Stage Monitoring Using Commodity Wi-Fi Devices

Sleep monitoring is essential to people's health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention...

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Bibliographic Details
Published in:IEEE internet of things journal 2021-09, Vol.8 (18), p.13900-13913
Main Authors: Yu, Bohan, Wang, Yuxiang, Niu, Kai, Zeng, Youwei, Gu, Tao, Wang, Leye, Guan, Cuntai, Zhang, Daqing
Format: Article
Language:English
Subjects:
Biomedical monitoring
Channel state information (CSI)
Commodities
Computer Science
Feature extraction
Heart rate
Monitoring
Radar equipment
Respiration
Sensors
Signal processing
Sleep
sleep monitoring
State (computer science)
Wi-Fi
Wireless access points
Wireless fidelity
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Summary:Sleep monitoring is essential to people's health and wellbeing, which can also assist in the diagnosis and treatment of sleep disorder. Compared with contact-based solutions, contactless sleep monitoring does not attach any device to the human body; hence, it has attracted increasing attention in recent years. Inspired by the recent advances in Wi-Fi-based sensing, this article proposes a low-cost and nonintrusive sleep monitoring system using commodity Wi-Fi devices, namely, WiFi-Sleep. We leverage the fine-grained channel state information from multiple antennas and propose advanced fusion and signal processing methods to extract accurate respiration and body movement information. We introduce a deep learning method combined with clinical sleep medicine prior knowledge to achieve four-stage sleep monitoring with limited data sources (i.e., only respiration and body movement information). We benchmark the performance of WiFi-Sleep with polysomnography, the gold reference standard. Results show that WiFi-Sleep achieves an accuracy of 81.8%, which is comparable to the state-of-the-art sleep stage monitoring using expensive radar devices.
ISSN:2327-4662
2372-2541
2327-4662
DOI:10.1109/JIOT.2021.3068798