Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals

By Xuefeng Liu, Jiannong Cao, Shaojie Tang, Jiaqi Wen. IEEE Real-Time Systems Symposium, 2014

317 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4dea8b07b2bd8a22f61b4857fbb87c202bb907e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Gang Zhou
2. Yongsen Ma
3. Shuangquan Wang
4. Daqing Zhang
5. Tao Gu
6. Xue Liu
7. Yunhao Liu
8. Shengjie Li

In Electrical and Electronic Engineering

1. Gang Zhou
2. Zheng Yang
3. Chenshu Wu
4. Yunhao Liu
5. Yongsen Ma
6. Shuangquan Wang
7. Zimu Zhou
8. Jiannong Cao

Citations in the corpus

• RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, background): “Index Terms—Fall detection, activity recognition, channel state information (CSI), WiFi, phase difference Ç”
• Human respiration detection with commodity wifi devices: do user location and body orientation matter? (2016, background): “WiSleep [22] was the first work to detect human respiration rate for sleep monitoring based on CSI in commodity WiFi devices.”
• Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, background): “Furthermore, we envision the impacts of chest motion on the phase information of CSI as a potential proxy for more complicated cases where multiple persons could hold nearly identical breathing rates.”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, background): “Besides, Wi-Sleep [40] deploys three pairs of transmitter and receiver to collaboratively monitor the respiration of a person with three diferent sleeping positions, respectively.”
• WiFi Sensing with Channel State Information (2019, methodology): “Wavelet Filter is similar to LPFs; the major difference is that it uses Discrete Wavelet Transform (DWT) instead of FFT. Details of signal transform methods and frequency-domain filters are introduced in Sections 3.2 and 3.3.”
• Rapid (2017, background): “2 RELATED WORK Wireless Sensing: Recent years have witnessed the emergence and development of WiFi-based sensing, which brings various applications such as indoor localization [29], activity and gesture recognition [12, 21, 27] and other types of applications such as sleep monitoring [13] and LoS id”