PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices
By Xuyu Wang, Chao Yang, Shiwen Mao. IEEE International Conference on Distributed Computing Systems, 2017
352 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/bdfc859df1e97dba885b1e45c1625c2598a3daa7
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In Biomedical Engineering
In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “It is evident that the double parameter of the least mean square (LMS) (He et al., 2017) can extract a respiration signal with a fundamental frequency (H. Wang et al., 2016; X. Wang et al., 2017).”
- Deep Learning for Sensor-based Human Activity Recognition (2020, background): “mplitude and phase aspects [171]. In addition to coarse activities like walking and jogging, CSI can also be used to recognize small movements like lip movements [152], keystrokes [5], and heartbeats [156]. Radio-frequency identification (RFID).RFID uses electromagnetic fields to automatically ident”
- WiFi Sensing with Channel State Information (2019, background): “Device-Free Human Localization/Tracking: Position [36, 52, 69, 74, 76, 93, 109, 148], Orientation [89, 130], Motion [41, 43, 115, 130], Walking Direction [63, 115, 126, 136], Step/Gait [97, 126], Hand Drawing [84, 130, 131], Speed [137] Device-based Human Localization/Tracking [46, 87, 123, 131] Obj”
- Wireless Sensing for Human Activity: A Survey (2020, background): “PhaseBeat [53] leverages CSI phase differences between two receiving antennas on WiFi devices [133] to monitor breathing rate and heart rate in real time.”