Human respiration detection with commodity wifi devices: do user location and body orientation matter?

By Hao Wang, Daqing Zhang, Junyi Ma, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, Bing Xie. Ubiquitous Computing, 2016

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Wireless Sensing for Human Activity: A Survey (2020, methodology): “…[74]–[83] CSI-based Channel conditions/properties of wireless links Fine-grained No [7]–[9], [9]–[11], [15], [16], [21]–[27], [34]–[41], [49]–[54], [67]–[71], [84]–[91] FMCW-based Frequency shift associated with human body depth Fine-grained Yes [28], [42]–[44], [55]–[57] [29], [30], [45],…”
WiFi Sensing with Channel State Information (2019, background): “Usually a sliding window and multiplying factors are used to give different weights, e.g., Weighted Moving Average (WMA) and Exponentially Weighted Moving Average (EWMA). low-pass filters (LPF) can also remove high frequency noises assisted by signal transform methods, e.g., Fast Fourier Transform (”
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).”
A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective (2019, background): “Radio Frequency-based (RF-based) ambient sensors are working toward the measurement of multiple biomarkers such as respiration [31], [32], heartbeat [33], and motion detection [34], [35], although practical designs are still an active research area.”