  This paper presents a method that estimates the respiratory rate based on the
frame capturing of wireless local area networks. The method uses beamforming
feedback matrices (BFMs) contained in the captured frames, which is a rotation
matrix of channel state information (CSI). BFMs are transmitted unencrypted and
easily obtained using frame capturing, requiring no specific firmware or WiFi
chipsets, unlike the methods that use CSI. Such properties of BFMs allow us to
apply frame capturing to various sensing tasks, e.g., vital sensing. In the
proposed method, principal component analysis is applied to BFMs to isolate the
effect of the chest movement of the subject, and then, discrete Fourier
transform is performed to extract respiratory rates in a frequency domain.
Experimental evaluation results confirm that the frame-capture-based
respiratory rate estimation can achieve estimation error lower than 3.2
breaths/minute.
