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. 2018 Dec 15;18(12):4443.
doi: 10.3390/s18124443.

Posture-Specific Breathing Detection

Hualin Guan  1 , Xiaodong Yang  2 , Wanrong Sun  3 , Aifeng Ren  4 , Dou Fan  5 , Nan Zhao  6 , Lei Guan  7 , Daniyal Haider  8 , Qammer H Abbasi  9
Affiliations

Posture-Specific Breathing Detection

Hualin Guan et al. Sensors (Basel). .

Abstract

Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations. In this research, we propose a non-invasive breathing activity detection method based on C-band sensing. Traditional non-invasive detection methods require special hardware facilities that cannot be used in ordinary environments. Based on this, a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system based on 802.11n protocol is proposed in this paper. Our system improves the traditional data processing method and has stronger robustness and lower bit relative error. The system detects the respiratory activity of different body postures, captures and analyses the information, and determines the influence of different body postures on human respiratory activity.

Keywords: body postures; non-invasive; respiratory activity detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The logic flow of our system.
Figure 2
Figure 2
C-band sensing-based human respiratory activity information detection model.
Figure 3
Figure 3
(a) The CFR (channel frequency response) amplitude from antenna 1. (b) The CFR phase from antenna 1. The CFR amplitude from (c) antenna 2 and (d) antenna 3.
Figure 4
Figure 4
Standard deviation of the subcarriers of the three antennas.
Figure 5
Figure 5
The respiratory signal measured by the respiratory sensor when laying down: (a) the time domain signal and (b) the frequency domain signal after Fourier transform.
Figure 6
Figure 6
The data of the 16th subcarrier of the third antenna for a person laying down. (a) Time domain signal of the subcarrier. (b) Fourier transform of subcarrier. (c) Time domain signal after subcarrier wavelet reconstruction. (d) Fourier transform of wavelet reconstruction signal.
Figure 7
Figure 7
Human respiratory signal after threshold denoising. (a) Time domain signal and (b) Fourier transform after soft threshold denoising. (c) Time domain signal and (d) Fourier transform after hard threshold denoising.
Figure 8
Figure 8
Respiratory signal relative error after data processing while lying down. (a) Average error estimate and (b) CDF (cumulative distribution function) of the estimated error after soft threshold denoising. (c) Average error estimate and (d) CDF of the estimated error after hard threshold denoising.
Figure 9
Figure 9
Respiratory signal relative error after data processing for the sitting posture. (a) Average error estimate and (b) CDF of the estimated error after soft threshold denoising. (c) Average error estimate and (d) CDF of the estimated error after hard threshold denoising.
Figure 10
Figure 10
Respiratory signal relative error after data processing for the standing position. (a) Average error estimate and (b) CDF of the estimated error after soft threshold denoising. (c) Average error estimate and (d) CDF of the estimated error after hard threshold denoising.
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