# Respiration Symptoms Monitoring in Body Area Networks
## 3 S-Band Sensing and Breath Detection  
The wireless channel information characterizes the wireless channel properties of a communication link. The information essentially presents the RF signal propagation from transmitter to receiver and describes the combined effect of multipath propagation such as reflection, fading, and scattering [15]. The signal received can be expressed as:  
\[\mathbf{H}(i)=\Bigr{|}\Bigr{|}\mathbf{H}(i)\Bigr{|}\Bigr{|}e^{(j\angle\mathbf{ H}(i))}. \tag{1}\]  
Here \(\mathbf{H}(i)\) is the wireless channel information, the terms \(||\mathbf{H}(i)||\) and \(\angle\mathbf{H}(i)\) denote the amplitude and phase information for \(i\)th frequency, respectively. It should be noted that the card reveals group of 30 frequencies known as one WCI packet and is intrinsic feature of the device that sweeps over multiple channel frequencies.  
The experimental design was implemented in a meeting room at Xidian University, as shown in Figure 1. An antenna which is in the range of S-Band (2 GHz to 4 GHz) was deployed as the transmitter and a 2.4 GHz omni-directional antenna working as a receiver. The wireless channel information extraction tool introduced by [16] was installed. The host computer receive 10 WCI packets per second [17]. From each WCI packet received using the specific NIC, a 30 \(x\) 1 matrix was extracted known as channel frequency response (CFR) and can be written as:  
\[CFR_{matrix}=\Bigl{[}h^{1},h^{2},h^{3},\ldots,h^{n}\Bigr{]}. \tag{2}\]Here \(h^{i}\) is a complex number and describes channel frequency response of the \(i\)th frequency and \(n=30\) is the total number of frequencies. In order to examine the time history of a total number of WCI packets received over a period of time, the \(CFR_{matrix}\) is put together recorded at various time intervals and forms a WCI time history stream as:  
\[CFR_{time\_history}=[CFR_{matrix}{}^{1},CFR_{matrix}{}^{2},CFR_{matrix}{}^{3}, \ldots CFR_{matrix}{}^{k}]. \tag{3}\]  
Here \(CFR_{time\_history}\) is a 30-\(x\)-1 matrix, where \(k\) represents the total number of WCI packets received within a particular time window.  
Figure 2 indicates the variances of raw WCI due to the subject's chest movement, which will be used to extract the breathing pattern in the subsequent section. In order to extract the breathing pattern, we select the frequency # 13 as in [15]. We have examined all the 30 frequencies for detecting breathing pattern and compared the results with digital respiratory sensor. The main advantage of using S-Band sensing which exploit the NIC is that one or multiple frequencies out of 30 can be used for desired purpose. We tested the proposed framework in 8 various places that had different geometry and structure. In 7 cases, frequency # 13 provided the best results when observed through the digital respiratory sensor. Only at one place, the frequency # 12 provided results closely matched to the digital respiratory sensor. The primary reason that frequency # 13 provides best results because it is available to all wireless local area network systems but rarely used making it less prone to interference.  
Figure 1: Experimental setting for respiration monitoring.