# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 3 Design Pipeline
### Data Collection and Labeling  
#### 3.1.1 Data Collection and Feature Selection  
In this work, we use a Laerdal SimBaby programmable infant mannequin. The mannequin was programmed to breathe at a rate of 31 breaths per minute for variable time intervals, then to stop breathing for 30 s, 45 s, and 60 s, alternating between these states for a period of one hour. An RFID investigator (Impii Speedway R420) was used to poll the Bellypatch wearable RFID tag and the antenna with a 900 MHz band RFID signal coming from the SimBaby. The RFID interrogator was also used to measure properties of the backscattered signal reflected from the RFID tag. The interrogator was positioned 1 foot from the mannequin, oriented above, astride, and at the feet. Interrogations were performed with a frequency of 90 Hz. RFID properties considered for model features include the Received Signal Strength Indicator (RSSI), interrogation frequency, and timestamp. Each of these properties is affected in-band by the frequency of the original signal emitted by the interrogator. Under United States Federal Communications Commission  
Figure 2: Design pipeline for respiratory classification using deep learning. (FCC) regulations, RFID interrogations must iterate (or channel hop) over 50 frequency channels in the 900 MHz band. In addition to perturbing the raw measurement observations at the interrogator, channel hopping poses challenges in computing higher order features from changes in the observed phase, because these features depend on observing changes in successive values of the phase under the assumption that they were observed from the same interrogation frequency. As a result, the observed Doppler shift is used to identify fine movements of the RFID tag, either in space or because of a strain force applied to the surrounding knit antenna. The received signal strength from an interrogation is influenced by several factors as defined by the Radar Cross Section (RCS) formula in Equation (1) [30]. Specifically, the RCS relates changes in received signal power (\(P_{Rx}\)) to the interrogation power (\(P_{Tx}\)), the reader and tag gains (\(G_{reader}\) and \(G_{tag}\), respectively), the return loss (R), and the interrogation wavelength \(\lambda\)[19]. \[P_{Rx}=P_{Tx}\cdot G_{reader}^{2}\cdot G_{tag}^{2}\cdot R\cdot\left(\frac{ \lambda}{4}\cdot\pi\cdot r\right)^{4} \tag{1}\]  
Some of these terms can be controlled by the interrogator configuration: for example, the interrogation transmitter power and antenna reader gain at the interrogator; however, the interrogation frequency changes due to channel hopping, and the receiving antenna gain (\(G_{tag}\)) changes as the wearer stretches the antenna or moves about in space. Thus, the observed RSSI alone confounds several artifacts about the state of the transmission along with the state of the wearer. As a result, a higher order feature \(\zeta\) is computed from the RSSI measure by accounting for the interrogation frequency. We manipulate the RCS equation to arrange those terms related to wearer state on one side and set them as equal to those terms related to the interrogator configuration, as shown in Equation (2). Thus, we observe that the changes in the gain of the tag \(G_{tag}\) (resulting from movement or a strain force on the antenna), the distance r between the interrogator and the tag (resulting from movement), and return loss \(R\) (resulting from movement, strain force, fading, or multipath interference) are proportional to the interrogation wavelength lambda and observed \(RSSI\) measure \(P_{Rx}\), along with the interrogation power \(P_{Tx}\) and the reader gain \(G_{reader}\), which are held constant at the interrogator and interrogating antenna [5].