# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 1 Introduction  
In recent times, with the growth in the aging population, people's desire to improve their health is increasing. Consequently, many researchers [1, 2, 3] have been conducted to identify methods to periodically monitor an individual's health in daily life and measure vitals to prevent disease. Because respiratory dysfunction can be attributed to cardiovascular, neurological, and psychiatric problems, it is necessary to monitor the vital respiratory signals to identify acute and chronic diseases [4]. In addition, respiratory signals are valuable clinical signs that are included in the clinical risk score pertaining to the National Early Warning Score. Conventional respiration monitoring methods use a contact sensor. The respiratory signal is extracted from the pressure signal obtained through a wearable belt [1] worn on the body or a single pressure sensor [2] attached to the body. Alternatively, a fiber optic sensor [3] can be attached to the body to measure respiration through the optical interference caused by chest movement. However, in the sensor contact method, which is commonly used for measuring respiration, the user may feel uncomfortable when the measurement sensor contacts his/her body. To overcome this limitation, respiration measurement methods [5, 6, 7, 8, 9] based on frequency-modulated continuous-wave (FMCW) radars are being actively examined. FMCW radars enable the non-contact extraction of distance and displacement information from the target, based on the frequency difference between the transmitting and receiving radio waves. Moreover, because such radars use millimeter waves, the power consumption is low, and the devices can be incorporated into a wide range of applications, owing to the small size of the sensor [10, 11]. In addition, in the context of the COVID-19 pandemic, contactless monitoring systems are advantageous for implementing infection control. In the existing studies [5, 6, 7, 8, 9], respiration was measured by considering the magnitude and phase of the radar transmission/receiving demodulation signal only. Notably, even invisible micromovements of the human body that occur while standing withoutany support may lead to a distorted signal as the output, owing to the combined signal resulting from the chest movement and other micromovements, which decreases the detection accuracy. Therefore, research [12; 13; 14; 15] to monitor respiration in a moving situation is being performed to alleviate the random body movement (RBM) phenomenon that causes distorted signals, and to expand the application range of non-contact respiration methods. Several researchers [12; 13] extracted the respiratory frequency, in terms of the modulated frequency shift, by using the motion direction detection as a DC offset, extracted through the phase of the signal that was distorted as a result of the RBM phenomenon. However, because the approach is aimed at predicting the frequency range that is distorted according to the degree of movement, the exact respiratory frequency cannot be determined. Certain researchers [14] used the range-bin alignment method, by combining the bidirectional distance information extracted by two radars, while other researchers [15] discarded the energy that was more than the average value of the respiration signal in a fixed range-bin. Notably, in the method of mitigating the RBM phenomenon through range-bin alignment, the complexity of the structure increases, owing to the use of two radars, and the computational complexity increases, owing to the continuously varying range-bin tracking. In this study, we deviated from the viewpoint of signal amplitude and phase change associated with chest micromovement caused by respiration. Specifically, we developed a differentiated method for measuring respiration through microvelocity changes according to chest movement. We considered the velocity of vital signals measured with a single channel using the FMCW radar.