1. Introduction
Respiratory rate is an important index reflecting human physiological states. Firstly, whether a life exists or not can be determined by the respiratory state. Secondly, parametric abnormalities of respiratory activities often reflect health emergencies such as respiratory choking and asthma. Finally, humans in cars, indoor spaces, and other special environments can be effectively protected through long-term intelligent monitoring of human respiratory states. Microwave-based noncontact detection is irreplaceably advantageous in convenience in comparison with contact respiration detection. Therefore, electromagnetic-wave-based noncontact respiration detection has recently become a hot research topic. Bioradars are widely used in medical applications such as sleep monitoring [
1] and SIDS detection [
2]. In general, noncontact respiration detection is carried out using the following two approaches.
Ultra-wideband (UWB) radar [
3,
4,
5,
6]: The UWB radar works by transmitting periodical pulse signals at the transmitter. The pulse period is longer than the transmission path duration, so near-field clutter is separated from echoes which contain target information in the time domain. Hence, near-field clutter and power leakage of the transmission can be mitigated with the UWB radar. In addition, low power consumption can be easily designed for the UWB radar due to its large bandwidth and low average power consumption. Nevertheless, the application of UWB radar is generally limited due to the high cost of the hardware.
Continuous wave (CW) radar: Compared with UWB, the CW radar has a simpler structure. Several types of structures are generally applied in CW radar, such as the homodyne structure [
7], double-sideband structure [
8], and direct intermediate frequency sampling structure [
9].
Unlike UWB radars, the propagation delay time cannot be measured with CW radars, making it difficult to obtain distance information. In addition, the transmitter is coupled with the receiver in a CW radar. Hence, direct current bias and low-frequency noise are generated, which influence detection results. Nevertheless, the simple hardware structure and low hardware cost of CW radars are always needed in the industry. In this regard, Sandra Costanzo proposed low-cost flexible respiration detection with a universal software radio platform (USRP) [
10]. In addition, Carolina Gouveia summarized a solution to random movement in aspects of hardware and signal processing [
11].
Taking the above discussions into consideration, few works examine breath detection with random orientation of the test subject. The radars based on the biological mechanism of chest wall micromotion do not perform well enough without the correct orientation of the antennas and of the test subject. During sleep monitoring, the test subject cannot be asked to maintain one posture throughout the night. When the test subject sleeps on their side, the antenna cannot be facing towards their chest wall. As a result, effective information can hardly be detected with radars based on chest wall micromotion detection principles. In addition, during monitoring of a driver’s respiration, the detection error of a single-static radar will be increased when the driver twists their body while operating the steering wheel.
The problems mentioned above must be solved. Specifically, radars should be insensitive to human body rotation, and their hardware structures need to be simplified in their realization. Meanwhile, the hardware cost must be brought down, and noise generated from random human motion must be removed. In this way, human respiratory rate can be accurately detected with bioradars. To solve these problems, we propose a respiratory rate detection scheme by exploiting forward-scatter-based bioradar. In this system, the transmitter and receiver are arranged on opposite sides of a person so that their abdomen is placed on the baseline. The radar cross-section (RCS) will change during human respiration, causing a change in the received signal amplitude. The respiration curve can be obtained through envelope detection of the received signal.
The main contributions of this paper can be summarized in the following:
Considering the disadvantage of traditional detection in that it relies on chest wall micromovement but is sensitive to random orientation of the human, a novel detection scheme based on human RCS changes is proposed. Respiration-based changes of RCS are analyzed. Then, the analytical expression of the received signals is derived.
To confirm the theoretical model, we conducted the respiration detection experiments in an anechoic chamber with a forward scatter radar. Experimental results show that the respiratory rate is accurately detected by the proposed scheme.
To test the system’s performance in different orientations, the FSR system for calibration of the contact sensor was completed indoors. The verification results show that the system is insensitive to human rotation. The respiratory rate can still be measured with rotation of less than 90°. The reliability of the theoretical model is verified by the experimental data.
2. Theoretical Model
During respiration, the chest volume is changed due to muscle contraction and relaxation. As a result, a pressure difference is generated between the internal and external environments of the chest. Air flows from the high-pressure area to the low-pressure area, periodically entering and exiting the lungs. The motion of the thorax and abdomen is driven by respiratory motion. The body shape is changed as a result of abdominal motion. Consequently, the RCS is changed, and thus, the amplitude of the received signal changes. In this way, the human respiratory state is detected. A radar system based on this biological mechanism, forward scatter radar (FSR)—which is sensitive to RCS changes—is introduced.
As a special bistatic radar system, FSR is characterized by a bistatic angle reaching up to 180°. Existing studies mainly focus on FSR detection of objects such as stealth aircraft, unmanned aerial vehicle (UAV), and automobiles [
12,
13]. FSR has a very interesting feature: With a bistatic angle of less than 180°, the change of RCS only depends on the projection outline area of an electrical conductor and the wavelengths of electromagnetic wave signals; the surface wave absorption features of the target are irrelevant (it is insensitive to clothing textures during human respiration detection). To the best of our knowledge, respiration detection with FSR has not been well investigated in existing literature. The main idea of most existing studies is to detect the micro-Doppler features changed by chest wall micromotion. In this paper, we found that the amplitude of the received signal changes with the changes as a result of human respiration.
Figure 1 shows the RCS change pattern of the FSR radar.
As shown in
Figure 1a, BL is the baseline, namely, the connecting line between the transmitter and the receiver. α is the included angle between the normally-oriented human and the baseline; it is defined as the orientation angle. δ is the antenna beam angle.
In this paper, the scattered field generated by human surface currents and the electromagnetic field of human penetration by electromagnetic waves are neglected. Therefore, we have the following equation:
Ere denotes the electric field from the signal to the receiver and
E0 denotes the electric field without the body. The power of the transmitter is constant, so
E0 does not change.
Esh is a shadow field generated by the human body and is linearly correlated with RCS generated within the scope of antenna exposure on the human body.
Esh varies with the change of human RCS. Computation of near-field RCS is very complicated. However, RCS of the target—denoted by
σb(r)— can be obtained using Equation (2) when the bistatic angle approaches
βb = 180°, according to the RCS computation equation proposed by Chernyak [
14]:
In Equation (2), At is obtained through Babinet’s principle and refers to the aperture of the accurate human outline within the scope of antenna exposure. ρ denotes the radial component at a random point in the effective aperture, r denotes the unit vector in the direction of the receiver, and λ denotes the wavelength of the transmitted signal.
As shown in Equation (2),
ρ is perpendicular to
r under
βb = 180°. Then, Equation (3) is obtained.
where
St refers to the area of the equivalent aperture. According to [
15,
16], the theory is accurate when
βb = 180
° and
BL > 2D2/λ, where
BL denotes the distance between a human body and the transmitter or the receiver and D denotes the maximum antenna size. Furthermore, the following equation is obtained using Equation (3):
where G
t denotes the antenna’s gain of uniform exposure with the area of
St. The change of RCS in human respiration can be analyzed according to Equation (4).
As seen in
Figure 1a, the thorax expands and the abdomen gets larger during human inhalation, so RCS increases. During exhalation, the external intercostal muscles and diaphragm muscles relax, and the abdomen gets smaller, so RCS decreases. As seen in
Figure 1b, RCS values can be estimated with the Babinet method. Using a 34-year-old adult male as an example, the abdomen width is 25 cm during inhalation and 29 cm during exhalation within the antenna exposure area. Furthermore, the human projection can be roughly interpreted as a rectangle that is 25 cm wide and 25 cm high (inhalation state). During exhalation, the waist gets larger, and
Figure 1b is added onto the projection face. As seen in the quantitative analysis results in
Figure 2a,b, the human abdomen was observed at the orientation angle
α = 0° during exhalation and inhalation. We placed another black belt at 26 cm above the waistband. Then, after the detection of edges with ImageJ, the change in the abdomen area was obtained through the processing of these pictures. Finally, the rate of expiratory area to inspiratory area is equal to 95.483%. According to Equation (3), the RCS has decreased to 91.17%.
ξ is the RCS attenuation coefficient to quantify the change of the RCS.
Forward scatter radar (FSR) was designed for respiration rate detection according to the above analysis. In comparison with traditional detection techniques, FSR has advantages in analysis of RCS changes during human respiration and is insensitive to human rotation. It can be observed in
Figure 1 that RCS at the exhalation and inhalation states changes as a whole after human rotation.
In the FSR, a single-frequency continuous wave signal is sent by the transmitter. The transmitted signal
T(
x) can be expressed as:
where
A is the amplitude and
φ is the initial phase. During human respiration, the thoracoabdominal motion changes with respiration, thus leading to the change in RCS. As a direct consequence, the amplitude of the received signal is changed and
A turns into
A(t) varying with time, or
t. Let
ξ denote the RCS ratio between exhalation RCS and inhalation RCS, where 0 <
ξ < 1.
B(t) ∈ [−1,1] is deemed as a normalization function for the change of the respiratory capacity. We assume that RCS is linearly influenced by human respiratory motion. The following equation can be obtained:
where
Esh(t) denotes the electric field strength of the shadow field,
Einhale is the electric field strength of the shadow field during inhalation, and
Eexhale denotes the electric field strength of the shadow field during exhalation. According to Equation (1), we have:
where
E0 denotes the amplitude of the received signal for the case that there is nobody in the field. Incorporating Equation (6) into Equation (8), we get:
According to Equation (9), the change in amplitude can be deemed as a course of amplitude modulation. During exhalation, amplitude of the received signal is at its maximum, i.e., AMAX = E0 – ξEinhale, while during inhalation, the amplitude is at its minimum, namely AMIN = E0 – Einhale. In a practical application, E0, AMAX, and AMIN can be measured easily.