A Hybrid Radar-Camera Sensing System With Phase ... - IEEE Xplore

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 61, NO. 12, DECEMBER 2013

A Hybrid Radar-Camera Sensing System With Phase Compensation for Random Body Movement Cancellation in Doppler Vital Sign Detection Changzhan Gu, Member, IEEE, Guochao Wang, Student Member, IEEE, Yiran Li, Student Member, IEEE, Takao Inoue, Member, IEEE, and Changzhi Li, Senior Member, IEEE

Abstract—This paper presents a Doppler radar vital sign detection system with random body movement cancellation (RBMC) technique based on adaptive phase compensation. An ordinary camera was integrated with the system to measure the subject’s random body movement (RBM) that is fed back as phase information to the radar system for RBMC. The linearity of the radar system, which is strictly related to the circuit saturation problem in noncontact vital sign detection, has been thoroughly analyzed and discussed. It shows that larger body movement does not necessarily mean larger radar baseband output. High gain configuration at baseband is required for acceptable SNR in noncontact vital sign detection. The phase compensation at radar RF front-end helps to relieve the high-gain baseband from potential saturation in the presence of large body movement. A simple video processing algorithm was presented to extract the RBM without using any marker. Both theoretical analysis and simulation have been carried out to validate the linearity analysis and the proposed RBMC technique. Two experiments were carried out in the lab environment. One is the phase compensation at RF front end to extract a phantom motion in the presence of another large shaker motion, and the other one is to measure the subject person breathing normally but randomly moving his body back and forth. The experimental results show that the proposed radar system is effective to relieve the linearity burden of the baseband circuit and help compensate the RBM. Index Terms—Doppler radar, noncontact, linearity, random body movement, saturation, vital sign detection.

I. INTRODUCTION

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HE research of Doppler radar vital sign detection has become a hot topic since 1970s [1]–[3]. It provides a noncontact means to detect the subject’s respiration and heartbeat

Manuscript received July 05, 2013; revised October 08, 2013; accepted October 17, 2013. Date of publication November 12, 2013; date of current version December 02, 2013. This work is supported in part by the Cancer Prevention Research Institute of Texas (CPRIT) RP120053, in part by the National Science Foundation (NSF) ECCS-1254838, and in part by the National Instruments. This paper is an expanded paper from the IEEE International Microwave Symposium, June 2–7, 2013, Seattle, WA, USA. C. Gu was with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409 USA. He is now with MaxLinear Inc., Irvine, CA 92618 USA (e-mail: [email protected]). G. Wang, Y. Li, and C. Li are with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409 USA (e-mail: [email protected]). T. Inoue is with the National Instruments, Austin, TX 78759 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMTT.2013.2288226

using Doppler nonlinear phase modulation [4]–[7]. Many research efforts have been made to push forward the technology in the past few decades. For example, quadrature receiver has been proposed to solve the null point problem [8], arctangent demodulation was introduced to recover the phase information [9], and the whole radar system has been integrated on a single CMOS chip [10]. Though many technical challenges have been overcome, RBM remains one of the most difficult to solve. Because of the unpredictable nature, the RBM noise cannot be simply filtered out. Moreover, the large RBM at times results in signal clipping at baseband output, which not only challenges the linearity of the radar circuit but also poses further obstacle to RBMC. Researchers have made a few studies to fight against the RBM problem. The subject’s motion of fidgeting interference was removed using the empirical mode decomposition [11]. However, it only works for well defined motion patterns. The unwanted motion in the handheld Doppler radar was cancelled using a motion sensor of accelerometer placed on the radar antenna to compensate the antenna motion [12]. This method works well to cancel the hand-induced radar motion, but it does not account for the subject’s body motion. To effectively remove the RBM, several sensors array techniques have been proposed. For example, [13] presented a differential method using two radar sensors and [14] introduced injection locking radars array for RBMC. The subject is supposed to be in between the two radar sensors with one sensor measuring in front of the subject and the other one measuring from behind. However, the sensors array approaches inevitably add to the system complexity, cost, and power consumption. Moreover, they have some inherent limitations. For example, [13] relies on the baseband signals for RBMC. It does not work when the signals at baseband output are clipped due to circuit saturation, which is likely to happen in the presence of large body movement. In [14], users have to either sweep the radar’s carrier frequency or adjust the subject’s position, in order to establish a specific cancellation condition, which makes RBMC quite inconvenient. A Doppler radar system with RBMC technique using phase compensation has been presented in [15]. Since both the vital signs and the RBM modulate the radar received signals in the phase, it would be straightforward to cancel the RBM by adding external phase information that is opposite to that of RBM modulated in radar received signals. In the proposed radar system, a camera measures the subject’s body motion that is fed back to the radar system to compensate the RBM. It should be noted

0018-9480 © 2013 IEEE

GU et al.: HYBRID RADAR-CAMERA SENSING SYSTEM WITH PHASE COMPENSATION

that, although there are techniques using cameras for monitoring the physiological signals, they have inherent limitations. For example, the algorithm proposed by MIT researchers can reveal the subtle motions such as the pulse, but it does not work if the subject is moving [16]. Other approaches include using the HD camera [17] and the Microsoft Kinect sensor (depth camera) [18] with advanced algorithms for respiration monitoring. It is expected that the Doppler vital sign detector would be integrated into mobile devices such as smart phones in the near future for applications such as healthcare and security. Advanced cameras such as depth camera are not available on mobile devices due to the cost and size issues. Moreover, simple motion extraction algorithms rather than advanced ones are preferred on mobile platform in order to save computational load and power. In the proposed radar system, an ordinary camera on iPhone5 is used to measure RBM using a simple motion extraction algorithm. Three RBMC strategies are proposed: 1) phase compensation using a phase shifter at the radar RF front-end, 2) phase compensation for the baseband complex signals, and 3) movement cancellation for the demodulated signals. The first strategy compensates phase at RF front-end. It helps to relieve the radar circuit from potential saturation due to the large body motion. The other two strategies, which are implemented in the baseband digital domain, can perform more accurate fine tuning to compensate the phase. The linearity of the radar system is strictly related to the circuit saturation problem in noncontact vital sign detection. In homodyne radar receiver, the baseband circuit is usually configured with high-gain, in order to boost the weak vital sign signals to a high voltage level that is immune from the ADC quantization noise. However, the large body motion can cause circuit saturation, i.e., signals are clipped at the baseband output. The circuit saturation fails the existing RBMC techniques such as the differential method in [13] and the baseband phase compensation strategies proposed in this paper. What is more, due to the non-linearity of the RF/analog circuit, the saturation may produce unwanted harmonics and inter-modulation components that may interfere with the vital sign signals. Therefore, it is beneficial and necessary to study the linearity issue in Doppler radar system. II. RADAR SYSTEM WITH RBMC In the past few decades, various radar system architectures have been proposed for noncontact vital sign detection. In summary, there are basically two types of radar systems: heterodyne and homodyne. The heterodyne system has the merits of less DC offset and mismatch, which are two important factors in radar vital sign detection [19]. Typical heterodyne radar systems include the frequency tuning radar [20], the digital IF radar [19], and the heterodyne radar [21]. However, the heterodyne radar has its demerits, such as image problem, more complex system architecture and lower level of integration. The homodyne radar system is becoming more popular due to its simplicity and high level of integration. Fig. 1 shows the block diagram of the proposed radar sensor system with RBMC. It is a homodyne architecture that has two stages of DC tuning, one at the RF frond-end and one at baseband, which allows the radar sensor to work in the

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Fig. 1. Block diagram of the proposed 2.4 GHz radar sensor system with RBMC. The camera measures the RBM that is fed to the radar system for motion compensation: phase compensation at RF (1) relieves the system’s linearity burden, while baseband compensations (2) (3) allow accurate finetuning. TABLE I LIST OF THE KEY BUILDING BLOCKS OF THE RADAR SENSOR SYSTEM

DC-coupled mode that preserves all the DC information [22]. The key building blocks of the proposed radar system are listed in Table I. In noncontact vital sign detection, the radar transmits a single tone signal to the subject person. Both the RBM and the periodic motion of the chest wall will modulate the single tone signal in the phase. The reflected signal from the chest wall is received at the radar RF front-end [3]. It is (1) where is the amplitude, is the carrier frequency, is the wavelength, is the vital sign signal, is the RBM, and is the residual phase. It is seen from (1) that both vital sign signal and RBM are phase modulated. Due to the random nature of the body movement, the phase information from the RBM may interfere with the vital sign signals, making them very difficult to be identified. It is possible to reduce the interference from the RBM by compensating its phase information but retaining that from the vital sign signals. In [15], the body motion is extracted by using a Cannon camera to track a marker that is placed on the subject. In this paper, iPhone5 was used to measure the body motion, which better validates the feasibility of integrating the vital sign detector into smart phones. A simple markerless motion extraction algorithm is proposed. The flow chart of the algorithm is shown in Fig. 2. The iPhone5 measured video has a resolution of 1900 pixels 1080 pixels and a frame rate of 30 frames/second. If the subject is moving back and forth in front of the camera, the number of pixels occupied by the subject in the video is also changing according to the body motion. At the beginning of motion extraction, the first frame of the video is extracted, converted to gray image, and saved as reference.

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easily saturate the receiver chain. Advanced signal processing at baseband can be used to further improve the SNR. B. Phase Compensation for Complex Signals Fig. 2. Flow chart of the simple algorithm for motion extraction.

The subsequent frames are also converted into gray images and then subtracted from the reference frame to generate the difference frames. The stationary objects in the video result in black background (pixel value of zero) in the difference frame, while the moving subject leads to pixel values larger than zero. The summation of the pixels’ values reflects the pattern of the subject movement. It should be noted that the actual amplitude of the RBM does not have to be known. The RBMC is realized by linearly scaling the camera-measured motion pattern in a range of values to find the optimum point that leads to the maximum SNR of respiration. Though the proposed algorithm may not completely extract the body movement, it reduces the computational load and provides a rough motion pattern estimation, which helps to reduce the RBM to the extent that the vital sign signals can be identified. Moreover, it can also help to reduce the linearity burden for the radar system, which will be discussed in details in the next section. In the proposed radar sensor system, three phase compensation strategies are presented for RBMC.

If the RBM is not large enough to saturate the baseband amplifier, the phase compensation for RBMC can be realized at baseband in the digital domain. After quadrature down-conversion, the baseband signals are

(3)

(4) where (t) is the residual phase noise considering the initial position of the subject and the noise from the circuit, are the amplitudes and are the DC offsets of the channels respectively. The second RBMC strategy is the phase compensation for the baseband complex signals. After DC calibration of removing the DC offset but retaining the DC information [23], the two channels can be combined to form a complex signal

(5)

A. Phase Compensation at RF Front-End The first RBMC strategy is to compensate the phase information that is caused by RBM at the RF front-end, while retaining the phase information of the vital sign signals. It is realized by using a phase shifter at the RF front-end. It is seen from (1) that the phase modulation is due to RBM, which can be reduced if a phase shift of is added to the carrier signal. Based on the camera-measured RBM, the time-variant phase information can be derived to compensate the radar-measured signal through the phase shifter to reduce the RBM, as shown in Fig. 1. Therefore, the signal that appears at the input of the mixer is

(2) is the amplitude considering the total receiver gain, is the camera-measured RBM, is the deviation between the actual RBM and the camera-measured RBM. Since the camera has limited resolution and the proposed motion extraction algorithm cannot capture the precise body motion, there is a residual error remaining in (2). Though the cameraaided approach cannot completely remove the RBM, it significantly reduces the RBM and helps relieve the RF front-end from abrupt fluctuations that are caused by the RBM, which may

where

where is the amplitude. A similar phase compensation process as (2) can be employed at the baseband output to reduce the RBM. After RBMC, the vital sign signal can be recovered using arctangent demodulation [9]. The large RBM means large phase modulation, which leads to long trajectory that may cover more than one quadrant on the constellation. The reduction of the large RBM leads to shorter trajectory, which may eliminate the necessity of phase unwrapping in arctangent demodulation [24]. C. Phase Compensation After Demodulation The third RBMC strategy is realized after the phase demodulation. It is the most straightforward approach that does not need any change to the framework of the existing radar systems. It compensates the phase information by directly adding the opposite phase information of RBM, which is measured by a camera to the demodulated signal, which is (6) It is easy to be implemented without any change to the radar architecture and demodulation approaches. However, the downside of this strategy is that the large trajectory, which is caused by large RBM, may likely result in phase discontinuity in arctangent demodulation. In this case, either the phase unwrapping or the DACM demodulation [25] should be applied to compensate the phase discontinuity.


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III. RADAR SYSTEM LINEARITY ANALYSIS In Doppler radar vital sign detection, it is common that the baseband output may get clipped when the subject is too close to the radar antennas. It is because the radar system amplifies the vital sign signals to such a high voltage level that it saturates the baseband amplifier. In vital sign detection with RBMC, it is more common to have the saturation problem, because the RBM is much larger than either respiration or heartbeat. The circuit saturation problem not only fails vital sign detection but also allows no techniques to be applied to remove the RBM to recover the vital sign signals. A. Radar System Considerations The Doppler radar noncontact vital sign detection is based on electromagnetic backscattering. The radar sensor transmits a single tone unmodulated signal to the subject’s chest wall, which backscatters part of the signal back to the radar receiver. Because of fewer RF stages compared with a heterodyne receiver, the baseband of the homodyne radar receiver is usually configured with very high gain. It is generally believed that the saturation problem happens at baseband. According to the radar equation, the power received at the radar receiver antenna is (7) where is the transmited power, is the transmitting antenna gain, is the subject’s radar cross section (RCS), is the aperture of the receiving antenna, is the receiving antenna gain, and is the distance between the subject and the radar antenna. In the proposed 2.4 GHz radar system, a free-running VCO outputs a power of 4.5 dBm. A Wilkinson power divider splits the power equally into two parts: one for transmitting and one for driving the LO port of the mixer. So the power that is fed into the transmitting antenna is 1.5 dBm. Both transmitting and receiving antennas have a gain of 5.8 dBi. The subject’s RCS is estimated to be around 0.3 0.5 m , which is roughly equal to the radiation area on the subject. The receiving antenna’s aperture . Fig. 3(a) shows the setup of two single-element patch antennas in the radar system. The antennas are placed close to each other. The direct coupling between two antennas was estimated in HFSS by simulating the antenna pair as used in the practical setup, as shown in the inset of Fig. 3(b). The simulation result shows a 21 of dB, which is equal to the isolation between the antennas. The power at the receiver antenna was experimentally analyzed, as shown in Fig. 4. The subject person was sitting at a distance of from the radar system. The receiver antenna, instead of being connected to the receiver chain, was connected to the spectrum analyzer to evaluate the received power level. The experimental results and the theoretical analysis using the radar equation are shown in Fig. 5. It is seen that the measured power is much higher than the theoretical analysis at all distances from 0.3 m to 1.5 m. This is because the theoretical analysis only accounts for the useful backscattered signal from the subject, while the measured power not only includes the backscattered signal, but also includes reflections from the surrounding environment and the coupling between TX and RX. It

Fig. 3. (a) Photograph of the 2.4 GHz radar sensor. The TX/RX antennas are of the antennas pair as used in the close to each other; (b) Simulated practical setup. Inset shows the simulation model in HFSS.

Fig. 4. Experimental setup for measuring the radar received power.

Fig. 5. Received power at radar front-end with a transmitting power of 1.5 dBm: theoretical versus measured. The difference is due to the direct coupling from TX to RX and the reflections from the surrounding stationary objects.

is also seen in Fig. 4 that the signal power remains almost constant at dBm after 1 m. This is because the direct coupling and stationary reflections start to dominate. The strong coupling contributes to most of the power received at the RF front end. It needs to be taken care of in the receiver design in order to avoid potential linearity issues. However, this does not affect the radar sensitivity because it does not contain phase-modulated information and will generate a DC offset after the mixer. The received power of the reflections depends on the nature of the surrounding subjects, e.g., metal subjects would reflect more power back to the radar than wood ones. The reflections can be relieved using the coarse-tuning technique at RF front end [22], [26]. The remaining DC offset can be compensated using the baseband fine-tuning [27]. The above analysis shows that the radar received power is a combination of the radar backscattering power, the direct coupling, and the stationary reflections, which makes the RF design

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of the radar system more challenging. The direct coupling and the stationary reflections do not carry any useful information, but they significantly add more burden to radar circuit. Attentions should be spent on radar RF front end design in order to avoid nonlinearity in RF amplifiers. For example, in Fig. 5, the received power is dBm at 0.5 m. If two LNAs as in Table I are used, the signal would be boosted to dBm, which exceeds the input P1 dB of many RF amplifiers. Special considerations should be given to the linearity of the following stage of amplifier or mixer. B. Non-Linear Phase Modulation Both vital signs and body movement modulate the radar signal in the phase. They have no effect on the radar received power if the body movement amplitude is negligible to the distance between the radar and the subject. In a word, the amplitude modulation is negligible in Doppler radar vital sign detection. Therefore, as long as the radar RF front end is well designed, saturation will not happen at RF chain in vital sign detection. It is usually observed that larger chest wall/body motion leads to larger radar baseband output. If the output voltage reaches the bias voltage or the ground of the rail-to-rail amplifier, the output signals will be clipped and amplifier saturation happens. It is necessary to analyze how the radar output will respond to different degrees of RBM and how RBM affects the radar baseband outputs. The radar baseband outputs, as shown in (3) and (4), can be re-written as follows: (8) (9) where represents the combination of vital signs and RBM or any other kind of movement, and is the amplitude for both channels because the amplitude imbalance can be negligible in modern integrated circuit (IC) technologies. It is seen that the movement is modulated in the trigonometric functions of Cosine and Sine, which have a definitive range of to . Therefore, the peak-to-peak value of baseband signals is limited within the range of to . If is equal to , where is the bias voltage of the baseband rail-to-rail amplifier, the baseband signals are expected to have the range of to . Small Movement: Fig. 6 shows the simulated baseband signals for a 2.4 GHz radar measuring a small vibration motion , where ( is the wavelength of 2.4 GHz), Hz, and is the elapsed time. It is assumed that the amplitude is equal to . According to (8) and (9), results in a trajectory length of , i.e., 1/6 of the entire circle, as shown in Fig. 5(a1)/(a2). The residual phase determines the location of the trajectory on the constellation. When , the trajectory sits in the middle of the first quadrant, and signals are equal in amplitude, as shown in Fig. 6(a1)/(b1). It is because the the projection of the trajectory on the axis is equal to the projection on the axis. However, when the residual phase increases to , the

Fig. 6. Simulated baseband signals for a 2.4 GHz radar measuring a small with different residual phases of vibration motion and on the constellation: (a1)(a2), and in the time domain: (b1)(b2).

trajectory moves to sit on top of the constellation. channel starts to overwhelm channel in amplitude, because most of the trajectory is projected on the axis rather on the axis, as shown in Fig. 5(a2)/(b2). It is also noted that the amplitude of channel in Fig. 6(b2) is larger than that of channel in Fig. 6(b1). Therefore, the amplitude of the baseband signals is determined not only by the target vibration amplitude but also determined by the residual phase. In order to refrain from saturation, the radar baseband needs to accommodate the maximum amplitudes when the residual phases is 0, , or . It is actually when one channel is at the optimum point, and the other one is at the null point [8]. Large Movement: The amplitude of channel depends on the length of the trajectory projection on the axis. The largest trajectory projection is equal to the diameter of the unit circle on the constellation. Fig. 7 shows the simulated baseband signals for a 2.4 GHz radar measuring two large vibration motions with the same residual phase of but different amplitudes of and . When , the trajectory forms half of the unit circle, as shown in Fig. 7(a1). For the channel, the trajectory projection reaches its maximum by covering the diameter of the unit circle, ranging from to , which means to as can be verified in Fig. 7(b1). As the amplitude increases to , the trajectory covers the whole unit circle, as in Fig. 7(a2). Though the phase information crosses all four quadrants on the constellation, the signals remain in the amplitude range of to , as shown in Fig. 7(b2). It is because the trajectory projection does not exceed the length of the diameter.


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TABLE II PARAMETERS USED FOR RADAR SIMULATION

Fig. 8. RBM used in simulation with an SNR of 30 dB.

Fig. 7. Simulated baseband signals for a 2.4 GHz radar measuring two but different amlarge vibration motions with the same residual phase of and on the constellation: (a1)(a2), and in the time domain: plitudes of (b1)(b2).

C. Signal to Noise Ratio (SNR) The above analysis shows that larger movement does not necessarily lead to larger radar baseband output. It is possible to design a radar system that can be free from saturation at baseband. It is done by properly adjusting the baseband gain so that the baseband amplifier does not get saturated when the trajectory projection on the axis is equal to the diameter. That is, if the baseband gain is optimized in such a way that the vibration motion as in Fig. 7(a1) does not lead to baseband saturation, any other larger motions will not saturate the baseband amplifier. It should be noted that the amplitude in Fig. 7(a1) is normalized and the actual amplitude is , i.e., the actual amplitude corresponds to the radar carrier frequency. This rationale can be used to guide the design of saturation-free radar systems for measuring large vibration motions. However, in vital sign detection with RBM, the SNR of the vital sign signals has to be taken into consideration. Because RBM is usually larger than vital signs, in order to design such a saturation-free radar system, the baseband gain must be lowered so as to be optimized for the large RBM. However, lowering baseband gain also degrades the vital sign signals, which leads to poor SNR that cannot be used to recover the vital sign signals. In the proposed vital sign detection system with RBM, the large RBM can be compensated at the RF front end, so that high gain is allowed at baseband to amplify the vital sign signals. D. Simulation Radar simulation has been carried out to validate the phase compensation at RF front end to help relieve the baseband from

Fig. 9. Simulation of RBMC at the RF front-end. The radar measured signals at the output of the mixer (a), and the baseband amplifier (b). The circled areas in (b) indicate where saturation happens. Q.N.: quantization noise of a 10-bit data acquisition system.

saturation. Table II shows the simulation parameters that are chosen to be the same as the real radar system. The radar system is biased at 3.3 V. The vital sign signal is interfered by a RBM with peak-to-peak amplitude of cm. Fig. 8 shows the RBM that is used in simulation for phase compensation. It has an SNR of 30 dB that mimics the camera-measured results. The simulation results are shown in Fig. 9. It is seen that, without RBMC, the baseband amplifier will be saturated. This is because the RBM introduces large phase modulation, leading to large signal at the mixer output. However, after RBMC is applied, it is seen in Fig. 9(a) that the undesired signal fluctuations at the mixer output are significantly reduced in amplitude. The phase modulation from RBM is reduced and the remaining phase information better represents the vital sign signals. Since the vital sign signals are so weak at the mixer output, they cannot be directly digitized considering the quantization noise level shown

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Fig. 10. Experimental setup for phase compensation at the radar RF front end. The radar was measuring two motions of the phantom and the shaker. Phase information was compensated at the RF front end using a phase shifter to reject the large shaker motion and recover the small phantom motion.

in Fig. 9(a). With RBMC, the baseband amplifier is relieved from saturation and can boost the vital sign signals to a level that can be digitized with a reasonable SNR, as shown in Fig. 9(b). IV. EXPERIMENT Experiments have been carried out in the lab environment to validate the phase compensation techniques in radar vital sign detection system. A. Phase Compensation at RF Front End Large body movement leads to large phase modulation, which may saturate the baseband amplifier. However, if the phase information is compensated at the RF front end, the phase modulation from large movements can be reduced and thus the baseband circuitry can be relieved from saturation. Fig. 10 shows the experimental setup for phase compensation at RF front end. A phase shifter, as shown in Table I, was used to provide additional phase information to the radar measured RF signals. The radar was measuring two motions of the phantom (Varian Medical Systems, Palo Alto, CA) and the shaker (ELECTRO-SEIS APS 113, APS Dynamics, Inc.). The phantom, which was placed on top of the shaker, has a fixed motion pattern [27], while the shaker can be programmed to perform any kind of movements. A metal plate was placed on the phantom to increase the reflections back to the radar. In order to provide control signals to the shaker and the phase shifter, two sinusoidal signals were programmed in LabVIEW. The two signals were programmed out of phase, i.e., one is sine and the other one is -sine. Both signals are converted to analog using NI USB-6009 Multifunctional DAQ. The “sine” signal is used as tuning voltage to control the phase shifter, and the “-sine” signal determines the shaker motion. There are additional attenuation and phase shift in LabVIEW for the “sine” signal. The attenuation is used to adjust the tuning voltage of the phase shifter so as to adjust the phase information fed into the radar RF front end. The phase shift in LabVIEW has a small fixed value that is used to compensate for the phase delay in the circuit. The radar measured signals were recorded and demodulated using MATLAB on the laptop. The first experiment is to evaluate how well the radar can reject a movement by compensating its phase at RF front end.

Fig. 11. (a) The gradual process of phase compensation at the RF front end. The vibration motion was mostly cancelled. (b) Spectrum comparison of the radar measured signals before/after phase compensation shows a rejection of 29.12 dB.

In this experiment, the shaker was programmed to vibrate sinusoidally at 0.5 Hz. The phantom was turned off. By looking at the real-time signals on the laptop and gradually tuning the control voltage of the phase shifter at the RF front end, the phase compensation effect can be observed. Fig. 11 shows the experimental results. It is seen in Fig. 11(a) that, before phase compensation, the signals exhibit large amplitudes. This is the situation where the radar measures the shaker motion without phase compensation at RF front end. However, after phase compensation is turned on by feeding the tuning voltage to the phase shifter, both signals start to decrease in amplitudes. This is because out-of-phase information is added to the received RF signals to cancel the phase information from the shaker motion. Fig. 10(b) shows the spectrum comparison of the complex signals before and after the phase compensation. It is seen that, most of the shaker motion is suppressed after phase compensation. The phase compensation at the RF front end provides a rejection of 29.12 dB. In the second experiment, the radar was measuring two motions of the phantom and the shaker. Phase compensation was performed at the RF front end. The experimental results are shown in Fig. 12. It is seen in Fig. 12(a) that, without phase compensation, a complex motion pattern is observed due to the existence of two motion patterns. Moreover, the channel is in such a high amplitude that it saturated at the supply voltage of 3.3 V. However, after turning on the phase compensation, the saturation disappears and a single motion pattern is observed for the phantom, as seen in Fig. 12(a). To evaluate the accuracy of the recovered phantom motion, a separate DC radar was used to measure the phantom motion and the measured motion


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Fig. 13. Experimental setup in lab environment for Doppler radar vital sign detection with RBMC. An iPhone was used to measure the RBM.

Fig. 12. (a) The radar measured signals for two motions of the phantom and the shaker with and without phase compensation at the RF front end. (b) After phase compensation, the phantom motion was recovered and compared with that measured by a DC coupled radar separately.

pattern was compared with that recovered from phase compensation. It has been demonstrated that the DC radar is able to capture the complete motion pattern having a stationary moment [22]. The comparison result is shown in Fig. 12(b). It is seen that the two signals match very well. There is small fluctuation on top of the curve (the stationary moment). This is because the phase compensation has limited resolution and cannot completely cancel all the phase information from the shaker motion. The phase compensation at RF front end greatly lightens the linearity burden of the radar system, and allows advanced algorithms to be carried out at baseband in the digital domain. B. Vital Sign Detection With RBMC at Baseband The RBMC can be performed at radar baseband in the digital domain as long as the baseband circuit is not saturated. Experiments were carried out in the lab environment to measure a subject breathing normally but randomly moving her body when the radar baseband amplifier was not saturated. The experimental setup is shown in Fig. 13. A pulsed sensor (UFI 1010 pulse transducer) was attached to the subject’s finger to provide reference heartbeat, and a chest belt (UFI 1132 piezo-electric respiration transducer) was used to provide reference respiration. An iPhone5 was placed over the radar to mimic the situation of integrating the radar sensor with the smart phone. The iPhone5 recorded the video during the measurement and post processing was carried out to extract the camera-measured RBM. The RBMC was carried out for the baseband complex signal. The actual amplitude of the RBM does not have to be known. Once the motion pattern of the RBM is obtained, the RBMC is realized by scaling the motion pattern in a range of values to find the point that leads to the maximum SNR of respiration.

Fig. 14. (a) RBM measured by camera using a marker. Spectra of the radarmeasured signals without RBMC (a) and with RBMC (b) [15]. The insets show the corresponding time-domain signals with duration of 20 seconds.

Due to the lack of a defined marker and the simplicity of the proposed motion extraction algorithm, less RBM information was retrieved in this work. Therefore, to better demonstrate RBMC, the subject person was asked to perform simpler RBM than that in [15]. In this way, the extracted RBM could be more accurate and could lead to better RBMC in radar sensing. Two experiments were carried out in this work. In the first experiment, the subject person was asked to breathe normally with a relatively constant breathing strength. This is to demonstrate that the proposed RBMC technique is able to reproduce the stable breathing pattern. In the second experiment, the subject person performed slow breathing rhythm, such as in sleep, so that there is a stationary moment at the end of expiration [22]. This is to demonstrate if the complete respiration pattern can be preserved in RBMC. The experimental results in [15] using a marker are summarized in Fig. 14. The experimental results of this work are shown

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Fig. 15. (a) RBM measured by iPhone5; (b) the raw signal measured by radar without RBMC; (c) signal after high-pass filtering; (c) signal with RBMC. The insets show the corresponding time-domain signals with duration of 20 seconds.

Fig. 16. (a) RBM measured by iPhone5; (b) the raw signal measured by radar without RBMC; (c) signal after high-pass filtering; (c) signal with RBMC. The insets show the corresponding time-domain signals with duration of 20 seconds.

in Figs. 15 and 16. It is seen that the camera-recorded RBMs in Figs. 15 and 16 are simpler than that in Fig. 14. This is due to the limited resolution of the proposed algorithm and the intention of the subject to perform less RBM in this work. It should be noted that more advanced video processing algorithms will extract more movement information, and thus will allow more complicated RBM to be compensated in radar vital sign detection. Since most body movements are slow, a straightforward method to remove RBM may use a high pass filter. To compare with the proposed RBMC technique, a 4th order Butterworth high pass filter with a corner frequency of 0.25 Hz is used in the first experiments. The same filter with a corner frequency of 0.08 Hz was used in the second experiment. In the first experiment, strong near-DC spectral components are observed as shown in Fig. 15(a). The heartbeat is invisible in the spectrum and the time-domain signal has fluctuations due to the RBM. After applying the high pass filter, it is seen in Fig. 15(b) that the near-DC spectral components are significantly suppressed and the heartbeat signal can be identified. Fig. 15(c) shows the results with RBMC. It is seen that, not only the near-DC interferences are suppressed but also the second harmonic of respiration becomes visible, while it is hard to be identified in Fig. 15(b).

This is because the measured RBM contains more spectral information. It can help to remove more spectral interferences. It is also seen that the time-domain signal using the proposed RBMC technique has more uniform pattern than that using the high pass filter. In both Fig. 15(b) and (c), the radar measured respiration and heartbeat match well with the reference signals from the pulse sensor and the chest belt. Fig. 16 shows the results for the second experiment. It is seen that the stationary moment during the breathing cycle is lost when using the high pass filter. However, it is retained by the proposed RBMC technique. This is because the high pass filter inevitably removes the stationary moment, which is actually the DC information. However, the proposed RBMC does not employ any spectral filtering, but compensates the phase to remove the interference motion and does not affect the completeness of the respiration pattern. V. CONCLUSION A Doppler radar noncontact vital sign detection system with RBMC techniques has been presented. The RBMC is based on phase compensation at radar RF front or at baseband in the digital domain. An iPhone camera was used to track the RBM that


is fed back to the radar system as phase information. The linearity that is strictly related to the circuit saturation problem in radar system has been thoroughly analyzed and discussed. Both theoretical analysis and radar simulation have been carried out to validate the proposed RBMC techniques. Experiments have been carried out for both phase compensation at RF front end and at baseband in the digital domain. The phase compensation at the RF front end helps to relieve the radar baseband from saturation in the existence of large body movement. Experimental results show that the proposed radar system is able to effectively reduce RBM. REFERENCES [1] J. C. Lin, “Noninvasive microwave measurement of respiration,” Proc. IEEE, vol. 63, no. 10, pp. 1530–1530, Oct. 1975. [2] D. D. Mawhinney, Noninvasive Heart Rate Monitor No. RCAPRRL-83-CR-13. RCA LABS Princeton NJ, 1983. [3] E. F. Greneker, “Radar sensing of heartbeat and respiration at a distance with applications of the technology,” in Radar Systems Conf. (RADAR 97), Jan. 1997, pp. 150–154. [4] C. Li, Y. Xiao, and J. Lin, “Experiment and spectral analysis of a a-band heartbeat detector measuring from four sides of low-power a human body,” IEEE Trans. Microw. Theory Tech., vol. 54, no. 12, pp. 4464–4471, Dec. 2006. [5] M. Mercuri, P. J. Soh, C. Pandey, P. Karsmakers, G. Vandenbosch, P. Leroux, and D. Schreurs, “Analysis of an indoor biomedical radar-based system for health monitoring,” IEEE Trans. Microw. Theory Tech., vol. 61, no. 5, pp. 2061–2068, May 2013. [6] J. Wang, X. Wang, Z. Zhu, J. Huangfu, C. Li, and L. Ran, “1-D microwave imaging of human cardiac motion: An ab-initio investigation,” IEEE Trans. Microw. Theory Tech., vol. 61, no. 5, pp. 2101–2107, May 2013. [7] C. Li, V. M. Lubecke, O. Boric-Lubecke, and J. Lin, “A review on recent advances in doppler radar sensors for noncontact healthcare monitoring,” IEEE Trans. Microw. Theory Tech., vol. 61, no. 5, pp. 2046–2059, May 2013. [8] A. D. Droitcour, O. Boric-Lubecke, V. M. Lubecke, J. Lin, and G. T. A. Kovac, “Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring,” IEEE Trans. Microw. Theory Tech., vol. 52, no. 3, pp. 838–848, Mar. 2004. [9] B. K. Park, O. Boric-Lubecke, and V. M. Lubecke, “Arctangent demodulation with DC offset compensation in quadrature Doppler radar receiver systems,” IEEE Trans. Microw. Theory Tech., vol. 55, no. 5, pp. 1073–1079, May 2007. [10] C. Li, X. Yu, C. M. Lee, D. Li, L. Ran, and J. Lin, “High-sensitivity software-configurable 5.8-GHz radar sensor receiver chip in 0.13m CMOS for noncontact vital sign detection,” IEEE Trans. Microw. Theory Tech., vol. 58, no. 5, pp. 1410–1419, May 2010. [11] I. Mostafanezhad, O. Boric-Lubecke, V. Lubecke, and D. P. Mandic, “Application of empirical mode decomposition in removing fidgeting interference in Doppler radar life signs monitoring devices,” in Proc. IEEE Eng. Med. Biolo. Soc. Annu. Int. Conf., Sep. 2009, pp. 340–343. [12] I. Mostafanezhad, O. Boric-Lubecke, V. Lubecke, and A. HostMadsen, “Cancellation of unwanted motion in a handheld Doppler radar used for non-contact life sign monitoring,” in Proc. IEEE MTT-S Int. Microw. Symp. Dig., Atlanta, GA, USA, Jun. 2008, pp. 1171–1174. [13] C. Li and J. Lin, “Random body movement cancellation in Doppler radar vital sign detection,” IEEE Trans. Microw. Theory Tech., vol. 56, no. 12, pp. 3143–3152, Dec. 2008. [14] F. K. Wang, T. S. Horng, K. C. Peng, J. K. Jau, J. Y. Li, and C. C. Chen, “Single-antenna Doppler radars using self and mutual injection locking for vital sign detection with random body movement cancellation,” IEEE Trans. Microw. Theory Tech., vol. 59, no. 12, pp. 3577–3586, Dec. 2011. [15] C. Gu, G. Wang, T. Inoue, and C. Li, “Doppler radar vital sign detection with random body movement cancellation based on adaptive phase compensation,” presented at the IEEE MTT-S Int. Microw. Symp. Dig., Seattle, WA, USA, Jun. 2013. [16] H. Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. T. Freeman, “Eulerian video magnification for revealing subtle changes in the world,” in Proc. ACM Trans. Graphics (TOG)—SIGGRAPH 2012 Conf., Jul. 2012, vol. 31, no. 4, Art. 65.

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[17] S. Wiesner and Z. Yaniv, “Monitoring patient respiration using a single optical camera,” in Proc. IEEE Eng. Med. Biolo. Soc. Annu. Int. Conf., 2007, pp. 2740–2743. [18] N. Burba, M. T. Bolas, D. M. Krum, and E. A. Suma, “Unobtrusive measurement of subtle nonverbal behaviors with the microsoft kinect,” in Proc. IEEE Virtual Reality Workshops (VR), 2012, pp. 10–13. [19] C. Gu, C. Li, J. Lin, J. Long, J. Huangfu, and L. Ran, “Instrument-based noncontact Doppler radar vital sign detection system using heterodyne digital quadrature demodulation architecture,” IEEE Trans. Instrum. Meas., vol. 59, no. 6, pp. 1580–1588, Jun. 2010. [20] Y. Xiao, J. Lin Boric-Lubecke, and V. M. Lubecke, “Frequency tuning technique for remote detection of heartbeat and respiration using lowpower double-sideband transmission in Ka-band,” IEEE Trans. Microw. Theory Tech., vol. 54, no. 5, pp. 2023–2032, May 2006. [21] A. Singh and V. Lubecke, “A heterodyne receiver for harmonic Doppler radar cardiopulmonary monitoring with body-worn passive RF tags,” in Proc. IEEE MTT-S Int. Microw. Symp. Dig., Anaheim, CA, USA, Jun. 2010, pp. 1600–1603. [22] C. Gu, R. Li, H. Zhang, A. Fung, C. Torres, S. Jiang, and C. Li, “Accurate respiration measurement using DC-coupled continuous-wave radar sensor for motion-adaptive cancer radiotherapy,” IEEE Trans. Biomed. Eng., vol. 59, no. 11, pp. 3117–3123, Nov. 2012. [23] W. Xu, C. Gu, C. Li, and M. Sarrafzadeh, “Robust Doppler radar demodulation via compressed sensing,” Electron. Lett., vol. 48, pp. 1428–1430, Oct. 2012. [24] S. Kim and C. Nguyen, “A displacement measurement technique using millimeter-wave interferometry,” IEEE Trans. Microw. Theory Tech., vol. 51, no. 6, pp. 1724–1728, 2003. [25] J. Wang and L. Ran, “Non-contact distance and amplitude independent vibration measurement based on an extended DACM algorithm,” IEEE Trans. Instrum. Meas., to be published. [26] I. Mostafanezhad and O. Boric-Lubecke, “An RF based analog linear demodulator,” Proc. IEEE Microw. Wireless Compon. Lett., vol. 21, no. 7, pp. 392–394, Jul. 2011. [27] C. Gu and C. Li, “DC coupled CW radar sensor using fine-tuning adaptive feedback loop,” Electron. Lett., vol. 48, pp. 344–345, 2012.

Changzhan Gu (S’07–M’13) received the B.S. and M.S. degrees in information and electronic engineering from Zhejiang University, Hangzhou, China, in 2006 and 2008, respectively, the M.S. degree in electrical engineering from University of Florida, Gainesville, FL, USA, in 2010, and the Ph.D. degree in electrical engineering from Texas Tech University, Lubbock, TX, USA, in 2013. He is currently a Senior RF Systems Engineer at MaxLinear Inc., Irvine, CA, USA. His research interests include broadband RF systems, RF system-onchip (SoC), wireless sensing technologies, and the biomedical applications of RF/microwave. Dr. Gu was the recipient of five Best Paper Awards as author/coauthor of IEEE RWW 2011, 2012, and 2013, and IEEE WAMICON 2011, and 2012. He was the recipient of IEEE Microwave Theory and Techniques Society (MTT-S) 2013 Graduate Fellowship for Medical Applications, 2013 Texas Tech Horn Professors Graduate Achievement Award, and 2012 Chinese Government Award for Outstanding Self-Financed Students Abroad.

Guochao Wang received the B.S. and M.S. degrees in electronics and information engineering from Northwestern Polytechnical University, Xi’an, Shaanxi, China, in 2007 and 2010, respectively. He is currently working towards the Ph.D. degree in electrical engineering with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA. His current research interests include microwave/RF wireless sensors, and microwave/millimeter-wave circuit and system design.

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Yiran Li (S’11) received the B.S. degree in electrical engineering from Southern Medical University, Guangzhou, China, in 2009, and the M.S. degree in electrical and computer engineering from Texas Tech University, Lubbock, TX, USA, in May, 2012, where she is currently pursuing a Ph.D. degree in electrical and computer engineering. Her research interests include RF circuit designs and applications, analog IC and RFIC designs.

Takao Inoue received the B.S. and M.S. degrees in electrical engineering from Oregon State University, Corvallis, OR, USA, and the Ph.D. in electrical engineering from the University of Texas at Austin, TX, USA, in 1996, 1998, and 2009, respectively. In 1998, he joined Motorola Inc. where he worked on ADSL modem development and ITU standardization for G.DMT. In 2004, he became a consultant to work on an FPGA based signal processing accelerator for wireless applications and became the co-founder and CTO of Fish Technologies Inc. specializing in advanced wireless system prototyping. Since 2010, he has been with National Instruments where he has been involved with wireless system prototype development, 3GPP standardization activities, and RF/microwave systems research. Dr. Inoue has served on the steering committee for IEEE Radio Frequency Integrated Circuits Symposium (2001 - 2013) and IEEE Radio and Wireless Symposium (2004-2014). He was the recipient of 2004 IEEE MTT-S Meritorious Service Award. His research interest includes signal processing and mathematics for wireless communications systems and related circuits and systems implementation.

Changzhi Li (S’06–M’09–SM’13) received the B.S. degree in electrical engineering from Zhejiang University, Hangzhou, China, in 2004, and the Ph.D. degree in electrical engineering from the University of Florida, Gainesville, FL, USA, in 2009. He worked at Alereon inc. and Coherent Logix inc. Austin, TX, USA, in the summers of 2007–2009, on ultrawideband (UWB) transceiver and software-defined radio. In August 2009, he joined Texas Tech University, Lubbock, TX, USA, as an Assistant Professor. His research interests include biomedical applications of microwave/RF, wireless sensor, and RF/analog circuits. Dr. Li received the NSF Faculty Early CAREER award in 2013, the Texas Tech Alumni Association New Faculty Award in 2012, and the IEEE MTT-S Graduate Fellowship Award in 2008. He was the finalist of the Vodafone Wireless Innovation Project competition in 2011. He received seven best conference/student paper awards as author/advisor in the IEEE Radio and Wireless Week (RWW) and the IEEE Wireless and Microwave Technology Conference (WAMICON). He served as the TPC co-chair for IEEE WAMICON 2012 and 2013.