WE4C-5
Proceedings of Asia-Pacific Microwave Conference 2010
Recent Advances in Doppler Radar Sensors for Pervasive Healthcare Monitoring Changzhi Li 1, Jenshan Lin 2 1
Department of Electrical and Computer Engineering, Texas Tech University Electrical and Computer Engineering, Box 43102, Lubbock, Texas, 79409, USA 1
[email protected] 2 Department of Electrical and Computer Engineering, University of Florida 559 Engineering Building, Gainesville, Florida, 32611, USA 2
[email protected] alarm goes off. With growing interests in health and life sciences in engineering community, many researchers have been contributing to the technology advancement in this field. This paper reviews the achievements reported in recent years, especially the last three years from 2008 to 2010 [18][56], and discusses several architectures with respect to monolithic integration. Most of the results referenced in this paper were published on IEEE journals and conference proceedings. Although many results were demonstrated using bench-top prototypes or board-level integration, their architectures still show the potential of being implemented on chip. In addition, there have been several reports of vital sign radar sensor chips using some of the architectures [21][33][52]. The paper starts with a comparison to UWB vital sign radar, which uses a fundamentally different principle for detection, in Section II. The paper then focuses on CW Doppler radar approach and discusses the various architectures of RF front-end in Section III and different baseband demodulation and signal processing methods in Section IV. A conclusion with discussion on future applications and challenges is given in Section V.
Abstract — This paper reviews recent advances in technologies using Doppler radar to detect heartbeat and respiration of a human subject. With contributions from many researchers in this field, new detection methods and system architectures have been proposed to improve the detection accuracy and robustness. The advantage of noncontact/covert detection has drawn interests on various applications. While many of the reported systems are bench-top prototypes for concept demonstration, several portable systems and integrated radar chips have been demonstrated. This paper reviews different architectures and discusses their potentials for integrated circuit implementation. Integrating the radar sensor on a chip allows the function of noncontact vital sign and vibration detection to be embedded in portable electronic equipment, like many other radio frequency (RF) devices. A radar sensor network is then feasible for pervasive monitoring in healthcare applications. Index Terms — Doppler radar, noncontact measurement, vital sign, heartbeat, respiration, cardiopulmonary, sensor, healthcare, vibration, integrated circuit.
I. INTRODUCTION Doppler radar has been widely used in a number of applications including vehicle speed measurement and storm tracking. The same principle, detecting the frequency or phase shift in a reflected radar signal, can be used to detect tiny body movements induced by breathing and heartbeat, without any sensor attached to the body. The noncontact remote detection of vital signs lead to several potential applications such as searching survivors after earthquake and monitoring sleeping infants or adults to detect abnormal breathing condition. While the concept of noncontact detection of vital signs has been successfully demonstrated by pioneers in this field before 2000 [1]-[4], research efforts in the first decade of this century have been moving the technology development toward lower power, lighter weight, smaller form factor, better accuracy, longer detection range, and more robust operation for portable and handheld applications. Among many possible applications this technology can be used for, the applications in healthcare seems to be drawing most of the interests [17]. As an example, a baby monitor using this technology was recently demonstrated [30]. The baby monitor integrates a low power Doppler radar to detect tiny baby movements induced by the breathing. If no movement is detected within 20 seconds, the
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II. CW DOPPLER RADAR VS. UWB PULSE RADAR There are two main categories of noncontact vital sign detection radar: continuous wave (CW) Doppler radar and ultra-wideband (UWB) pulse radar. A. CW Doppler Radar
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Fig. 1. Scenario of CW Doppler radar vital sign detection. The scenario of CW Doppler radar vital sign detection is shown in Fig. 1. An un-modulated signal T(t) = cos(2πft + Φ1)
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with carrier frequency f and residual phase Φ1 is transmitted toward a human body, where it is phase-modulated by the physiological movement x(t). The reflected signal captured by the radar receiver is represented as R(t) = cos[2πft 4πx(t)/λ + Φ2], where Φ2 is a constant phase shift due to nominal detection distance d0 and the phase noise. Using the same transmitted signal T(t) as the local oscillator (LO) signal, the radar receiver down-converts the received signal R(t) into baseband signal B(t) = cos[4πx(t)/λ + ΔΦ], where ΔΦ is determined by the nominal detection distance and oscillator phase noise. After analog-to-digital conversion (ADC), the information x(t) related to physiological movement (i.e. heartbeat and respiration) can be identified by proper digital signal processing (DSP). Fig. 2 shows an example of the baseband signal and spectrum detected using a CW Doppler radar integrated on a single CMOS chip. The radar chip has a homodyne quadrature architecture, and thus has two output channels (I/Q) as will be discussed in Section III-A.
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significantly alleviated by the range-correlation effect at short detection distance [9]. By using the same transmitted signal as the LO of the down-converter, the LO phase noise is effectively cancelled out, making high sensitivity vital sign detection radar possible. The choice of carrier frequency is very important. [15] has demonstrated that there exists an optimal carrier frequency for people with different physiological movement strength. Carrier frequencies ranging from hundreds of MHz [6] to millimeter wave frequency [31] has been tested for noncontact vital sign detection. In [31], a 228 GHz carrier was used for noncontact vital sign detection for three reasons. First, shorter wavelengths provide a greater sensitivity to small displacement. Second, this frequency is in an atmospheric window with at least 50% single-pass transmission [12]. Finally, higher frequency can maintain a collimated beam over much greater distances for reasonable aperture sizes, and the radar cross section of the vital sign area may also increase as frequency increases. This 228 GHz system has successfully extended the respiration and heart rate measurement to a range of 50 m. However, using very high carrier frequency makes it difficult to measure respiration and heartbeat together. The results in [31] only showed the detection of heartbeat while the subject was holding the breadth. Nevertheless, the encouraging result verified that long range detection is feasible by using higher carrier frequency.
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In a UWB pulse radar, the transmitter sends very short electromagnetic pulses toward the target. The typical pulse duration for vital sign detection is around 200~300 ps, and the pulse repetition frequency is in the range of 1~10 MHz. When the transmitted pulse reaches the chest wall, part of the energy is reflected and captured by the receiver. The nominal round-trip travelling time of the pulse is t = 2d/C, where d is the nominal detection distance and C is the speed of electromagnetic wave. If a local replica of the transmitted pulse with a delay close to the nominal round-trip travelling time is correlated with the received echo, the output correlation function will have the same frequency as the physiological movement. There are two ways to build such a UWB pulse radar. In one approach as shown in Fig. 3 (a) [23], the pulse generator is activated by the negative edge of a digital control signal. The delay generator provides a digitally programmable delay time of the aforementioned control signal in the range of 1-3 ns. The 5-bit programmable delay extends the flexibility and the ranging of the radar system. The integrator output is proportional to the correlation between the delayed pulse and the echo pulse. As a result, the signal at the output of the integrator is time-variant with the frequency of the physiological movement monitored, and thus it contains the information of heart beat and respiration.
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Fig. 2. Baseband I/Q channel signal (a) and spectrum (b) detected from the front of a human subject at 1.5m away. From [33]. Because the same transmitted signal is used as the LO to down-convert the received signal which is phase-modulated by the physiological movement, there is no frequency offset in the baseband. The timing delay does not affect the detection either. Therefore, no synchronization mechanism is required for the system. It should be noted that the radar block diagram in Fig. 1 is simplified. For the implementation of robust CW Doppler vital sign detection radar, building blocks such as low noise amplifier (LNA), baseband amplifier, and filter are needed. Different front-end architectures can be used for the radar, as will be discussed in Section III. In radar applications, the phase noise of the LO can mix with the received echo signal and disrupt the desired baseband signal if the phase noise of the two signals entering the mixer is not correlated. Mathematically, it means the ΔΦ term in the baseband signal B(t) may disrupt accurate detection of x(t) due to phase noise. This challenge is
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In the other approach as shown in Fig. 3 (b) [25], the pulse generator forms two short UWB pulses in every pulse repetition period. The first short pulse from the generator enters the transmit chain then is transmitted toward the target through the antenna. The second short pulse from the generator is directed into a reference channel of the phase detector. When the transmitted pulse is reflected by the target, it is captured by the receiving antenna, amplified, and then fed into the other channel of the phase detector. As a result, the phase detector plays the role of correlation and generates in-phase and quadrature (I/Q) outputs containing vital sign information.
focus on recent development of CW radar for noncontact motion detection in healthcare applications. III. RF FRONT-END ARCHITECTURES Various RF front-end architectures for noncontact motion detection have been reported. In addition to previously used homodyne, heterodyne, and double-sideband architectures, direct intermediate frequency (IF) sampling and self-injection locking were recently reported. A. Homodyne It is known that a single-channel direct conversion Doppler radar has a null detection point problem [9], which means the detected signal is very weak at certain detection distances. To eliminate this problem, quadrature radar architecture can be used. A block diagram and a chip microphotograph of homodyne motion detector are shown in Fig. 4. It has been implemented and demonstrated in [9] that since there are in-phase and quadrature (I/Q) baseband outputs, there is always one channel not at the null detection point, thus eliminating the null detection point problem. Moreover, since the vital sign signal has low bandwidth, the two output channels can be combined in software to perform complex signal demodulation [18] or arctangent demodulation [16] as low-cost baseband solutions, which will be discussed in Section III.
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Fig. 3. Block diagram of UWB vital sign detection radar of: (a) delay cell, multiplier, and integrator solution [23]; (b) phase detector and double-pulse-generator solution [25].
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By controlling the delay between the two inputs of the correlation function block, the detection range of UWB radar can be changed since the delay corresponds to the signal round-trip travelling time. This mechanism makes it possible for UWB radar to eliminate interference caused by reflection from other objects (clutter) and multi-path reflection. This is the main advantage of UWB vital sign detection radar. However, the disadvantage of UWB radar is, the delay need to be calibrated once the detection distance changes, thus increasing the system complexity and cost. Also, since the correlation function may be nonlinear, it may be nontrivial for UWB radar to recover the original movement pattern even though frequency information can be easily obtained. CW radar has the advantages of low power consumption and simple radio architecture. Moreover, CW radar can also cancel out clutter noise by proper adjustment of the radio front-end architecture [37][53]. Also, MIMO/SIMO [11][14][20] technologies can be easily implemented with CW radar for the detection of multiple targets and multiple movements. Therefore, the remaining part of this paper will
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Fig. 4. (a) Simplified block diagram of homodyne motion detector; (b) chip microphotograph of a homodyne motion detector fabricated in 0.13 µm CMOS process. B. Heterodyne Heterodyne radio architecture [4][6] has been the only solution for noncontact vital sign detection until the homodyne noncontact vital sign detector was first
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demonstrated at the beginning of this century [7][8]. Compared with the homodyne architecture, the heterodyne architecture has the advantage of robust against DC offset. However, single-channel heterodyne radar has the null detection point problem. In order to overcome the null detection point problem, quadrature architecture or frequency-tuning on a single channel has to be used. In 2005, a double-sideband transmission heterodyne architecture was proposed to eliminate the need for generating quadrature LO and several filtering requirements in traditional heterodyne architecture [10].
The double-sideband transmission method with indirectconversion architecture eliminates the need for the imagereject filter and IF filter, thus can be monolithically integrated. A double-sideband vital sign detector integrated in 0.18 µm CMOS process has been demonstrated in [21]. The doublesideband transmission method also eliminates the need of generating quadrature LO signals. D. Direct IF Sampling For the conventional quadrature direct-conversion architecture, I/Q imbalance and DC offset are two inevitable challenges that degrade the demodulation accuracy and the output signal-to-noise ratio (SNR). To ensure I/Q amplitude and phase balance, complicated calibration procedures such as the Gram-Schmidt procedure have to be applied [5]. By using a digital I/Q demodulation technique, the two output channels are in perfect quadrature phase relationship, making calibration procedures unnecessary. To overcome the DC offset problem, subsystems such as the DC offset compensation unit [16] are needed. However, these methods add to the system complexity and increase the hardware cost. If configured with a heterodyne architecture, there will be no significant amount of DC offset. Therefore, direct IF sampling with heterodyne architecture has been proposed for noncontact vital sign detection [26]. Fig. 6 shows the block diagram of direct IF sampling architecture. In the receiver chain, the mixer after LNA down-converts the received signal into an intermediate frequency. Then high speed ADC is used to convert the analog IF signal to digital. After that, quadrature demodulation is performed using a high speed DSP.
C. Double-Sideband Fig. 5 shows a simplified block diagram and a chip microphotograph of the double-sideband radar architecture. In a double-sideband radar [13], both upper and lower sidebands are transmitted by mixing signals from two VCOs. In the receiver, the two sidebands are automatically combined by two-stage down-conversion. The signal detected by either the upper or the lower sideband has null detection and optimal detection points separated by 1/8 of the signal wavelength. When the two sidebands are combined together, the distance between optimal and null detection points is changed to λIF/16 [13], where λIF is the wavelength at the IF and thus resulting in a much longer separation. Moreover, when the IF is tuned, the location of optimal/null detection points can be changed [13]. Therefore the null detection point problem can be eliminated by IF tuning. (a) Power Splitter LO1
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Fig. 6. Direct IF sampling architecture. Extensive experimental verifications have been carried out in [56] to demonstrate the direct IF sampling architecture’s advantages in alleviating I/Q imbalance and eliminating the complicated DC offset calibration. Since the information bandwidth of vital sign signals is very small, IF can be chosen at a low frequency such that the demand on ADC sampling speed can be relaxed. Therefore, this architecture is suitable for integrated circuit implementation.
Fig. 5. (a) Block diagram of the double-sideband radar architecture; (b) chip microphotograph of a double-sideband radar detector fabricated in 0.18 µm CMOS process.
E. Self-Injection Locking Unlike all previous architectures that were adopted from RF front-end architectures used in wireless communications, a new approach of using self-injection locking to detect vital
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signs was proposed in 2010 [54]. The architecture is based on an injection-locked oscillator that was previously used for spectrum sensing [35]. By transmitting the oscillator signal to a human subject through an antenna and using the reflected signal as the injection signal to the oscillator itself, the vital sign movement modulating on the frequency (or phase) of the injection signal due to Doppler effect can be demodulated through this self-injection locking mechanism [54]. In this architecture (Fig. 7), the spectrum sensing and vital sign sensing can be performed concurrently as long as the radar round-trip time delay is much smaller than the oscillator frequency scan time. The analysis of modulation and noise transfer functions showed that this self-injection locking method has higher signal gain at low frequency modulation and better noise attenuation with increased delay time (longer distance), which is desirable for vital sign detection. Its measurement result demonstrated successful detection of an adult subject's vital signs at 50 cm away, with about 0 dBm of VCO output power. Even though the demonstrated system was not an integrated circuit solution, the proposed architecture can potentially be monolithically integrated.
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Another way to eliminate the optimum/null detection point problem in the quadrature demodulation system is to use arctangent demodulation [16] by calculating the total Doppler phase shift as Ψ = arctang(Q/I). Since Ψ is directly proportional to the movement x(t), desired motion information can be recovered reliably. However, DC offset calibration is required before performing the arctangent demodulation. FFT
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B. Noise Cancellation and Multiple Subject Detection It was found in experiments that other major problems of the Doppler non-contact motion detection include the noise caused by other motion artifacts and the presence of multiple subjects. The multiple-input, multiple-output (MIMO) technique was then proposed to solve the problems. Another proposed approach is the single-input, multiple-output (SIMO) technique. In [11] and [14], the single and multiple antenna systems and SIMO/MIMO signal processing were explored to isolate desired radar return signals from multiple subjects. A generalized likelihood ratio test (GLRT), based on a model of the heartbeat, has been developed to show that this technique can be used to distinguish between the presence of 2, 1, or 0 subjects, even with a single antenna. Furthermore, this technique was extended to detect up to 2N1 subjects using N antennas. In the meantime, multiple transceivers have been used to cancel the noise caused by motion artifacts such as random body motion. It has been demonstrated that based on the different movement patterns of physiological movement and random body movement, it is possible to cancel out the noise caused by random body movement using two transceivers detecting from two sides of the human body [27]. Another solution to improve the performance against random body motion is the differential front-end Doppler radar operating at two different frequencies. By using dual helical antennas each with a 40-degree beamwidth, it is possible to illuminate the body in two adjacent locations to perform a differential measurement. Since only one of the beams illuminates the heart, the baseband signal from the second radar is used for motion cancellation. A dual helical
Fig. 7. Block diagram of the concurrent spectrum and vital sign sensing architecture. From [54]. IV. BASEBAND SIGNAL PROCESSING METHODS Various methods have been used to process the baseband signals for noncontact motion detection. They are implemented in analog or digital domain, or a combination of both. A. Demodulation Complex signal demodulation and arctangent demodulation are two popular demodulation methods for noncontact motion detection. Their simplified block diagrams are presented in Fig. 8. The complex signal demodulation can eliminate the optimum/null detection point problem by combining the I and Q signals in baseband to form a complex signal [18]. Since the amplitude of the complex signal is not affected by the residual phase, desired signal components can be reliably identified from its spectrum.
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antenna and simple direct-conversion radar were implemented to demonstrate this approach [34]. Similar approaches such as two-frequency radar sensor [19] and multifrequency interferometric radar [32] have also been reported recently. Combined with advanced signal processing, these technologies can improve the performance of noncontact vital sign detection.
movements were demonstrated, more sophisticated signal processing methods are still needed to make it more robust to measure a subject's vital signs while in motion, e.g., walking or running. Even without physical body movement artifacts, the accurate measurement of respiration rate and heartbeat rate can be complicated when abnormal cardiopulmonary activities happen. The combination of tachypnea (rapid breathing) and bradycardia (slow heartbeat) that might possibly happen to newborn infants is an example [39]. In this case the rates might be mistakenly measured. Advanced signal processing and hardware will be needed to accurately diagnose this syndrome.
C. Spectral Estimation Another challenge encountered in vital sign detection is the presence of undesired harmonic terms and intermodulations other than the sinusoids of interest. For example, the thirdand forth- order harmonics of respiration signal are very close to the frequency of heartbeat, complicating the output spectrum [15]. A spectral estimation algorithm is needed to accurately estimate the sinusoidal frequencies before identifying the heartbeat and respiration rates. Sometimes the conventional periodogram obtained from Fourier transform cannot reliably separate the rich sinusoidal components since it suffers from smearing and leakage problems, particularly for the case of limited data samples. A parametric and cyclic optimization approach, referred to as the RELAX algorithm, was suggested to mitigate these difficulties. The superiority of using RELAX algorithm for accurate noncontact vital sign detection has been demonstrated from both simulation and experiment in [49].
ACKNOWLEDGEMENT The authors would like to thank many researchers who found this subject interesting and contributed to the advances in the technology development. The efforts hopefully will bring the technology to wider use and benefit humanity in the future. REFERENCES (IN CHRONOLOGICAL ORDER) [1] J. C. Lin, "Noninvasive Microwave Measurement of Respiration," Proceedings of the IEEE, vol. 63, no. 10, p. 1530, Oct. 1975. [2] K. M. Chen, D. Misra, H. Wang, H. R. Chuang, and E. Postow, "An X-band microwave life-detection system," IEEE Trans. Biomedical Engineering, vol. 33, pp. 697-702, July 1986. [3] H. R. Chuang, Y. F. Chen, and K. M. Chen, "Automatic cluttercanceller for microwave life-detection system," IEEE Trans. Instrumentation and Measurement, vol. 40, no. 4, pp. 747-750, August 1991. [4] J. C. Lin, “Microwave sensing of physiological movement and volume change: A review,” Bioelectromagnetics, vol. 13, pp. 557-565, 1992. [5] R. Moraes and D. H. Evans, “Compensation for phase and amplitude imbalance in quadrature Doppler signals,” Ultrasound Med. Biol., vol. 22, pp. 129–137, 1996. [6] K. M. Chen, Y. Huang, J. Zhang, and A. Norman, “Microwave life-detection systems for searching human subjects under earthquake rubble and behind barrier,” IEEE Trans. Biomed. Eng., vol. 27, pp. 105-114, Jan. 2000. [7] A. D. Droitcour, O. Boric-Lubecke, V. Lubecke, and J. Lin, “A microwave radio for Doppler radar sensing of vital signs,” in IEEE MTT-S Int. Microwave Symp. Dig., vol. 1, pp. 175–178, 2001. [8] A. D. Droitcour, O. Boric-Lubecke, V. Lubecke, J. Lin, and G. T. A. Kovacs, “0.25 μm CMOS and BiCMOS single-chip direct-conversion Doppler radars for remote sensing of vital signs,” in Int. Solid-State Circuits Conf. Dig., vol. 1, San Francisco, CA, pp. 348, 2002. [9] 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, pp. 838-848, March 2004. [10] Y. Xiao, J. Lin, O. Boric-Lubecke, V. Lubecke, “A Ka-Band Low Power Doppler Radar System for Remote Detection of
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