IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 3, MARCH 2014

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Noncontact Accurate Measurement of Cardiopulmonary Activity Using a Compact Quadrature Doppler Radar Sensor Wei Hu∗ , Student Member, IEEE, Zhangyan Zhao, Yunfeng Wang, Haiying Zhang, and Fujiang Lin, Senior Member, IEEE

Abstract—The designed sensor enables accurate reconstruction of chest-wall movement caused by cardiopulmonary activities, and the algorithm enables estimation of respiration, heartbeat rate, and some indicators of heart rate variability (HRV). In particular, quadrature receiver and arctangent demodulation with calibration are introduced for high linearity representation of chest displacement; 24-bit ADCs with oversampling are adopted for radar baseband acquisition to achieve a high signal resolution; continuous-wavelet filter and ensemble empirical mode decomposition (EEMD) based algorithm are applied for cardio/pulmonary signal recovery and separation so that accurate beat-to-beat interval can be acquired in time domain for HRV analysis. In addition, the wireless sensor is realized and integrated on a printed circuit board compactly. The developed sensor system is successfully tested on both simulated target and human subjects. In simulated target experiments, the baseband signal-to-noise ratio (SNR) is 73.27 dB, high enough for heartbeat detection. The demodulated signal has 0.35% mean squared error, indicating high demodulation linearity. In human subject experiments, the relative error of extracted beat-to-beat intervals ranges from 2.53% to 4.83% compared with electrocardiography (ECG) R-R peak intervals. The sensor provides an accurate analysis for heart rate with the accuracy of 100% for p = 2% and higher than 97% for p = 1%. Index Terms—Arctangent demodulation, cardiopulmonary, Doppler radar, heart rate variability (HRV), noncontact.

I. INTRODUCTION HE ability of remotely sensing vital signs such as heartbeat and respiration is particularly useful in situations where contact electrodes are impractical or inconvenient. Compared with traditional ways such as electrocardiography (ECG) or photoplethysmography (PPG), noncontact detection technology

T

Manuscript received August 29, 2013; revised October 19, 2013; accepted October 23, 2013. Date of publication November 4, 2013; date of current version February 14, 2014. Asterisk indicates corresponding author. ∗ W. Hu is with the Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026, China (e-mail: huwei@ mail.ustc.edu.cn). Z. Zhao, Y. Wang, and H. Zhang are with the Radio Frequency Integrated Circuit Department, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China (e-mail: [email protected]; wangyunfeng@ ime.ac.cn; [email protected]). F. Lin is with the Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026, China (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/TBME.2013.2288319

has advantages for using in sleep apnea monitoring, sudden infant death syndrom (SIDS) monitoring, fatigue monitoring, inhospital monitoring, and home healthcare. Previous efforts with related theoretical explains have demonstrated that microwave Doppler radar has potential to provide a practical tool for noncontact detecting cardiopulmonary activity [1]–[4]. Doppler radar sensor provides a remote sensing operating mode that is demanded in many occasions [2], [3]. For healthcare applications, the radar sensor systems are designed to work without interfering the user’s daily routine; freeing the users from the inconveniences of being attached to electrode makes it very user-friendly. Compared with other noncontact sensing technology such as ultrasound or infrared, the microwave Doppler radar aims to provide three main advantages: being able to penetrate clothing, bedding, or other nonmetallic obstacles; not sensitive to environment factors such as light and temperature; and having the potential to be integrated using low-cost silicon CMOS technology. As one family of sensor systems, continuous-wave (CW) Doppler radar is widely used in research lately. CW radar is simply in structure and can be considered as a phase detector. Quadrature CW radar with I/Q channel demodulation has been proposed to alleviate the null point problem occurring in direct-conversion single-channel receiver by Droitcour et al. [4]; compact sensor system with integrated CW radar transceiver is described in [5] and [6]. Li and Lin [7] have proposed a method to eliminate the false alarm caused by random body movement using CW radar sensors; to achieve robust performance, various advanced digital signal processing methods are studied for cardiopulmonary information extraction [8]–[10]. Recently, much interest has been paid to the topics on how to achieve high detection accuracy so that cardiopulmonary activities including not only heart and respiration rate but also heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) indicators can be extracted. Results presented by Massagram et al. [11] have shown that using a quadrature Doppler radar system with a linear demodulation method provides fairly accurate analysis for HRV and RSA. In special application, highly directive antenna or antenna array approach is induced to increase selectivity and decrease sensitivity to clutter [12]. However, in real applications, antennas with small size but relatively low gain such as printed circuit board (PCB) antennas is preferred, and in this case, heartbeat signal power at baseband is relatively small, easy to be affected by noise, clutter, intermodulation interference, and respiration harmonics. In

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Fig. 1.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 3, MARCH 2014

Doppler radar front-end block diagram.

addition, some of the potential problems exist when developing the technology into real applications. Main challenges can be summarized as follows for accurate cardiopulmonary sensing in particular: 1) Reconstruction of chest wall movement with high linearity and accuracy. It is the source information for physiology activities analysis; 2) Recovery and separation of the heartbeat and respiration signal and removal of the interference signal caused by body motion and background while the signal processing algorithm being robust for measuring abnormal physiological activities; 3) Extraction of cardiopulmonary information useful in healthcare or other applications from a human subject, such as variation of the HR, although the heartbeat waveform from radar is not as sharp as ECG or PPG. The intent of this paper is to study these problems and introduce specialized hardware and signal processing techniques to ameliorate their disruptive effects. II. QUADRATURE DOPPLER RADAR SENSOR SYSTEM Doppler radar sensing technology is based on the fact that a portion of the transmitted microwave signal backscattered off the subject is phase-modulated by the torso movement due to the Doppler effect. It is the subject’s body surface reflections that dominate the radar measurements of cardiopulmonary activities [13]. Then, the information can be extracted by processing the radar echo signal. A. Doppler Radar Front End In CW Doppler radar with homodyne receiver, the same oscillator is used to provide both the receiver’s local oscillator and the transmitted signal. A quadrature receiver configuration was adopted for Doppler radar front-end in this paper. I/Q channel demodulation with calibration method was proposed to alleviate the null point problem and acquire an accurate phase demodulation result with high linearity. Fig. 1 shows the block diagram of radar front end with signal flow. The target, at a distance of d0 from the antenna, has a periodic displacement x(t), which is mainly caused by respiration and heart activity. The transmitted signal T (t) travels a total distance of 2d(t) = 2(x(t)+d0 ) and becomes the received signal R(t). The receiver captures and down-converts the signal

by comparing the phase of the reflected wave with the LO(t). In quadrature receiver configuration, two orthonormal components at the baseband are obtained, namely, BI (t) and BQ (t). The baseband signal is proportional to the cosine or sine of a constant phase shift θ, which is determined by hardware and d0 , summed with a time-varying phase shift proportional to the time-varying chest motion x(t), and with the residual phase noise Δψ(t). The two receiver output channels will be   4πx(t) + Δψ(t) BI (t) = AB cos θ + λ   4πx(t) BQ (t) = AB sin θ + + Δψ(t) . (1) λ When only one-channel signal is chosen for demodulation, the sensitivity will vary with the target’s distance because θ changes. For example, if θ is an integer multiple of π, the I signal will be in null point where the demodulation sensitivity is low, and the Q signal will be at an optimum phase demodulation point. Quadrature receiver together with arctangent demodulation can solve the null point problem. The digital signal processing begins with two-channel combination; the baseband outputs of I/Q channels together provide the demodulated phase, which is proportional to the target movement x(t). For x(t), in theory, the maximum chest-wall displacement due to heartbeat is about 0.5 mm [14], and the displacement caused by respiration ranges from 4 to 12 mm [15]. When there is a case that the maximal heartbeat motion be detected, the chest wall motion will cause 56◦ –167◦ phase shift for Doppler radar operating in 5.8 GHz. Then, the cardiopulmonary information can be identified by processing the demodulated signal. This is the basis for cardiopulmonary sensing. However, additional challenges are introduced when the subject is not at rest or the antenna is not facing the location of and in the direction of maximum chest motion. In addition, the maximal heartbeat motion occurs over a very small area on the body surface, and the area in motion varies with individual physiology, shape, and orientation with respect to the antenna. So the proportion of the contribution of heartbeat signal in x(t) changes largely with range, direction, target position etc. The radar sensor system should have a high baseband signal-to-noise ratio (SNR) in order to ensure that the heartbeat signal won’t be submerged by noise. In this paper, a compact Doppler radar sensor with quadrature receiver is designed and fabricated on a PCB. Beside two 7 dBi patch antennas, the radar sensor used the following commercially available components: one LNA (Hittite HMC717), one MMIC I/Q mixer (Hittite HMC620), one power splitter (MiniCircuits SCN-2–65+), and one oscillator (Hittite HMC358). It operated at 5.8 GHz with 7.5 dBm power level at the transmitter antenna. The receiver has a relatively low gain, about 5 dB, and the output of radar front end are two-channel IF signals. B. Analog Signal Processing The analog signal processing (ASP) block that follows the radar front end consists of several filters, amplifiers, and ADCs. In real applications, respiration signal is much larger than

HU et al.: NONCONTACT ACCURATE MEASUREMENT OF CARDIOPULMONARY ACTIVITY

heartbeat, and the dc offset at baseband may be variable and greater than the respiration signal. So both large dynamic range and high resolution are required at the same time in order to acquire useful information. The signal at baseband tends to have a high dc level and a low ac level. ASP should have a low gain to prevent overload, and thus, it would be interesting to explore the use of high-resolution ADC so that the baseband signal can be digitized with enough resolution. This will help to eliminate the need for dc blocking circuit or high-pass filters, which require use of large capacitors and may cause amplitude or phase imbalance. The high-resolution ADC would also enable the subtraction of dc offsets and I/Q imbalance calibration in digital domain so that the information near dc is preserved; the arctangent combining technique would not result in the errors seen when using high-pass filters, thus resulting in more accurate phase demodulation. In this paper, each channel is filtered, amplified, and digitally acquired independently. The ASP block has a total 20 dB gain to improve radar range. Only low-pass filter is used to each channel for antialiasing. After filters and amplifiers, two channels of 24-bit ADCs are adopted. In addition, the ADCs are configured at sampling rate of 12.8 kHz and then the signals are down-sampled to 50 Hz; the oversampling technique is applied for 256 times in order to get 4-bit ENOB improvement. The components used include amplifiers (Analog Devices AD8221, AD8628) and 24-bit ADC (Texas Instrument ADS1251). C. Quadrature Signal Demodulation Demodulation sensitivity variation with the target’s distance can be solved by using a quadrature receiver and arctangent demodulation since it overcomes target position limitation and small angle limitation [4]. However, challenges inherent in the homodyne quadrature architecture—phase imbalance, amplitude imbalance, and dc offset—impede the arctangent technique to directly demodulate the phase. For quadrature channel imbalance, the amplitude error AE is defined as the ratio of the amplitude of the Q RF signal to that of the I RF signal. The phase error φE is defined as the difference between the phases of the two LO signals minus π/2. DC offsets in the quadrature channels act as a linear transform on I and Q components, refer to VI and VQ . With these nonidealities, the baseband signals become BI (t) = VI + AR cos(θ(t)) BQ (t) = VQ + AR AE sin(θ(t) + φE ).

(2)

If the channel imbalance factors are known, the Gram– Schmidt procedure can be used to correct imbalance errors [2]. DC offset of each channel is caused by imperfections in circuit components and reflections from stationary objects, as well as the dc information associated with target’s position required for accurate demodulation [16]. Since the dc information should be preserved, high-pass filter or dc blocking cannot be used. And it should be noted that once the distance between target and radar or environment changed, the dc offset will also change, thus requiring calibration again. As for imbalance errors, they are mainly caused by radar hardware, and since the baseband

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Fig. 2. Complex plot of I channel, Q channel, and their arctangent demodulation before and after calibration.

signals are nonstationary and random, special method is also required to estimate the imbalance errors. In this paper, these factors are calibrated in two steps: The first step is hardware calibration. I/Q imbalance errors and dc offset caused by imperfections in circuit components are calculated from special measurement and are removed. It is executed only once. When the baseband I and Q outputs are plotted on I/Q-plane, their imperfect factors can be clearly seen. Assuming that there is only one source of moving target and the target’s motion is much bigger than half wavelength, that is, 2.58 cm at 5.8 GHz (a half-wavelength change in range can cause 2π phase change), the complex plot of I and Q channel forms an ellipse. The center offset of the ellipse indicates the two channels’ dc offsets, and the rotation and length difference between the two axes indicates the amplitude error AE and phase error φE . For real radar system, take the impact of baseband signal amplitude into consideration, AR is inversely proportional to d2 (t) based on radar equation. So the complex plot of I/Q will form spiral-like curves. The curves have different radii that indicate the signal amplitude AR but same center point. I/Q plot of an example signal with these nonideal factors are shown in Fig. 2(a); the simulation used the example signal with 20 cm displacement, so there were 20/2.58 ≈ 7.75 cycles; their arctangent demodulated output is shown in Fig. 2(b). The center offset of the ellipse is easy to find; then in order to calculate AE , pick up one ellipse curve, seen as a Lissajous Figure, as shown in Fig. 2(a), and find the width A and height B; thus, AE equals B/A. C is the distance between two joints that is decided by the centerline parallel to I-axis and the ellipse, the φE can be calculated as arcsine (C/A). Although the curve is not actually closed, the VI , VQ , AE and φE are calculated with error less than 0.8% in simulation. After this procedure, we could bring back the center offset to the origin by substrate the

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VI and VQ from BI and BQ and then use the Gram–Schmidt technique [17] to estimate the amplitude and phase errors. The orthonormal basis vectors BI ,orth , BQ ,orth will be      1 0 BI ,orth BI − VI = . (3) 1 − tan(φE ) A E cos(φ BQ ,orth BQ − VQ E ) The I/Q plot of calculated baseband signal and its arctangent demodulated output are shown in Fig. 2(c) and (d); the demodulated signal shows a good linearity. In real calibration, for collecting the data, the target (human subject or a metal plane instead) in front of radar antenna should move within a reasonable range and create a long enough displacement such as 20 cm. The second step is dynamic dc offset compensation. Center estimate algorithm is applied to compensation the dc offset caused by the often uncontrolled changes of environment in which the measurements are performed. Although the dc offset maybe small for most of time, the compensation will help to enhance the demodulation linearity and make the radar system suitable for moving target. For 5.8 GHz radar system, 4–12 mm displacement caused by respiration will produce 56◦ –167◦ phase shift. So the arc length caused by one cycle of respiration is enough for center estimating. Thus, we choose to use ten seconds of baseband data for processing. In the center estimate algorithm, we approximate scattered points by a circle with radius R, calculate the minimum variance of R, which indicates the circle center points. The objective function we use is variance of R. Given n points (xi , yi ), 1 ≤ i ≤ n, the objective function is  n 2 n 1  1 2 R − 2 Ri (4) F = n i=1 i n i=1 where Ri is the distance from the point (xi , yi ) to the center (a, b). Find the minimum of F iteratively; when the acceptable accuracy is obtained, the center point (a, b) is calculated from the minimum of F . After that the center is brought back to the origin and then quadrature signals forming the arc centered at origin in complex plot are combined by using arctangent demodulation. The target displacement can be recovered by simply unwrapping the demodulated phase signal    BQ ,orth − b λ × unwrap arctan . (5) x(t) = 4π BI ,orth − a D. Cardiopulmonary Information Extraction The digital signal processing method we used for cardiopulmonary sensing is based on wavelet filter and ensemble empirical mode decomposition (EEMD). The flowchart of the algorithm is shown in Fig. 3. Utilizing arctangent demodulation, I/Q channels can be converted to the net spatial displacement of the object detected. The signal we acquired is the target’s chest movement combined with other unwanted clutter. To find the useful information such as heartbeat and respiration, we started from signal decomposing and denoising. Classic filters allow extracting the heartbeat rate by looking at the frequency components of the signal. However, they do not allow the extraction of the HRV. As the heartbeat

Fig. 3.

Digital signal processing method for cardiopulmonary sensing.

signal is nonstationary and has relatively sharp peaks, wavelet transform (WT) is an ideal choice for its analysis. Wavelets provide excellent time resolution for rapid events such as heartbeats and good frequency resolution for slower events such as respiration. The continuous WT (CWT) is employed in this paper that can be viewed as processing the demodulated signal using a bank of filters applied successively. Daubechies wavelet was chosen as the mother wavelet because of its accuracy after thorough experimentation results. The application of the wavelet filter decomposes the demodulated signal into a series of components, and for each decomposition levels, the coefficients can be either set to zero or reduced in magnitude, such that a particular feature of the signal is affected upon reconstruction. The high-frequency (HF) components are used for heartbeat signal recovery and the low-frequency (LF) components for respiration recovery. A moving average filter is then applied to each signal, thus the heartbeat and respiration signal are reconstructed. However, wavelets alone cannot always find every heartbeat with clearly seen peaks, so peak detecting method cannot provide a fairly accurate analysis for HR. Recently, the empirical mode decomposition (EMD) [18] has been used to process nonstationary signals and is applied in many related fields. The EEMD algorithm is proposed as a noise-assisted data analysis method for solving the EMD-mode mixing and other problems [19]. The EEMD algorithm provides an exact reconstruction of the original signal and a better spectral separation of the modes. Therefore, in this paper, EEMD is applied for further analysis of the heartbeat signal after wavelet filter. If x[n] is the targeted data, we can describe the EEMD method by the following algorithm: 1) Add a white noise series to x[n]. 2) Fully decompose the data with added white noise into a small number of intrinsic mode functions (IMFs) using EMD.

HU et al.: NONCONTACT ACCURATE MEASUREMENT OF CARDIOPULMONARY ACTIVITY

3) Repeat steps 1) and 2) with several realizations of Gaussian white noise added to the original signal. 4) Obtain the means of corresponding IMFs of the decompositions as the final result. Then, we choose to use certain IMFs, combining them to form a more accurate heartbeat signal. After cardiopulmonary signal extraction, we use zerocrossing detectors to acquire the beat-to-beat intervals of heartbeat and respiration signals. Time-domain analysis can be done after this step. HRV is a useful indicator to quantitatively measure physiological changes during treatment. HRV assessment stands out as one of the most promising methods for the future of general health evaluation [11]. ECG is traditionally considered the standard way to measure the beat-to-beat intervals that are the source information for HRV analysis. In this paper, HRV analysis of heartbeat signal extracted from Doppler radar is done and the results are compared to the HRV indices calculated from ECG. The beat-to-beat intervals and their Bland and Altman analysis are observed in the experiment, and two time-domain HRV indices, the standard deviation of normal-to-normal intervals (SDNN), the root-mean-square successive difference of intervals (RMSSD) are calculated. The interval between beats is denoted as δ(n) for n ∈{1, . . ., N }, where N is the total number of intervals and δ is the average beat-to-beat interval and then the SDNN and RMSSD are determined as

N

1 

2 SDNN =  δ(n) − δ (6) N n =1



RMSSD = 

N 1  [δ(n) − δ(n − 1)]2 . N − 1 n =2

(7)

The power spectra of HRV are calculated as described by Wong et al. [20]. From spectral analysis, two frequency bands were considered: LF band and HF band. The two band spectral power, LFP and HFP, are calculated by using the frequency ranges of 0.045–0.15 and 0.15–0.40 Hz, respectively. The LF and HF oscillatory components are presented in absolute (milliseconds square, ms2 ) units. Finally, the heartbeat and respiration rate can be calculated by convert the beat-to-beat intervals to instantaneous rate, and the heart rate is obtained by averaging the instantaneous rate over 15 s so that the output result is smoothed. E. Doppler Radar Sensor System Design and Experiment Device The schematic of the radar sensor system and the measurement setup are shown in Fig. 4. The size of the radar module with patch antenna facing outward is 3.3 cm × 7 cm. The ASP circuit, 24-bit ADC, Blue Tooth module (CSR HC05) and the dc–dc converter circuit are integrated in a single PCB board with the size of 6 cm × 7 cm. The two PCBs as the radar sensor has compact size that is suitable for handheld applications. The Bluetooth module was used to transmit the two-channel baseband signals to computer wire-

Fig. 4.

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Schematic of radar sensor system and the experiment setup. TABLE I POWER CONSUMPTION FOR EACH BUILDING BLOCK

lessly so that the radar sensor is more convenience to use for experiment. The software to collect and process the data was written in MATLAB. For the signal processing validation part, a simulated target with a metal plate (mimics human skin) facing the radar system is proposed for representing the cardiopulmonary activity. The simulated target can be programmed by a computer to move with a certain model such as the waveform shown in the right side of Fig. 4. For power supply circuit, components used include dc–dc converter 12–6 V (Texas Instrument TPS5430) and LDO 6–5 and 6–3 V (Analog Devices ADP3339). The total current of the compact sensor system is 126 mA with the supply voltage 12 V. The power consumption for each building block is listed in Table I. As it can be seen, the power was mainly consumed by the 5.8 GHz radar front end. III. EXPERIMENTAL RESULTS Numerical experiments are performed to demonstrate the advantages of signal processing techniques used in the paper for noncontact cardiopulmonary sensing. Demodulation linearity is first measured by using the simulated target. Then, human cardiopulmonary sensing results were compared with the reference signals: ECG and respiration signals from piezoelectric sensors on upper torso. A. SNR and Demodulation Linearity Before we apply our signal processing method to real cardiopulmonary signal analysis, we first analyze the performance of the designed quadrature radar sensor system and I/Q demodulation linearity by using the simulated target. The simulated target is first programmed to move with a period of 1 Hz and amplitude of 4 mm. When it was 50 cm away from the radar sensor, the power spectrum for one channel is shown in Fig. 5(a).

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Fig. 5. (a) Baseband signal spectra with or without a 1 Hz period, 4 mm amplitude target at 50 cm distance. The noise floor obtained by averaging the noise power over frequencies higher than 10 Hz measured without target motion is − 78 dBmV and the SNR is 73.27 dB. (b) Relationship between distance and the radar baseband SNR.

Fig. 6. Relationship between displacement of target and baseband SNR measured at the distance of 1 m using 1 Hz sine wave.

The noise floor obtained by averaging the noise power over frequencies higher than 10 Hz is − 78 dBmV. The SNR of the signal that is defined as the useful signal power at 1 Hz (22.99 dB, as the solid line shows) over average noise power measured without target (− 50.28 dB, as the dotted line shows) is 73.27 dB. Using the same method, the baseband SNR varies with distance can be obtained. As it is shown in Fig. 5(b), the SNR is unstable at the distance less than 20 cm for antenna near-field effect. The SNR is above 20 dB with the distance less than 6 m, and above 50 dB with the distance within 2 m. Another experiment also configured the simulated target in 1 Hz sine wave, but at a distance of 1 m and with different amplitudes varying from 5.4 mm to 5.4 μm (with a down step of 3 dB). Using the same method, the baseband SNRs were calculated. Fig. 6 shows the relationship between displacement and the SNR. As it can be seen, for radar front end operating at the distance of 1 m, the minimum detectable displacement is about 0.01 mm with the SNR above zero. For further analysis, the simulated target is programmed using the heartbeat and respiration waveform model derived from real people. The heartbeat movement is set with 1.25 Hz period and 0.8 mm amplitude, and respiration movement is set with 0.25 Hz period and 6 mm amplitude. The simulated target at the distance

Fig. 7. Radar baseband signal and demodulation: (a) simulated target movement. (b) I and Q channel signal after dc offset compensation and I/Q imbalance errors calibration. (c) Complex plot of original and calibrated I/Q channel signal. (d) Arctangent demodulated result.

of 50 cm combines the two movements to mimics a real subject chest motion, and the waveform of it is shown in Fig. 7(a). Fig. 7(b)–(d) show the processing result for movement signal gathered by the radar sensor. As described earlier, the dc offset and I/Q imbalance errors from hardware imperfect are calibrated before the system put into use; then during the experiment, dc offset caused by stationary objects reflection is calibrated using the dynamic dc offset compensation method. The I and Q channel signals after two steps calibration are shown in Fig. 7(b). The calculated estimation center is at (9.57, − 1.8) (mV) with radius 21.42 mV. One can find that the calibrated curve fits well with the fitted circle of it, and this proves that the phase and amplitude error is rather small after calibration and the center of the data is moved to the origin accurately. Complex plot of 10 s original and calibrated I/Q channel signal is shown in Fig. 7(c). At last, we got the arctangent demodulated and unwrapped x(t) using (5) as the demodulation signal, which is shown in Fig. 7(d). The maximum amplitude of demodulation signal is about 6 mm that corresponds with the target displacement. We can intuitively tell from the result that the signal measured by radar sensor is exactly consistent to the simulated target movement signal. The arctangent demodulation with calibration provides high linearity signal reconstruction. Compared to simulated target movement signal in Fig. 7(a), the demodulated signal gives a mean squared error (MSE) of 0.35%. B. Human Cardiopulmonary Sensing Results Ten healthy volunteers (25.9 ± 2.1 years, 170.7 ± 6.7 cm, 63.6 ± 9.7 kg, 5 males, and 5 females) were included in this study. The volunteers were free of known cardiac, respiratory, or any other diseases. The study was approved by the local institutional ethical committee, and all the volunteers gave their written informed consent. In human cardiopulmonary sensing experiment, 4 min data were recorded from each subject 0.5 m away from the radar sensor, seating still and breathing normally. While gathering

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Fig. 8. Data recorded from an actual human subject. (a) In-phase baseband signal. (b) Quadrature baseband signal. (c) Phase signal after applying the same demodulation algorithm as earlier.

these data, one-channel ECG and one-channel respiration signal measured from upper torso using piezoelectric sensor are also recorded synchronously from the subject. They are used as reference signal to examine the accuracy of the extracted cardiopulmonary information. The two-channel reference signals are recorded using a NI USB-6215 DAQ with a sampling rate of 2000 Hz. The example of the original reflected signal in radar baseband, zoomed in 100 s time internal, is shown in Fig. 8(a) and (b). After demodulation, the phase signal shown in Fig. 8(c) contains both heartbeat and respiration motion together with unwanted clutter. From the demodulated signal, it is easy to read the respiration rate as its peaks and valleys are quite obvious, but the heartbeat signal cannot be clearly observed from it. The signal quality is highly varying and unreliable due to the uncontrolled environment of body shaking in which the measurements are performed. The real person’s heartbeat movement that can be detected by radar is even smaller than 0.5 mm. The demodulated phase signal is processed using wavelet filters; moving average filters were then applied to both the high-pass-filtered signals, which contain the heartbeat information and the low-frequency signal for respiration analysis. After that we extracted the heartbeat and respiration waveform. The examples of 30 s extracted heartbeat signal and ECG reference signal are compared in Fig. 9(a), and 150 s extracted respiration signal and respiration reference signal are compared in Fig. 9(b). The results show that the heartbeats superimposed on the respiration can be decomposed with clearly seen peaks and corresponds with the ECG signal using this technique, whereas the respiration decomposed results also show a high consistency with the reference signal. The respiration rate can be simply calculated after zerocrossing detector with low relative error since it has big amplitude and long duration. The following will focus on HRV analysis and heartbeat rate detection. According to the signal processing method previously mentioned, the heartbeat signal was then processed using EEMD

Fig. 9. Extracted signal compared with reference signal. (a) Extracted heartbeat signal and ECG reference signal. (b) Extracted respiration signal and respiration reference signal.

Fig. 10. Estimated beat-to-beat intervals of filtered radar heartbeat signal and the peak-to-peak intervals calculated from the ECG reference signal.

algorithm to improve its measurement accuracy. From one of the subject, the beat-to-beat interval estimated after this processing is shown in Fig. 10, and it is compared with the peak-to-peak intervals calculated from the ECG reference signal. The results show that the estimated intervals consistent well with that of reference for all the 4 min data. The HRV analysis or time-domain cardiopulmonary information extraction is possible in human experiment. The summary of the HRV analysis results from ten subjects is listed in Table II. We define the relative error as the root-meansquare error (RMSE) over the mean value of intervals derived from reference signal. The method of Bland and Altman [21] is used to assess the agreement between the two signals. For the Bland and Altman analyses, the Bias is the mean difference between the two, SD is standard deviation of the difference,

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TABLE II CORRELATION COEFFICIENT AND BLAND AND ALTMAN ANALYSIS OF BEAT-TO-BEAT INTERVALS AND THE RESULTS OF HRV INDICES FROM DOPPLER RADAR AND ECG

TABLE III AVERAGE HEART RATE MEASUREMENT RESULT

Fig. 11.

Heart rate estimation using 15 s data.

and the 95% limits of agreement is defined as follows: lower limits of agreement (LL) = Bias – 1.96 SD, and upper limits of agreement (UL) = Bias + 1.96 SD. If the differences within Bias ± 1.96 SD are not clinically important, the two methods may be used interchangeably. In this experiment, to assess the feasibility of HRV measurement via radar sensor, four HRV indices were calculated from the beat-to-beat intervals and compared with ECG results. The average values of beat-to-beat interval extracted from radar sensor were similar to the average intervals extracted from ECG. The relative error ranges from 2.53% to 4.83%. Most of bias magnitudes and 95% limits of agreement were small. The results obtained from the Bland and Altman analysis are in agreement with those obtained from relative errors and suggest that the radar heartbeat signal could be suitable to get beat-tobeat intervals as a surrogate of ECG R-R peak time intervals. For HRV indices shown in Table II, all SDNNs of radar sensor and ECG differed within − 1.5 to 15.5 ms2 , RMSSDs differed within − 0.1 to 4.7 ms2 . However, the HF and LF results of some subjects are not quite accurate compared with ECG results. One out of ten subjects has LFs that differed more than ±35 ms2 , and five subjects have HFs not within ±42 ms2 . The example of average heart rate measurement result is shown in Fig. 11. The solid curve shows the radar-detected heartbeat rate in beats per minute (bpm); the dotted line shows the ECG reference heartbeat rate in bpm. Finally, the calculated heart rate of the ten subjects is listed in Table III. Values are expressed as mean ± standard deviation. Define the heart rate

accuracy as the percentage of time the detected rate is within ± 2% (presented as p = 2%) of the reference rate according to [22]; the results show that the accuracy is 100% for all subjects. And when p = 1%, the accuracy is higher than 97%. IV. DISCUSSION The Doppler radar sensor system can provide noncontact cardiopulmonary monitoring, which is more easy for data collection and is certainly convenience for both the subject and the examiner. Since signal obtained in Doppler radar sensor is equivalent to the chest-wall displacement signal, it is natural to ask whether the cardiopulmonary information obtained from it can be used as a surrogate of that obtained from traditional methods. The developed Doppler radar sensor system has a compact size and is portable and user friendly. It has a truly noncontact operation capability and can penetrate clothing, whereas the radiation power poses no threat to human safety. The sensor system uses common seen electronic components so that it can be considered as a low-cost solution for monitoring vital signs in many applications such as home healthcare. For Doppler radar sensor, experiment using simulated target showed that the baseband has a high SNR to cope with the change in the heartbeat amplitude. The 24-bit ADC was proved

HU et al.: NONCONTACT ACCURATE MEASUREMENT OF CARDIOPULMONARY ACTIVITY

to have enough resolution that enabled the digitalization of baseband signal with both large dc offset and tiny heartbeat signal. The demodulation linearity was high and tiny movement from target can be reconstructed. In addition, based on this method, the demodulated signal gives an absolute motion displacement of the target. The amplitude and demodulation sensitivity will not change with distance, so the acquired information has the potential not only for heartbeat and respiration analysis, but also useful when the amplitude of chest wall motion is of interest in some studies. It should be noted that the nature chest wall motion of a subject is hard to collect using traditional method. The objective of these experiments using simulated target was to demonstrate that the target’s chest wall movement can be detected with reasonable accuracy for further analysis. The noise from sensor hardware can be ignored. So, if there is clutter in the reflected signal when the target is human subject, it was mainly caused by body movement, body shaking or other background moving target. For digital signal processing, the wavelet filter and EEMD method proved to be powerful in Doppler radar signal processing. The weak biosignals can be detected in time domain using this method, which cannot be acquired using fast Fourier transform (FFT) based methods. It has positive meanings when considering that many physiological changes are measured in time domain, such as instantaneous alteration in heart rate are often measured during treatment. And thus, this method has promising to be used in real applications such as general health monitoring or early diagnosis of some diseases. From the experiment, the Bland and Altman analysis suggests that radar results correlated well with ECG R-R peak intervals. The heart rates show that the sensor can provide an accurate analysis for HR in all subjects. It suggests that the proposed method can serve as an alternative to traditional rate monitor and get used in hospital wards or other long time monitoring applications to provide a more convenient measurement. The result that some estimated HRV indices not consistent well with reference results, especially for HF and LF values, is probably related to the following aspects: 1) Different from ECG method, the beat-to-beat intervals of radar signals are calculated from zero crossing of the filtered waveform, thus resulting in some errors. The Doppler radar heart signal does not exhibit a peak as sharp as the ECG signal since it is a measurement of mechanical motion of the chest wall, so the detection accuracy of ECG or PPG methods cannot be reached by Doppler signal. The results should be interpreted in such context. 2) The radar signal from human subjects may have nonlinear distortion by itself. The chest wall movement due to respiration will certainly affect how the heart’s motion is and cause the translation, rotation, and deformation of the heart, thus resulting in a peak location shift at the skin surface. But there are rarely known studies of skin surface chest motion due to heart beat at different points in the respiration cycle, since most of detection method measured heartbeat and respiration signals respectively. 3) The amplitude of respiration is more than 100 times larger than the heartbeat in real applications, and the large ampli-

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tude signal cannot be seriously filtered out in the following procedure. In addition, Morgan and Zierdt [10] have found that the respiration movement at the skin surface typically has many significant harmonics, which affect the reliable detection of the much smaller heartbeat signal. Respiration harmonics change the relationship of the peak location and affect the HRV indices, especially HF and LF values. This problem may be special for Doppler signal for its simultaneous acquisition of breathing and heart rate. 4) Signal caused by body shaking has the same frequency band as cardiopulmonary signal, which will affect the detection accuracy. The results from Massagram et al. [11] have shown that the HR and HRV assessments from Doppler radar were more accurate when the subjects were in a supine position than in a seated position for the same subjects. This reveals that the extracted heartbeat signal may be influenced by tiny body movement or shaking, which contributes to clutter within the interesting band. The improvement of measurement accuracy requires better processing algorithms. For instance, the paper will focus on finding an optimal wavelet since the waveform extraction accuracy could be influenced by the choice of wavelet filters. For EEMD algorithm, in future works, studies will be carried out to determine a proper ensemble size and the added noise amplitude. V. CONCLUSION In this study, a method for noncontact cardiopulmonary sensing based on Doppler radar technique was proposed. The sensor system was realized and integrated on a PCB. An algorithm for the determination of the respiration and heartbeat features was developed. The method was validated by experiments using both simulated target and human subject. Advantages of this method include the following: it is a truly noncontact detection method, requiring no user initiative intervention, and the sensor system is compact; the quadrature receiver used in radar front end ensured good recovery of chest displacement without the null point problem; the demodulation algorithm with calibration method was proposed to provide accurate and high linearity demodulation output. The wavelet filters and EEMD method were adopted to extract heartbeat and respiration waveforms with high accuracy. The human experiment results have shown that the sensor can provide an accurate analysis for HR and fairly accurate interbeat times. The Doppler radar sensor as a noncontact cardiopulmonary sensing method has the potential to be used in sleep monitoring, infant monitoring, home healthcare [2], [11], or medical treatment application [23]. As this paper was mainly concerned with introducing accurate measurement techniques, practical issues such as dynamic motion compensation were not discussed. Large body motion of the subject overwhelms the small respiration and heartbeat signals, making estimation of vital signs very difficult. We were not able to obtain reliable results using the proposed method under these conditions. Improvements in signal processing method may help to inhibit noise and clutter or separate the stable segment from

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 3, MARCH 2014

short interference signal, as well as getting the measurement results in real time. When considering the proper solution to cope with the nonlinear superposition of cardiopulmonary signals, the sensor configuration method can be studied. These are the possibilities for future work. REFERENCES [1] K. M. Chen, D. Misra, H. Wang, H. R. Chuang, and E. Postow, “An X-band microwave life-detection system,” IEEE Trans. Biomed. Eng., vol. BME-33, no. 7, pp. 697–702, Jul. 1986. [2] C. Li and J. Lin, “Recent advances in Doppler radar sensors for pervasive healthcare monitoring,” in Proc. 22nd Asia-Pacific Microw. Conf., Yokohama, Japan, Dec. 2010, pp. 283–290. [3] K. M. Chen, Y. Huang, J. Zhang, and A. Norman, “Microwave lifedetection systems for searching human subjects under earthquake rubble and behind barrier,” IEEE Trans. Biomed. Eng., vol. 47, no. 1, pp. 106– 114, Jan. 2000. [4] A. D. Droitcour, O. Boric-Lubecke, V. M. Lubecke, J. Lin, and G. T. A. Kovacs, “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. [5] J. H. Choi and D. K. Kim, “A remote compact sensor for the real-time monitoring of human heartbeat and respiration rate,” IEEE Trans. Biomed. Circuits Syst., vol. 3, no. 3, pp. 181–188, Jun. 2009. [6] X. Yu, C. Li, and J. Lin, “System level integration of handheld wireless non-contact vital sign detectors,” in Proc. IEEE Radio Wireless Symp., Jan. 2009, pp. 514–517. [7] 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. [8] S. Bakhtiari, S. Liao, T. W. Elmer, N. Gopalsami, and A. C. Raptis, “A real-time heart rate analysis for a remote millimeter wave I-Q sensor,” IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1839–1845, Jun. 2011. [9] C. Li, J. Ling, J. Li, and J. Lin, “Accurate Doppler radar non-contact vital sign detection using the RELAX algorithm,” IEEE Trans. Instrum. Meas., vol. 59, no. 3, pp. 687–695, Mar. 2010. [10] D. R. Morgan and M. G. Zierdt, “Novel signal processing techniques for Doppler radar cardiopulmonary sensing,” Signal Process., vol. 89, no. 1, pp. 45–66, Jan. 2009. [11] W. Massagram, V. Lubecke, A. Host-Madsen, and O. Boric-Lubecke, “Assessment of heart rate variability and respiratory sinus arrhythmia via Doppler radar,” IEEE Trans. Microw. Theory Tech., vol. 57, no. 10, pp. 2542–2549, Oct. 2009. [12] C. Gu, Z. Salmani, H. Zhang, and C. Li, “Antenna array technology for radar respiration measurement in motion-adaptive lung cancer radiotherapy,” in Proc. BioWireleSS, Santa Clara, CA, Jan. 2012, pp. 15–18. [13] O. Aardal, Y. Paichard, S. Brovoll, T. Berger, T. S. Lande, and S. Hamran, “Physical working principles of medical radar,” IEEE Trans. Biomed. Eng., vol. 60, no. 4, pp. 1142–1149, Apr. 2013. [14] G. Ramachandran and M. Singh, “Three-dimensional reconstruction of cardiac displacement patterns on the chest wall during the P, QRS, and T-segments of the ECG by laser speckle interferometry,” Med. Biological Eng. Comput., vol. 27, no. 5, pp. 525–530, 1989. [15] A. E. Aubert, L. Welkenhuysen, J. Montald, L. de Wolf, H. Geivers, J. Minten, H. Kesteloot, and H. Geest, “Laser method for recording displacement of the heart and chest wall,” J. Biomed. Eng., vol. 6, no. 2, pp. 134–140, 1984. [16] B. 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. [17] F. E. Churchill, G. W. Ogar, and B. J. Thompson, “The correction of I and Q errors in a coherent processor,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-17, no. 1, pp. 131–137, Jan. 1981. [18] N. Huang, Z. Shen, S. Long, M. C. Wu, H. Shih, Q. Zheng, N. Yen, C. Tung, and H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis,” Proc. Roy. Soc. London, vol. 454, no. 1971, pp. 903–995, Mar. 1998. [19] Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: A noise-assisted data analysis method,” Advances Adaptive Data Anal., vol. 1, no. 1, pp. 1–41, 2009.

[20] J. S. Wong, W. A. Lu, K. T. Wu, M. Liu, G. Y. Chen, and C. D. Kuo, “A comparative study of pulse rate variability and heart rate variability in healthy subjects,” J. Clin. Monit. Comput., vol. 26, no. 2, pp. 107–114, 2012. [21] J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet, vol. 1, no. 8476, pp. 307–310, 1986. [22] C. Li, Y. Xiao, and J. Lin, “Experiment and spectral analysis of a lowpower Ka-band heartbeat detector measuring from four sides of a human body,” IEEE Trans. Microw. Theory Tech., vol. 54, no. 12, pp. 4464–4471, Dec. 2006. [23] C. Gu, R. Li, H. Zhang, A. Y. C. Fung, C. Torres, S. B. Jiang, and C. Li, “Accurate respiration measurement using DC-coupled continuouswave Radar sensor for motion-adaptive cancer radiotherapy,” IEEE Trans. Biomed. Eng., vol. 59, no. 11, pp. 3117–3123, Nov. 2012.

Wei Hu (S’12) received the B.S. degree in electronic information science and technology from the University of Science and Technology of China, Hefei, China, in 2009, where she is currently working toward the Ph.D. degree in circuits and systems. She is currently a Visiting Student with the Department of Radio Frequency Integrated Circuit, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China. Her current research interests include biomedical applications of microwave and millimeter-wave technologies, wireless biomedical sensor, and radio-frequency-integrated circuits.

Zhangyan Zhao received the B.S. degree in electronic information science and technology and the Ph.D. degree in biomedical engineering from the University of Science and Technology of China, Hefei, China, in 2005 and 2010, respectively. He is currently a Research Assistant at the Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China. His current research interests include biomedical sensors and systems, data acquisition and instruments, and radio frequency measurement.

Yunfeng Wang received the B.S. in microelectronics from the Xidian University, Xi’an, China, in 2004, and the Ph.D. degree in microelectronics and solid state electronics from the Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China, in 2009. He is currently an Associate Professor in the Institute of Microelectronics, Chinese Academy of Sciences. His current research interests include analog and RF circuit design for low-power wireless communications, biomedical circuits and systems, and wearable healthcare and its enabling ultra-low-power design and technology.

HU et al.: NONCONTACT ACCURATE MEASUREMENT OF CARDIOPULMONARY ACTIVITY

Haiying Zhang was born in China, in 1964. She received the B.S. degree in semiconductor physics and technology from the Harbin Institute of Technology, Harbin, China, in 1986, the M.S. degree in microelectronics from Jilin University, Changchun, China, in 1989, and the Ph.D. degree in electrical and electronic engineering from Saint-Petersburg State Electro-Technical University, Saint Petersburg, Russia, in 1993. From 1994 to 1997, she was an Engineer with Croena Semiconductor Ltd., Moscow, Russia, where she was involved with clean-room fabrication and technology process setup. From 1997 to 1999, she was with the Department of Microwave Circuits and Compound Semiconductor Devices, Institute of Micro-electronics, Chinese Academy of Sciences (IMECAS), Beijing, China. In 1999, she became a Professor and Deputy Director with the Department of Microwave Circuits and Compound Semiconductor Devices, IMECAS. In 2010, she established the RF Integrated Circuit Department, IMECAS. Since 2000, she has been involved with research of RF circuit design technology and device technology for years. Her current research interests include 3G/4G wireless transmitter circuit design technology, embedded multimode, multifrequency wireless transceiver IP design methodologies, medical electronics integration technology, ultra-wideband wireless personal area network standard, and Internet of things related technologies.

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Fujiang Lin (M’93–SM’99) received the B.S. and M.S. degrees from the University of Science and Technology of China (USTC), Hefei, China, in 1982 and 1984, respectively, and the Dr.-Ing. degree from the University of Kassel, Kassel, Germany, in 1993, all in electrical engineering. After finishing his Ph.D. program in Germany in 1993, he was a Research Scientist in National University of Singapore. In 1995, he was transferred to the Institute of Microelectronics (IME), Singapore, as a Member of Technical Staff, where he pioneered practical RF modeling for MMIC/RFIC development. In 1999, he joined HP EEsof, as the Technical Director, where he established the Singapore Microelectronics Modeling Center, providing accurate state-of-the-art device and package characterization and modeling solution service worldwide. From 2001 to 2002, he started up and headed Transilica Singapore Pte. Ltd., a research and development design center of Transilica Inc., a Bluetooth and IEEE 802.11 a/b wireless system-on-chip (SoC) company. The company was acquired by Microtune Inc. After the close down of Transilica Singapore in 2002, he joined Chartered Semiconductor Manufacturing Ltd. (now GlobalFoundries, second largest foundry), as the Director, where he led the SPICE modeling team in support of company business. In 2003, he rejoined IME as a Senior Member of Technical Staff, where he focused on upstream research and development initiatives and leadership toward next waves. His current research interests include the development of CMOS as a cost-effective technology platform for 60-GHz band millimeterwave SoC, as well as millimeter wave therapy for healthcare applications. As an Adjunct Associate Professor with the National University of Singapore, Singapore, and Adjunct Professor with USTC, Hefei, China, he is actively involved in educating and training post-graduate students. In 2010, he returned back USTC as full professor under the Chinese “1000 Talents Program,” where he is currently the head of the Department Electronic Science and Technology. He is the Executive Director of the MESIC (Micro-/Nano-Electronic System Integration R&D Center,” which is jointly established by USTC and IME.CAS http://english.ime.cas.cn/). He has authored or coauthored over 120 scientific papers. He holds seven patents. His current research interests include modeling cum IC validation of next-generation technology devices such as GaN and FinFET. Dr. Lin has been involved in the IEEE activities in different functions since 1995, including chair of the IEEE Singapore MTT/AP Chapter, Committee Member of Singapore Section, Reviewer Board Member for a few of transactions or journals, and Technical Program Committee (TPC) member of numerous conferences such as RFIC and ESSCIRC. He is the Initiator and Coorganizer of international workshops and short courses at APMC99, SPIE00, ISAP06, and IMS07. In 1995, he and his team initiated and organized the IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), Singapore and now become MTT-S fully sponsored flagship conference in Asia. Dr. Lin was the recipient of the 1998 Innovator Award presented by EDN Asia Magazine.