Target Discrimination for Multiple Vital Sign Detection with Super-Resolution Algorithm

Hyunjae Lee and Jong-Gwan Yook

Department of Electrical and Electronic Engineering, Yonsei, University Seoul, 120-749, Republic of Korea

Abstract –Microwave Doppler radar based vital sign sensor

has been attracting attention due to an advantage of RF/microwave characteristics including non-contact, non-destructive, and non-obtrusive characteristics. However, although Doppler radar has the high sensitivity for vital signs, multiple subject issue is one of the inevitable issue in remote sensing system. In this paper, we proposed the vital sign detection method for two adjacent targets through target discrimination using 24 GHz frequency-modulated continuous-wave (FMCW) Doppler radar with super-resolution algorithm. To verify the proposed system, we carried out an indoor experiment in the hallway. Furthermore, it was compared with the commercial piezoelectric sensor. The proposed system not only distinguished two targets about 50 cm away beyond the limit of theoretical range resolution by bandwidth limitation of FCC regulation, but also detected the vital signs of each.

Index Terms — Target discrimination, vital sign sensor, FMCW Doppler radar, super-resolution algorithm

1. Introduction

As the interest in a personal health care increased, various researchers have contributed to the advancement of technology in the sensing methods of the physiological signs, such as pulse rate, breathing rate, blood glucose, blood pressure, and so on [1-2]. Particularly, there are lots of the studies involved in the heart rate detection since the heartbeat is a significant factor not only for the patients but also for the healthy people as an intuitive indicator of the health condition. The heart rate can be used for the diagnosis and prevention of cardiovascular diseases (CVDs). Among various methods of the vital sign detection, the Doppler radar based vital sign sensor has been attracting attention due to its advantage of contactless and unobtrusive measurement. It can measure the infants and people, who are stressed by the pressure, without discomfort of wearing. However, since CW Doppler radar cannot detect the range information, it has a critical issue of multiple subject [3]. To resolve this problem, FMCW Doppler radar, which can detect both range and velocity information, is adopted [4]. However, it is still difficult to detect the adjacent two people owing to its bandwidth limitation of FCC regulation.

In this paper, a noncontact vital sign sensor for multiple targets using 24 GHz FMCW Doppler radar with super-resolution algorithm. We aimed to accurately and precisely detect the respective vital sign for two adjacent people. In this study, the positions of two people are estimated by applying the super-resolution algorithm to the beat frequency obtained by FMCW Doppler radar. Using the estimated

target range by threshold value, the phase history is extracted to detect the vital sign. As a result, the proposed system can detect the respective heartbeat signals at distance of each.

2. Theory of operating system

For multiple subjects, the baseband beat signal, which is the output of the mixer passed through the lowpass filter, can be written as

1

4 ( ) 4 ( )( ) exp

Nn c n

b n RVPn

mR f RS t A j t

c cπ τ π τ

φ=

= + +

∑ (1)

where An is the amplitude of the received signal of the n-th target, Rn is the target range of the n-th target, m is the frequency modulation slope, fc is the carrier frequency, φRVP is the residual video phase, which is negligible factor. Since the range information is inherent in the beat frequency and vital signal information is inherent in the phase history, (1) is converted to a frequency function and interpreted. By the fast-time Fourier transform, the beat signal can be expressed as

( ) ( )

1

4 2( ) exp sinc .

Nc n n

b nn

f R mRS f B j T f

c cπ τ τ

=

≈ −

∑

(2) To distinguish multiple targets using this formula, the sinc functions interfere with each other. In the case of two adjacent target, two sinc functions are integrated. It makes it recognizes as if the target is one.

To resolve this problem, the multiple signal classification (MUSIC) algorithm, one of the super-resolution method, is adopted. The MUSIC algorithm belongs to the subspace-based algorithm using the orthogonality of the signal and noise subspaces [5]. The searching vector is parametrically multiplied to the noise subspaces extracted from the autocovariance matrix. When the parametric searching vector is matched to the target signal, the pseudospectrum displays the peak value owing to the orthogonality of signal and noise subspaces. The pseudospectrum is given as

( ) ( ) ( )*

1.P f

f f=

*N Na U U a

(3)

3. Measurement results

Experiments are carried out in an indoor environment with two people sitting on each chair. To validate the performance

[WeD2-6] 2018 International Symposium on Antennas and Propagation (ISAP 2018)October 23~26, 2018 / Paradise Hotel Busan, Busan, Korea

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Fig. 1. Configuration of measurement setup.

Fig. 2. The result of range estimation in real time.

of the proposed vital sign sensor, a reference heartbeat signal is measured by a piezoelectric pulse sensor (UFI-1010) at the same time, as presented in Fig. 1. The bandwidth of the FMCW Doppler radar is from 24 to 24.25 GHz, i.e. compliant to FCC regulation. The real-time signal was digitized by the data acquisition (NI-9234) board and saved by a LabVIEW. Two people are located at 140 cm and 190 cm. This gap between two people is closer than theoretical range resolution of 60 cm.

Fig. 2 shows the result of range estimated from the beat signal in real time. The signal amplitude of the rear target is relatively smaller than that of the front target. Nevertheless, they were fully distinguished. However, target ranges were estimated to be located at 140 cm and 180 cm. This error is resulted from the dispersed human body, which is not a point scatterer. At each estimated distance, the phase history is extracted from (2). To detect the heart rate, the signal is filtered by bandpass filter from 1 Hz to 2 Hz, which is a typical range of adult’s resting heart rate. Fig 3 shows the results of vital sign in real time. Though errors occur in some parts due to the large interference of each other, the proposed sensor can detect continuously respective vital signs. Fig. 4 shows the compared result between reference sensor and the proposed sensor. The respective vital sign obtained by the proposed sensor is good agreement to the reference results.

4. Conclusion

This paper proposed multiple vital sign sensor without any rotating system. We adopted the super-resolution algorithm to discriminate two adjacent targets. Despite a large amplitude difference between the front and rear target, they are obviously estimated. In addition, the heart rate of each is detected from the extracted phase history at extracted range

Fig. 3. Heart rate of the front and rear target in real time.

0 0.5 1 1.5 2 2.5 3

Frequency [Hz]

0

0.2

0.4

0.6

0.8

1

No

rma

lize

d a

mp

litu

de

Target1 (proposed)

Target1 (reference)

Target2 (proposed)

Target2 (reference)

Fig. 4. Comparison result between the reference sensor and

the proposed sensor. by the super-resolution algorithm. In conclusion, the proposed vital sign sensor can detect the vital sign of two adjacent people. We envision that the proposed sensor is used in the application such as personal health care system and medical surveillance system.

Acknowledgment

This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2017-2013-0-00680) supervised by the IITP(Institute for Information & communications Technology Promotion).

References

[1] L.-C. Jiang and W.-D. Zhang, “A highly sensitive nonenzymatic glucose sensor based on CuO nanoparticles-modified carbon nanotube electrode,” Biosensors and Bioelectronics, vol. 25, no. 6, pp. 1402-1407, 2010.

[2] A. Pantelopoulos and N. G. Bourbakis, “A survey on wearable sensor-based systems for health monitoring and prognosis,” IEEE Transactions on Systems, Man,and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 1, pp. 1-12, 2010.

[3] O. Boric-Lubecke, V. M. Lubecke, A. Host-Madsen, D. Samardzija, and K. Cheung, “Doppler radar sensing of multiple subjects in single and multiple antenna systems,” in Telecommunications in Modern Satellite, Cable and Broadcasting Services, 2005. 7th International Conference on, vol. 11. IEEE, 2005, pp. 7-11.

[4] G. Wang, J.-M. Munoz-Ferreras, C. Gu, C. Li, and R. Gomez-Garcia, “Application of linear-frequency-modulated continuous-wave (LFMCW) radars for tracking of vital signs,” IEEE Transactions on Microwave Theory and Techniques, vol. 62, no. 6. Pp. 1387-1399, 2014.

[5] P. Stoica, R. L. Moses, et al., Spectral analysis of signals, Pearson Prentice Hall Upper Saddle River, NJ, 2005, vol. 1.

2018 International Symposium on Antennas and Propagation (ISAP 2018)October 23~26, 2018 / Paradise Hotel Busan, Busan, Korea

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