IEICE Communications Express, Vol.2, No.2, 36–41

Biological activity detection
method using MIMO system
Masaki Nango1a) , Naoki Honma1 , Kentaro Nishimori2 ,and Hiroaki Sato1
1

Graduate School of Engineering, Iwate University 4–3–5 Ueda, Morioka, 020–8551, Japan2

Graduate School of Science & Technology, Niigata University 2–8050, Ikarashi,Niigata, 950–2181, Japana) t2311045@iwate-u.ac.jp

Abstract:
This letter proposes two biological activity detection
methods using MIMO system and compares them. These methods
detect the biological activity of human by using the time-variant MIMO
channel and improve the detection sensitivity by using the multiantennas. Experiment and simulation results demonstrate the proposed method can improve the detection sensitivity than the detection
method using SISO system.
Keywords: MIMO, biological activity, microwave, welfare, sensor
Classification: Antennas and Propagation
Referencesc

IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 2013[1] Y. J. An, B. J. Jang, and J. G. Yook, “Detection of human vital signs andestimation of direction of arrival using multiple doppler radars,” Journal of The Korean Institute of Electromagnetic Engineering and Science,
vol. 10, no. 4, pp. 250–255, Dec. 2010.
[2] R. R. Fletcher and S. Kulkarni, “Clip-on wireless wearable microwave
sensor for ambulatory cardiac monitoring,” EMBC 2010, vol. 1, pp. 365–369, Aug. 2010.
[3] N. Petrochilos, M. Rezk, A. Host-Madsen, V. Lubecke, and O. BoricLubecke, “Blind separation of human heartbeats and respiration by theuse of a doppler radar remote sensing,” ICASSP 2007, vol. 1, pp. I-333–I-336, April 2007.
[4] D. Samardzija, B. K. Park, O. Boric-Lubecke, V. Lubecke, A. HostMadsen, and T. Sizer, “Experimental evaluation of multiple antenna
techniques for remote sensing of physiological motion,” 2007 IEEE/
MTT-S International Microwave Symposium, vol. 1, pp. 1735–1738, June
2007.
[5] O. Boric-Lubecke, V. M. Lubecke, A. Host-Madsen, D. Samardzija, andK. Cheung, “Doppler radar sensing of multiple subjects in single and multiple antenna systems,” Telecommunications in Modern Satellite, Cableand Broadcasting Services, 2005, vol. 1, pp. 7–11, Sept. 2005.
[6] J. G. Kim, S. H. Sim, S. Cheon, and S. Hong, “24 GHz circularly polarized doppler radar with a single antenna,” Microwave Conference, 2005
European, vol. 2, pp. 1383–1386, Oct. 2005.
[7] D. Nagae and A. Mase, “Measurement of heart rate variability and stressevaluation by using microwave reﬂectometric vital signal sensing,” Re-36

IEICE Communications Express, Vol.2, No.2, 36–41

view of Scientific Instruments, vol. 81, pp. 094301-1–094301-10, Sept.
2010.
[8] H. Avagyan, A. Hakhoumian, H. Hayrapetyan, N. Pogosyan, and T.
Zakaryan, “Portable non-contact microwave doppler radar for respirationand heartbeat sensing,” Armenian Journal of Physics, vol. 5, no. 1, pp. 8–14, April 2012.
[9] K. Higashikatsuragi, Y. Nakahata, I. Matsunami, and A. Kajiwara,“Remote respiration monitoring using ultra-wideband microwave sensor,” IEEJ Transactions on Electronics, Information and Systems (inJapanese), vol. 129, no. 6, pp. 1056–1061, 2009.
[10] K. Ota, M. Otsu, Y. Ota, and A. Kajiwara, “Elderly-care motion monitoring mensor using ultra-wideband radio,” IEEJ Transactions on Electronics, Information and Systems (in Japanese), vol. 131, no. 9, pp. 1547–1552, 2011.

1

Introduction

A population aging and the increase of the elderly people living alone are ongoing issues in Japan. As a result, fall, tumble, and lonely death have become
the social problems, and the need of safety conﬁrmation system for elderly
is growing. The use of video camera and the ECG (Electrocardiogram) canbe considered for the safety conﬁrmation. However, the former has a privacyproblem and the latter has the problem that has physical and mental burden
due to direct contact of the probe to the human body.
In order to solve these problems, the technologies using microwave havebeen studied [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The technologies do not violate
privacy and can detect the vital sign without attaching probes. But there isa problem that the detection accuracy is degraded if the distance between ahuman body and the detector becomes greater [8].
In this letter, two biological activity detection methods, which can achieve
high sensitivity by using MIMO (Multiple-Input Multiple-Output) system,
are proposed. These methods detect the biological activity of human by exploiting the time-variant MIMO channel and improve the detection sensitivity
by using the multi-antennas. Experiment and simulation results demonstrate
the proposed method can improve the detection sensitivity more than the detection method using SISO (Single-Input Single-Output) system.

2c

IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 2013

Channel measurement conditions

Fig. 1 shows the overview of the measurement system. In this study, theMIMO channels are measured by switching multiple transmitting and receiving antennas by using SPDT (Single-Pole Double-Throw) switches. 2 × 2
MIMO conﬁguration where both transmitter and receiver have two horizontally arranged patch antennas are used. 6 patterns of the distance, d,between the antenna and the human body are tested, i.e., d is 1.0 m, 2.0 m,3.0 m, 4.0 m, 5.0 m, and 6.0 m. The antenna height is 1.17 m which is astall as the chest of subject. The spacing between transmitting antennas is37

IEICE Communications Express, Vol.2, No.2, 36–41

0.5 wavelengths, and the spacing between transmitting and receiving arrays
are 1.5 wavelength. Transmitted power is set to 0 dBm. 2.4 GHz band CW
signal is used; therefore, this measurement system can work with narrower
bandwidth compared to the conventional studies [9, 10]. The time-variant
channels are measured for 50 seconds when the subject is standing alone andfacing to the antennas.

Fig. 1. Experimental conﬁguration

3Proposed method

The measured m × n time-variant MIMO channel is expressed as,⎛h11 (t).
⎜H(t) = ⎝ ..

···..
.

⎞h1n (t).. ⎟. ⎠,(1)hm1 (t) · · · hmn (t)where, hij is the complex channel response, and t represents the time when
the channel is observed. By Fourier transformation, the frequency responseof this time-variant MIMO channel is expressed as,⎛F11 (f )..
⎜F (f ) = ⎝.
Fm1 (f )···..
.

⎞F1n (f )..
⎟⎠.
.

(2)· · · Fmn (f )By eigenvalue decomposition, F (f ) is expressed as,
F (f )F (f )H = U (f )λ(f )U H (f ),(3)where, {·}H means the hermitian transpose. Also, U (f ) represents the eigenvalue vectors and λ(f ) is diagonal matrix representing eigenvalues. They areexpressed as,⎛u11 (f )..
⎜U (f ) = ⎝.
um1 (f )⎛c


IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 2013

⎜λ1 (f )λ(f ) = ⎝0···..
.

⎞u1m (f )..
⎟⎠,.

(4)

· · · umm (f )
0..

.

⎞
⎟
⎠.

(5)

λm (f )
38

IEICE Communications Express, Vol.2, No.2, 36–41

Fig. 2 shows the frequency responses of time-variant SISO channels measured without the subject and with the subject when the distance, d, is 3.0 m.
This SISO channels are the components of the ﬁrst row and ﬁrst column ofthe MIMO channel matrices. This ﬁgure indicates that the inﬂuence of biological activity of human appears at low-frequency part.
In this letter, the following two methods are proposed and compared.
(1)Method 1 :This is a method using sum of the energy spectral density of the timevariant channel as the evaluation function, which is expressed as,
ρ1 = max

 f2

1≤i≤m1≤j ≤n

f1|Fij (f )|2 df.

(6)

The channel with maximum energy is chosen, that is, this method will yield
diversity eﬀect.
(2)Method 2 :This is a method using sum of the maximum eigenvalue at each frequency,
and the evaluation function is expressed as,
ρ2 =

 f2
f1

λmax (f )df,

(7)

where, f1 and f2 are the frequency range containing the inﬂuence of humanactivities. In this letter, f1 and f2 are set to 0.02 Hz and 1.6 Hz respectively
to observe the variation component inﬂuenced of biological activity except
the DC component. 0.02 Hz is the lowest frequency which is determined byfrequency resolution, and 1.6 Hz is the analyzable maximum frequency inthis measurement since the acquisition frequency of the channel is 3.2 Hz. If
the evaluation function exceeds the threshold, the biological activity can bedetected. The threshold value is set so that the false detection rate becomes
1% for both proposed methods. Where, the false detection is deﬁned as theprobability of evaluation function exceeding the threshold when there is noperson.

Fig. 2. Frequency responsec

IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 2013

39

IEICE Communications Express, Vol.2, No.2, 36–41

4

Experimental result

The detection rate is evaluated in the Monte Carlo simulation by adding
the Gaussian noise to the measured channel. Detection rate is deﬁned as(detection times) / (number of simulations). SNR (Signal to Noise Ratio) isdeﬁned as the ratio of the averaged signal power in static environment without
the human to the noise power per antenna. Number of the simulations is setto 2000.
Fig. 3(a) indicates the detection rate versus SNR when antenna distance,d, is 6.0 m. In this result, the method 1, which uses sum of the energy spectral
density of the time-variant MIMO channel, improves the required SNR byabout 3 dB for 90% detection rate compared with the method 1 using SISOchannel. It is considered that this improvement is obtained by the diversity
eﬀect. And the method 2, which uses the maximum eigenvalue of frequency
response matrix, shows the highest detection rate for any SNR, and improves
the required SNR by about 5 dB for 90% detection rate compared with themethod 1 using SISO channel. It is considered that this improvement isobtained by the eﬀect of MRC (Maximum Ratio Combining).
Fig. 3(b) indicates the detection rate versus antenna distance when SNR is10 dB. In this result, the method 1 using MIMO channel improves the detec-c

IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 2013

Fig. 3. Experimental results

40

IEICE Communications Express, Vol.2, No.2, 36–41

tion rate by about 20% compared with method 1 using SISO channel whenantenna distance is 6.0 m. And the method 2 shows the highest detection
rate for any antenna distances and improves the detection rate by about 30%
compared with method 1 using SISO channel. Based on the results shown
here, it is found the MIMO conﬁguration yields higher sensitivity than SISO
conﬁguration, and the MRC scheme outperforms the diversity scheme.

5Conclusion

This letter has proposed two biological activity detection methods with highsensitivity using MIMO system and compares their performances. The dependence of the detection rate on SNR was evaluated, and it is found thatthe proposed method 2, which uses the maximum eigenvalue of frequency
response matrix, shows the highest detection rate among the methods discussed in this study. It is also found that the method 2 improves the requiredSNR by about 5 dB for 90% detection rate compared with method 1 using
the SISO channel. The eﬀect of the antenna distance was evaluated when
SNR is 10 dB, and it is found that the proposed method 2 improves the detection rate by about 30% compared with method 1 using SISO channel whenantenna distance is 6.0 m. From these results, it is found that the proposed
method using the eigenvalue in MIMO channel is eﬀective in detecting thebiological activity. In this letter, the measurement was performed in a statein which the subject is standing. In the future, the evaluation in the diﬀerent
posture such as the lying subject will be investigated.

Acknowledgements
This research was partially supported by the Center for Revitalization Promotion of the Japan Science and Technology Agency (JST), and MEXT
KAKENHI (23500549).

c

IEICE 2013

DOI: 10.1587/comex.2.36
Received December 14, 2012
Accepted January 10, 2013
Published February 13, 201341