九州大学大学院総合理工学報告第31巻 第4号 8-12頁 平成22年3月

Engineering Sciences Reports, Kyushu University
Vol. 31, No. 4, pp. 8-12, MAR, 2010

Non-contact and Non-invasive Stress Evaluation
Using Microwave Reflectometry
Daisuke NAGAE*1,† and Atsushi MASE*2

E-mail of corresponding author: d-nagae8@asem.kyushu-u.ac.jp†(Received February 5, 2009)
In this paper, we present a novel signal processing technique for stress evaluation using a microwave reflectometric cardiopulmonary sensing. This technique enables the heart rate variability(HRV) to be reconfigured from measurements of body-surface dynamic motion, which is subsequently used for the stress evaluation. In particular, a novel element is due mainly to a reconfiguration of the HRV from the time variation of the heartbeat frequency which is evaluated by a repetitionof the maximum entropy method (MEM). An experimental setup of a microwave reflectometer ispresented and several sets of real data are analyzed using a new signal processing technique, which
are subsequently used for the stress evaluation. Finally, a utility of the new signal processing technique is discussed.
Key words: Cardiopulmonary sensing, ECG, Heart rate variability, Microwave reflectometry, Stressevaluation

1.

Introduction

In recent years, the stress evaluation using
the heart rate variability (HRV) has been
widely recognized as advanced application toprevention of stress syndrome, detection of
sleep prediction in driving a car, and so on. The
HRV is generally obtained from peak intervals(R-R intervals) in an electrocardiogram (ECG).
However, the evaluation method of the stressby using the ECG seems to be unsuitable as along-time monitor since several electrodes
have to be directly attached to a human bodyto acquire the ECG data. We report here on a
new evaluation method of the stress using a
microwave reflectometer which has the features of non-contact and non-invasive method.
There is a problem that must be solved toperform the evaluation of the stress by using a
microwave reflectometer, since the informationof vital signal is obtained from phase fluctuation of reflected waves. It is caused by the
movement of a human body surface (reflection
layer) consisting of the respiration, the heartbeat, and the random movement of the body.
To extract the heartbeat signal from themeasurement signal, for example, the respiration signal is removed by a high-pass filter*1 Department of Applied Science for Electronics andMaterials, Graduate Student*2 Art, Science and Technology Center for Cooperative
Research(HPF) with cutoff frequency of, such as, 0.7Hz,
although the random movement of the humanbody still can not be removed. The heartbeat
signal is very small and has low signal-to-noise(S/N) ratio since the cardiac movement appears on the body surface through the bone
and the connective tissue.1-3) For these reasons,the peak positions in the heartbeat signals
obtained by a microwave reflectometer are notas clear as those observed in the ECG signals.
Therefore, it is not easy to reconfigure theHRV correctly and automatically from the microwave reflectometer signal. In order to solve
this problem, we have proposed the cross-correlation technique. This is a method reconfiguring the HRV from the cross-correlation
function between a measurement signal and atemplate signal which is configured by averaging periodic waveform in a measurement
signal.2) However, it is difficult to reconfigure
the HRV in real time by the cross-correlation
technique, and which defies the detection ofstress state timely. The purpose of this paper is
to introduce a novel signal processing technique to reconfigure the HRV in real time.

2.

Stress Evaluation Based on ECG

Generally, the evaluation of the stress isperformed by analyzing the frequency spectrum of the HRV.2), 4) The HRV is reconfigured

9Non-contact and Non-invasive Stress Evaluation Using Microwave Reflectometry

by interpreting R-R intervals in an ECG. The
heartbeat interval is fluctuating all the time byreflecting autonomous nerve activity composedof sympathetic nerve activity and parasympathetic nerve activity. In the frequency spectrum of the HRV, the parasympathetic nerve
activity appears as the power spectrum in thehigh frequency (HF) component with frequency
range of 0.15-0.45 Hz, and both sympathetic
nerve activity and parasympathetic nerve activity appear in the low frequency (LF) component with frequency range of 0.03-0.15 Hz.
The peak ratio or area ratio of the power spectrum in the LF component to the HF component (LF/HF) is used to evaluate the stress,
since the sympathetic nerve activity increases
in a stressful state and, on the other hand, theparasympathetic nerve activity increases in arelaxed state. The value of the LF/HF in a
stressful state is larger than that in a relaxedstate. For the evaluation of stress in a stationary state, the fast Fourier transform (FFT)or the maximum entropy method (MEM) has
been used as a frequency analysis. In this case,the HRV data for 180 sec is often used for the
reliable estimation of stress. On the other
hand, in a non-stationary state, the wavelet
transform can be applied to the HRV data forthe evaluation of stress.
In addition, the HRV has to be interpolated
at regular intervals in derivation of the frequency spectrum. The most often-used interpolation interval is about 0.25 sec.

where xk is the observed time-series data interval, nk is the stationary white noise which is
independent from xt (l<k), m is the order of theautoregressive model, and ami is the autoregressive coefficient at the model order m. Now,
we define the autocorrelation function of thetime-series data xk as
Ri = R(i∆t ) ≡ E{xk xk −i }(2)where E{ } is the expectation value. The autocorrelation function at lag 0 is shown as

{ }

{ }

m

R0 = E xk2 = −∑ami Ri + E nk2

(3)

i =1

Since nk and xi (i<k) are independent eachother, the autocorrelation function at each lagis organized by

 R0
R
 1
 M

 Rm

R1 K Rm   1   pm R0
Rm −1   am1   0 =OM   M   M  (4)  L L R0  amm   0 

where Pm is the variance of the stationary
white noise. By applying the formula of Wiener-Khintchine to Eq.(1), the relationship between the autoregressive model {ak} and thepower spectrum S(ω) is shown as

S (ω) =Pm∆tm

1+ ∑ amie− jωi∆t

2

(5)

i =1

3.

Signal Processing

The time variation of the heartbeat intervals
can be regarded as the short-term time variation of the heartbeat frequency. In this section,
the time variation of the heartbeat frequency
is evaluated by applying the MEM repeatedly
at the short term. The time window is shifted
step by step along the temporal axis. Then, theHRV can be reconfigured since the value of theheartbeat interval is calculated by the inverseof the heartbeat frequency. The MEM is aneffective method for estimating the frequency
spectrum at a shot data window, and has muchhigher frequency resolution than the FFT.
In the MEM of analysis, the autoregressivemodel of observational data is estimated first,
then the spectrum estimation is performed on
the basis of it. The autoregressive model isgiven bym

xk = −∑ ami xk −i + nk
i =1

(1)

In the MEM, the model order is generally
unknown. If the model order is selected lower,
the estimated spectrum is smoothed and thespectrum peak which should be found in anormal situation does not appear sometimes.
Conversely, if the model order is selected
higher, a lot of spurious peaks appear finely inthe estimated spectrum. The optimum selection of the model order is of importance in the
MEM. Thus, we selected the optimum modelorder as 870 from the empirically-based experiences.
The heartbeat frequency is estimated byfinding the largest peak value in the frequency
range of 0.7–1.55 Hz (42–98 bmp) after calculating the power spectrum of some data windows by MEM. Here, the frequency 0.7-1.55 Hz
covers sufficiently the range of heartbeat frequency for a seated human subject at rest. It issaid that the heart rate for a seated humansubject is typically in the range of 45-90 bmp,
i.e., 0.75-1.5 Hz.3)

平成21年度

九州大学大学院総合理工学報告 第31巻 第4号

If the analyzing data window is selected long,time response of the heartbeat intervals becomes sluggish in the HRV while the reliabilityof the estimation enlarges. This could exert abad influence upon the estimation of LF/HF. In
this paper, 2.5 sec is selected as the optimum
length of the data window for the MEM analysis from the empirically-based experiences.
In addition, 0.25 sec is selected as the shiftquantity of the data window. It is equal to theinterpolated value of the HRV reconfigured by
the ECG. The MEM is the stationary spectrum
analysis technique where the consistency ofthe signal in the data window is assumed.
Therefore, since a correspondence relationshipbetween the data windows is ignored, some
spectrum estimation errors consist by the selected data window and appear as the fluctuation by 0.25 sec (4 Hz in frequency) in the HRV.
However, it is no problem for the evaluation of
the stress because the spectrum estimation
error between data windows is very small andits fluctuation does not flop over the frequency
domain of the LF and the HF components.
For these reasons, the time variation of theheartbeat frequency is estimated by applying
the MEM repeatedly at the 2.5 sec data window which is shifted by 0.25 sec along the
temporal axis.

4. Experimental Results
4.1

Experimental setup

Figure 1 shows a schematic diagram of themeasurement system. We select a thigh as anirradiation point of microwave, since its skin
movement is large due to the existence of thickartery, while it should be small during the respiration. It is not a problem whether themeasurement point is right thigh or left one.
We install a 16-elements patch antenna into aseat of a chair under the left thigh of a seatedhuman subject. The back of the chair can also
be selected as the antenna position since it isclose to the heart. The analog output from themicrowave reflectometer is fed to a HPF inorder to remove the respiratory components.
The output signal is then converted into digital
signal using the sampling frequency 1000 Hz
by a high-resolution (16 bits) memory scope.
The digitized signals are fed into a computer,and the heartbeat signals are calculated by thedetection of phase differences.1-3) Afterward,
the evaluation of the stress is performed byapplying the proposed algorithms to the calculated heartbeat signals.

Fig.1

4.2

10

Experimental setup.

Stress evaluation: static environment

In the first experiment, the evaluation
methods of the stress are applied to a person inboth relaxed state and stressful state. The
flash mental calculation is used to put stresson a person, which is a method that the person
adds up figures displayed on a monitor continually in his head. The heartbeat measurement by a microwave reflectometer is applied
to the person who is in the relaxed state or the
stressful state for 180 sec. Two types of proposed methods (the cross-correlation techniqueand the MEM technique) are applied to reconfiguration of the HRV. Then, the LF/HF value
is calculated by applying FFT to the HRV fortime interval of 180 sec. For reference, theECG data are measured at the same time ineach state and the LF/HF is calculated by theconventional evaluation method of the stress.
Figure 2 shows the time evolutions of the
microwave reflectometer signal and the ECG
signal of the person in the relaxed state. Notethat the time interval of the periodic signal isin good agreement between two signals. Thismeans that the heart beat can be obtainedcorrectly from the microwave reflectometry.
Figure 3 shows the subsets of the HRVs calculated by the original microwave reflectometer signal and by two proposed methods together with the one obtained from the ECG.
The HRVs calculated by two proposed methods
agree well with that by the ECG, comparing tothat by the unprocessed reflectometer signal.
Additionally, the HRV calculated by the MEM
contains periodical noise component comparedto that calculated by the cross-correlation
technique. This might be caused by some
spectrum estimation errors between data
windows which appear as the fluctuation by
0.25 sec (4 Hz in frequency) in the HRV. How-

11

Non-contact and Non-invasive Stress Evaluation Using Microwave Reflectometry

ever, the estimated error of the HRV due to thespectrum estimation error is very small, i.e.,
about several percent of the HRV values. Thus
the fluctuation of estimation errors is not a
problem for the evaluation of the stress as described in Sec. 3. Therefore, it is understoodthat the HRV is reconfigured correctly by using
the proposed method.
The frequencies of the HRVs reconfigured by
two proposed methods and the ECG in the relaxed state are analyzed, respectively. Generally, it is said that the individual variability of
the LF/HF is about ±30 %.5) If the spectral
shapes of the HRVs are similar and the
LF/HFs can be estimated by two proposed
methods in the range of ±30 % as compared to
the ECG, it could be argued that the estimation of stress is successful.
The results are shown in Fig. 4. In the sameway, the result in the stressful state is shownin Fig. 5. Here, In Figs. 4 and 5, the maximum
values of the spectrum peaks are made fit witheach other and normalized into 1 in order tocompare three spectra. The frequency spectraof the HRVs reconfigured by applying two
proposed methods to the microwave measurement are nearly conformable to that reconfigured by the ECG, respectively. The LF/HF
values by two proposed methods lie in therange of ±30 % compared to that obtained by
the ECG in each state. Here, the LF/HF value
is obtained from area values of the spectrum.
In addition, it is understood that the flash
mental calculation put stress on the person
since the LF/HF value in the stressful state isabout twice larger than that in the relaxedstate. Those results show that the evaluationof the stress can be performed by applying two
proposed methods to the microwave reflectometer signal similar to the ECG.
4.3

Fig.2

Comparison between the microwave reflectometersignal and the ECG signal.

Fig.3

Fig.4

HRV reconfigured by each signal.

FFT spectrum of the HRV in relaxed state.

Stress evaluation: non-static environment

In the second experiment, the evaluation
methods of the stress are applied to a person in
non-static environment. It is not always truethat the stress evaluation is applied to static
environment in any time. In order to search
the applicable scope of the system, the system
is installed in a car. The stress evaluation is
performed during the engine vibration isadded. The heartbeat measurement by using
the microwave reflectometer is applied to theperson who is sitting on a driver’s seat.
The two proposed methods are applied to thereconfiguration of the HRV. Then, the LF/HF
value is calculated by applying the FFT to the

Fig.5

FFT spectrum of the HRV in the stressful state.

HRV for 180 sec. For reference, the ECG data
are measured at the same time in each state
and the LF/HF value is calculated by themethod.
In the non-static environment, the microwave reflectometer signal includes various
noise (spurious) components due to random
movements of the body surface.
They mask the heartbeat signal, and makethe detection of the heartbeat interval difficult.
Figure 6 shows the example of the measurement with such noise components. We inves-平成21年度

九州大学大学院総合理工学報告 第31巻 第4号

tigate whether the evaluation of stress is possible or not even in the non-static environmentby two proposed methods.
Figure 7 shows the frequency spectra of the
HRV reconfigured by two proposed methods
together with those obtained from the ECG
signals. In this case, the calculation of thecross-correlation was repeated three times tomake the peak intervals of the waveform clear[2]. The spectral shapes of the HRV using thethird cross-correlation function and MEM
technique is nearly equal to that by the ECG
and their LF/HFs lie in the range of ±30 %compared to that obtained by the ECG.
Those results show that the evaluation of
the stress can be performed by applying two
proposed methods to microwave reflectometersignals similar to the ECGs even in non-static
environment.

Fig.6

Time evolution of the microwave reflectometersignal.

12

measurement of stress by the cross-correlation
technique.
On the other hand, the HRV can be reconfigured timely by 0.25 sec using the MEM
technique. The MEM technique also enables
the evaluation in only one calculation even inany environment.
If the real-time evaluation of stress is desired, the MEM technique could work better
than the cross-correlation technique. In the
future works, the detection of stress syndromeor sleep prediction will be performed during
driving a car.

5. Conclusions
In conclusion, we have proposed a new signal processing technique for microwave reflectometer sensing that enables the estimation of mental stress from measurement ofbody dynamic motion. The algorithm based on
the MEM is studied to reconfigure the HRV
correctly and automatically from low-S/N microwave reflectometer signals. This techniqueenables the stress evaluation by microwave
reflectometers in any environment, and indicates a possibility of the real-time evaluationof stress toward detection of stress syndromeor sleep prediction in driving a car.
Acknowledgments
This work is partly supported by the
Grant-in-Aid for Scientific Research, the Ministry of Education, Science, Sports and Culture(No. 20360186), and by Grant for Practical
Application of University R&D Results under
the Matching Fund Method, NEDO.

Fig.7

FFT spectra of the HRV reconfigured by the MEM,
the cross-correlation technique and the ECG signal.

4.4

Comparative discussion

We discuss here on the utility of the new
proposed method comparing with the previous
one. In the previous proposed method, the microwave original signal for the first several
tens sec is required at least to improve theaccuracy of the template signal, and which add
the time lag for several tens sec to the time
variation of stress. Additionally, the repetitive
operations of the cross-correlation function are
required in non-static environment as shown
above. These operations append large amountof calculation and complicate the processing.
Thus, it could be difficult to get real-time

References
1) N. Tateishi, A. Mase, L. Bruskin, Y. Kogi, N. Ito, T.
Shirakata, and S. Yoshida, “Microwave Measurementof Heart Beat and Analysis Using Wavelet Transform’’,
Proc. Asia Pacific Microwave Conference, 2151-2153(2007).
2) D. Nagae and A. Mase, “Measurement of Vital Signal
by Microwave Reflectometry and Application to Stress
Evaluation’’, to appear Proc. Asia Pacific Microwave
Conference, (2009).
3) Dennis R. Morgan, Michael G. Zierdt, “Novel signal
processing techniques for Doppler radar cardiopulmonary sensing’’, Signal Processing, 89, 45-66 (2009).
4) Y. Yoshida, K. Yokoyama, N. Ishii, “Real-time Continuous Assessment Method for Mental and Physiological Condition using Heart Rate Variability’’, IEEJ
Transactions on Electronics, Information and Systems,
126-12, 1441-1446 (2006).
5) A. Kagaya, Y. Nakamura, “Regulation of Blood Flow
During Exercise –Present and Future-’’, NAP Limited,Japan (2001) pp. 93-106.