Vital Sign Monitoring of A

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Proceedings of IC-NIDC2014

products of breathing and heart rate are derived by using

Bessel functions and Fourier transformation. An MTIfilter is proposed to cancel out the breathing harmonics.

In Ref. [8] a new algorithm Harmonic Path Algorithm

(HAPA) is proposed which makes use of the

fundamental as well as harmonics of the heart rate signal

to improve the accuracy of heart rate detection. Theauthors in Ref. [9] presented algorithm to detect motion

rate based on energy in frequency for finding the

respiration rate of the target body. In Ref. [10] the

authors have suggested a new method in which more

than one peak are selected in the heart frequency range

and the process is repeated for several iterations and the

peak which is repeated highly is selected as heart rate. In

Ref. [12] an algorithm based on wavelet transform is

proposed for estimation of heart and breathing rates for

continuous monitoring of a patient.

3 Problem statement

In real life, it is not possible for a person to be stationaryfor longer time. Therefore, some method is required todetect motion of the human and still able to calculate thevital signs. The motion may either be caused by thesubject or by the radar. In our work, we have consideredthe radar as stationary while the person may benon-stationary. In next section algorithm formeasurement of vital signs of moving human is

presented and experimental results are also shown toprove the validity of proposed method.

4 Movement detection algorithm

In autocorrelation the signal is matched with its shiftedversion. It shows how fast the signal changes. If thesubject in front of the radar is stationary then the signalchanges very slowly. While if the target is moving thenthe signal varies very fast. The following two graphsshow the autocorrelation of two signals in case ofstationary and non-stationary human.

Figure 1Normalized autocorrelation function of signalreflected from stationary human

Figure 2Normalized autocorrelation function of signalreflected from non-stationary human

The width of the main lobe (centered at zero) isconsidered as the criteria of moment detection. If thewidth of this lobe becomes extremely narrower than thestationary case, then the body is supposed to benon-stationary. Otherwise, the body is considered asstationary. Let be the average width of the main lobein stationary state, is the width when the object ismoving and c is constant which shows the sensitivity ofmotion sensing (here a value of 3 is assigned) then therelation given in following equation is used for momentdetection

(1)

The relation in Eq. (1) means that the width of

autocorrelation function is decreased during motion of

human in front of radar, which means that the signal is

varied fast and thus has less correlation with its shiftedversion in time domain. Fig. 1 show the normalized

autocorrelation function of the signal from the stationary

human whereas in Fig. 2 the person is in motion state. It

is clear that the width of main lobe in Fig. 1 is three time

more wider than width of Fig. 1.

5 Respiration and heart-rate detection

An averaging filter is used to remove the clutter from thereflected signal. The signal is converted to frequencydomain by using Fast Fourier transform. The respirationrate is simply the highest peak among the values in the

spectrum. The heart rate is found by using algorithm inRef. [10]. But before finding the heart and breathingrates, the object is checked whether stationary ornon-stationary. If the object is stationary, then thenrespiration and heart rate are calculated. Otherwise, themost recent value of respiration and heart rate ismaintained, and the movement is continuously checked.When the body comes to stationary position, thenrespiration and heart rates are calculated again. Thefollowing block diagram explains the concept clearly.

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Figure 3 Detection of heart and respiration rate (process

is repeated M times)

The respiration rate is estimated easily by detecting thehighest peak in the range of respiration frequencies i.e.10 to 30 beats per minute. Since the heart rate frequencymay be located close to the harmonics of respirationfrequency and/or the intermodulation products ofrespiration and heart frequencies, therefore, somefiltering technique is required to extract the heart ratefrom the desired range of frequencies. A bank of notchfilters with sharp bandwidth is used to separate the heartrate from the respiration harmonics [10]. N peak valuesare determined in the range of 0.8 to 2 Hz from theresulting spectrum. The process is repeated M times andthe frequency with highest number of repetition andmaximum mean value is selected as heart rate. Thealgorithm is summarized as follows.

Algorithm: Vital Signs detection of non-stationary

human

1. Remove the clutter from the received signal.

2. Find the status of the target, whether it isstationary or moving. If the object is moving,

keep the most recent values of respiration and

heart rate for the period of motion, otherwise,

go to step 3.

3. Transform the signal to frequency domain by

using Fast Fourier Transform (FFT).

4. Detect the highest peak of spectrum as breathing

rate

5. Find the heart rate by using algorithm in [10].

6. Go back to step 2.

6 Experimental results

The breathing rate is measured in case of stationary and

non-stationary subject. The person is sitting at a distance

of approximately 1 meter from the radar. The result of

the breathing rate estimation without movement

detection is shown in Fig. 4. The person is sitting idlein the period of sample 1-50. It moves in period 51-62

and then stops motion. From Fig. 4, it is clear that

without movement detection, it shows the false

breathing rate during motion period.

Figure 4 Respiration rate values of a non-stationary

human without motion detection

In Fig. 5, the movement is detected at sample 51 and the

value for breathing rate at 50 is kept constant until the

motion period is ended. After the motion period is

finished, it again starts measurement of respiration rate

as according to the algorithm. It shows better result as

compared to Fig. 4.

Figure 5 Respiration rate values of a non-stationary

human with motion detection

In the second experiment, the heart rate is estimated for

a non-stationary person. In Fig. 6, the stationary period

starts from sample 1-75. At sample 75, the person starts

motion which result in wrong estimation of heart rate as

it is clear from Fig. 6.

Clutter Removal

Movement

Detection

Frequency

domain

transformation

Select highest

peak as

respiration

frequency

N peaks in

range of 0.8 to

2 Hz

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Figure 6 Heart beat rate values of a non-stationaryhuman without motion detection

In Fig. 7, the motion is detected at sample 76, and the

value of heart rate at sample 75 is retained for the whole

motion period. Therefore, it result in better estimation as

compared to Fig. 6.

Figure 7 Heart beat rate values of a non-stationaryhuman with motion detection

From the above experimental results, it is clear that if we

observe the motion of body in each iteration before

measuring the vital signs and instead of wrong detection

of respiration and heart rates, it is better to keep the most

recent measurement value (before the motion starts) for

the period of motion until the object come back to its

stationary position.

7 Conclusions and future work

The measurement of vital signs for non-stationary

human is discussed. The autocorrelation concept is used

to detect the motion of target. If the signal is changing

fast in the time domain and the width of the main lobe

decreases then motion is detected and the most recent

values of respiration and heart rates are maintained

during motion period. However, the algorithm doesnt

calculate the vital signs during the motion period (it only

maintains the most recent measurement value before the

motion period as the values throughout the motion

period) and waits until the object becomes stationary.

Therefore, this algorithm is working in circumstances

where the patient moves for a short duration of time

while the steady state of patient is stationary. Our futuregoal is that instead of avoiding the vital signs in motion

period and waiting for the patient to come into rest

position, we will focus on determining the vital signs

during the motion period.

Acknowledgements

This research was supported by the MSIP (Ministry of Science,

ICT&Future Planning), Korea, under the ITRC (InformationTechnology Research Center) support program (NIPA-2014-H0301-14-1017) supervised by the NIPA (National IT IndustryPromotion Agency)

References

[1] Di Lena, Luigi. "Vital signs monitoring using Ultra Wide

Band pulse radar." (2010).[2] A. Lazaro, D. Girbau, and R. Villarino, "Analysis of vital

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[3] Staderini, Enrico M. "UWB radars inmedicine." Aerospace and Electronic Systems Magazine,IEEE 17.1 (2002): 13-18.

[4] McEwan, Thomas E. "Body monitoring and imaging

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Jun. 1998.[5] Bilich, Carlos G. "Bio-medical sensing using ultra

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[6] M.Y.W.Chia, S.W.Leong, C.K.Sim, K.M.Chan,Through-wall UWB radar operating within FCCsmask for sensing heart beat and breathing rate, IEEE

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"Harmonic Path (HAPA) algorithm for non-contact vitalsigns monitoring with IR-UWB radar."Biomedical

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[9] Sharafi, Azadeh, Mehran Baboli, and Mohammad Eshghi."A new algorithm for detection motion rate based on

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[10] Faheem Khan, Jeong Woo Choi and Sung Ho Cho.Design issues in vital sign monitoring through IR-UWBradar. To be published in ISCE 2014, Korea.

[11] Win, Moe Z., and Robert A. Scholtz. "Impulse radio:

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[12] Baboli, Mehran, et al. "A new wavelet based algorithmfor estimating respiratory motion rate using UWB

radar." Biomedical and Pharmaceutical Engineering,2009. ICBPE'09. International Conference on. IEEE,2009.

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Vital Sign Monitoring of A