Stable Measurement of Blood Flow While Running using a Micro

Blood Flowmeter

Wataru Iwasaki

, Masaki Nakamura

, Takeshi Gotanda

, Satoshi Takeuchi

, Masutaka Furue

Eiji Higurashi

and Renshi Sawada

1, 4

Graduate School of Systemus Life Sciences, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-city, Japan

Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka-city, Japan

Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1,

Komaba, Meguro-ku, Tokyo, Japan

Department of Mechanical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka-city, Japan

Keywords: Blood Flowmeter, Laser Doppler, Wearable Sensor, MEMS, Exercise.

Abstract: Skin blood flow during exercise has been studied before, with measurements made using laser Doppler

blood flowmeters; however, their use was limited to activities with minimal motion, such as riding bicycle

ergometers, because conventional devices are large and their measurements easily altered by movements of

the optical fiber, rendering them inappropriate for running. We have previously developed a micro

integrated laser Doppler blood flowmeter using microelectromechanical systems (MEMS) technology. The

micro blood flowmeter is wearable and can measure signal stably even while the wearer is moving. We

monitored skin blood flow during running at velocities of 6 km/h, 8 km/h, and 10 km/h, and were successful

in measuring a stable signal under these conditions. We found that at the forehead the skin blood flow

increases and, in contrast, at the fingertip it initially decreases during running. We also found that the level

of these increases and decreases correlated with the running velocity.

1 INTRODUCTION

Recently, more people have started running. The

Sasakawa Sports Foundation reported that the

number of people who run more than twice a week

has doubled in the last ten years in Japan (2010), and

the number of marathon completions is up more than

1.6-fold from ten years ago in the United States

(Mathews et al., 2012). Runners, especially those

with hypertrophie cardiomyopathy, should be aware

of the possibility for harm or even death due to

exercise-induced issues such as cardiopulmonary

arrest (Maron et al., 1996). Therefore it’s necessary

to observe health index of those person while

running.

Many studies on the physiological effects of

exercise have been conducted using laser Doppler

blood flowmeters, which can noninvasively measure

skin blood flow (Kellogg Jr. et al., 1993, Yasuda et

al., 1994. Alonso et al., 1998). Many varieties of

these blood flowmeters have been developed

(Watkins and Holloway, 1978, Wunderlich et al.,

1980), yet they face several challenges: for example,

their large size and the high power consumption

necessary for monitoring blood flow while subjects

run. Furthermore, such instruments are easily

affected by movement or vibration of the optical

fiber in the devices, which is also used as a strain

sensor (Newson et al., 1987). Therefore, laser

Doppler blood flowmeters were primarily used in

experiments having little subject motion, such as

with stationary bicycles, rather than running.

To meet and overcome these challenges, a laser

Doppler blood flowmeter that didn’t require the use

of optical fibers was developed (Mul et al., 1984).

We have also previously applied

microelectromechanical systems (MEMS)

technology to a blood-flow-measuring instrument,

and developed a micro integrated laser Doppler

blood flowmeter which is called MEMS blood flow

sensor (Higurashi et al., 2003). The MEMS blood

flow sensor can measure blood flow while the

subject is moving because it doesn’t use an optical

fiber. We have also developed a probe for the

MEMS blood flow sensor that consumes lower

Iwasaki W., Nakamura M., Gotanda T., Takeuchi S., Furue M., Higurashi E. and Sawada R..

Stable Measurement of Blood Flow while Running Using a Micro Blood Flowmeter.

DOI: 10.5220/0004196600300037

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 30-37

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

electric power using a built-in silicon microlens

(Kimura et al., 2010), and have used it to evaluate

dehydration (Nogami et al., 2011), alcohol

consumption (Iwasaki et al., 2012), and systemic

sclerosis (Kido et al., 2007). This probe’s design

enables wafer-level packaging, improving its

fabrication efficiency. Our MEMS blood flow sensor

with this newly developed probe is the world’s

smallest, lightest, and least power-consuming blood

flow sensor.

In the present study, we attempted to measure

blood flow using our developed MEMS blood flow

sensor at the tip of the ring finger, the forehead, and

the earlobe while participants ran. Blood flow was

measured at three different running velocities (6, 8,

and 10km/h) for 30 minutes each on a treadmill. We

evaluated the blood flow at each measurement site

and then compared blood flow with running

velocity.

2 MATERIALS AND METHODS

In this section, we describe the subjects, the MEMS

blood flow sensor, the experimental apparatus, and

the experimental protocol of the study.

Figure 1: Schematic of laser Doppler flowmetry.

2.1 Subjects

The research complied with the ethical standards of

the Human Experimentation Committee of Kyushu

University, and with the 2008 revision of the

Helsinki Declaration. Seven healthy young men

(mean age of 23.4 years old, range 22-26; mean BMI

22.9 kg/m2, range 20.4-29.5) participated in this

study. Subjects were fully informed of the

experimental details and any potential risks before

their participation in the study.

2.2 MEMS Blood Flow Sensor

The MEMS blood flow sensor can measure skin

blood flow noninvasively using the principles of

laser Doppler flowmetry. We explain these

principles in the first subsection, and lay out the

structure of the device in the second, and describe

the components of the blood flow signal in the third.

2.2.1 Principles

Figure 1 illustrates the principles behind laser

Doppler flowmetry. A laser beam with frequency f is

emitted from a light source to the skin and penetrates

it to a certain depth. Some light scatters from static

tissue such as the skin and blood vessels; a small

amount of light is scattered from moving tissue such

as red blood cells. Because of the Doppler effect, the

frequency of the light scattered by the moving

tissues changes slightly. The Doppler-shifted and

non Doppler-shifted light interfere on the photo

diode (PD), which detects the resultant intensity

modulations. The first moment of the power

spectrum of the beat frequency distribution as

obtained using a fast Fourier transform (FFT) is

proportional to the product of the average velocity

and patient-specific concentration of the red blood

cells as they move the body (Bonner and Nossal,

1981).

2.2.2 Structure

The MEMS blood flow sensor contains a probe and

a main body, as shown in Figure 2. The probe has an

optical MEMS chip and first-stage operational

amplifiers, and is attached directly to the subject.

The main sensor body has a digital signal processor

(DSP), a Bluetooth wireless link, display, and

battery. The MEMS blood flow sensor can be run on

a 3.7 V square electric battery, as is used in cell

phones.

Figure 3 shows a schematic of the optical MEMS

chip we developed. We used distributed feedback

laser diode (DFB-LD) as a light source of MEMS

blood flow sensor. Its wavelength is 1310 nm which

easily penetrates human skin. The laser beam

emitted from the DFB-LD is reflected vertically by

the <111> facet etched mirror, focused by the

microlens, and irradiated to the skin. The microlens

decreases coupling loss, a serious problem in

conventional instruments. The laser power coming

from the probe is 2 mW. The laser doesn’t harm to

human body, because it is not perfectly collimated.

Scattered light reenters through the window and is

detected by the PD.

2.2.3 Blood Flow Signal

Figure 4 shows the probes of our MEMS blood flow

sensor and a conventional, optical fiber-type

StableMeasurementofBloodFlowwhileRunningUsingaMicroBloodFlowmeter

instrument attached to adjacent fingertips for

comparison, and Figure 5 shows the blood flow

signals measured by each. The hand was waved

from the 10-second mark. It is apparent that the

signal of the fiber-type instrument was greatly

affected, while in contrast, the signal of our MEMS

blood flow sensor was stable.

Figure 2: Photograph of MEMS blood flow sensor.

Figure 3: Schematic of cross-sectional view of the optical

MEMS chip.

Figure 4: Photograph of MEMS blood flow sensor and

fiber-type instrument probes attached to fingertips.

There are three main components of blood flow

waves: basic, cardiac, and reflex waves (Kano et al.,

1993). Basic waves have periods of about ten

seconds, and some researchers have reported that

these large oscillations are modulated by the

sympathetic nervous system or by sympathetic

vasomotor tone (Sasano et al., 1999); (Mukae et al.,

2006). Cardiac waves are much smaller and simply

reflect the heartbeat. Reflex waves are a

phenomenon in which peripheral blood flow

temporarily yet markedly decreases when, for

example, the subject takes a deep breath or is

engaging in physical activity. We should note that

reflex waves only appear in palmoplantar areas.

Figure 5: Fingertip blood flow signals measured by the

MEMS blood flow sensor and fiber-type instrument

during hand-waving.

2.3 Experimental Apparatus

Participants ran on Tempo T931 treadmills (Johnson

Health Tech. Co., Ltd., Taiwan). MEMS blood flow

sensors were attached with double-side tape and

medical tape to the tip of the left ring finger, the left

earlobe, and the forehead (using a headband) as

shown in Figure 6. In addition to blood flow, we

measured body temperature, blood pressure, blood

lactate level, and body weight. Body temperature

was measured with an MC-510 ear thermometer

(OMRON HEALTHCARE Co., Ltd., Japan), which

can measure body temperature within one second

and can well perform even while running. Blood

pressure was measured with an upper-arm type

sphygmomanometer.

Figure 6: Photograph of subject with attached MEMS

blood flow sensor.

Blood lactate level was measured with the

Lactate Pro LT-1710 meter (ARKRAY, Inc., Japan),

which can measure blood lactate levels from only 5

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

μl of blood. We obtained blood samples using the

Naturalet EZ (ARKRAY, Inc., Japan), a device that

can easily obtain small blood samples with minimal

pain. We measured blood pressure and lactate levels

before and after running, because we couldn’t

measure them while the subject ran. Body weight

was measured before and after the experiment.

Figure 7: Experimental protocol.

2.4 Experimental Protocol

We measured blood flow at three different running

velocities (6, 8, and 10 km/h) for 30 minutes after

walking for 5 minutes at 4 km/h as a warm-up.

Subjects ran each velocity on different days with

intervals of more than three days between sessions,

to counteract fatigue from the previous experiment.

We observed blood flow when sitting for 40 minutes

after running to observe the recovery of blood flow.

If subjects complained of exhaustion while running,

we stopped the running session but continued to

observe blood flow for 40 minutes.

Figure 7 shows the experimental protocol. At the

beginning of a session, we measured resting blood

flow, body temperature, blood pressure, and blood

lactate levels while the subject sat. Then, the subject

walked on the treadmill at 4 km/h for 5 minutes.

Body temperature was measured 4 minutes after

walking onset. The subject ran at a constant velocity

for 30 minutes after the warm-up. Body temperature

was measured at 5, 10, 15, 20, 25, and 29 minutes of

running. We observed blood flow while sitting for

40 minutes. Body temperature, blood pressure, and

blood lactate levels were all measured just after, 20

minutes after, and 40 minutes after running

concluded.

3 RESULTS

We describe how the stability of blood flow signals

differ among measurement sites in the first

subsection; how heart rate is isolated from the blood

flow signal in the second subsection; and how blood

flow changes with velocity in the final subsection.

3.1 Stabilities of Blood Flow Signals in

each Measurement Site

Figure 8 shows the experimental data for a

representative subject. The blood flow signals

corresponding to sensor location are displayed. The

data was sampled at 50 Hz, and the values displayed

were averaged every 100 points, and plotted at 2-

second intervals for clarity. The three signals had

different values in the resting states because density

of capillary differs across measurement sites and

because of individual variability in MEMS blood

flow sensor calibration. Blood flow at the earlobe

suddenly and greatly increased upon the start of

running, indicating that the sensor is very sensitive

to subject movement at this location. Blood flow at

the forehead gradually increased as the subject

began running and gradually decreased after subject

had stopped. On the other hand, blood flow at the

fingertip decreased at the start of running and

gradually increased once it had ended. There were

many reflex waves observed in fingertip blood flow

during running. We could confirm these changes of

blood flow in all subjects.

Figure 8: Blood flow signals during running.

Figure 9: Spectrum of fingertip blood flow at 10 minutes

and 20 minutes after start of running.

StableMeasurementofBloodFlowwhileRunningUsingaMicroBloodFlowmeter

Figure 10: Average changes in blood flow (n=7) at forehead (a) and at fingertip (b), heart rate (c), and body temperature (d)

during the running experiment; standard deviations not shown for clarity.

3.2 Separation of Heart Beat from

Rhythm of Running

When subjects weren’t exercising, we could usually

see a single spectral peak around 1 Hz, which was

caused by heart rate. However we observed some

additional spectrum peaks during running. Figure 9

shows a spectrum distribution of blood flow at the

forehead at 10 and 20 minutes after the

commencement of running at 8 km/h. These spectra

were obtained by performing FFTs on blood flow

signal data lasting 40.96 seconds (2048 points).

Most spectral peaks remained unchanged between

samples, staying at a stable rhythmic frequency due

to running at constant velocity. However, one peak

did shift, suggesting it was tracking heart rate,

increasing during running. We can determine heart

rate by performing a FFT on a power spectrum of

blood flow data, tracking the rhythmic running

peaks as a constant, and observing the incremental

increase of the peak corresponding to heart rate: this

is possible even while running.

3.3 Changes of Blood Flow with

Running Velocity

Mean (n=7) blood flow at the forehead and at the

fingertip, heart rate, and body temperature at each

running velocity are shown in Figures 10(a), 10(b),

10(c), and 10(d), respectively. All subjects

completed the 30 minutes of running in the 6 km/h

and 8 km/h sessions. In the 10 km/h session, one

subject retired after 15 minutes, and another after 20;

all other subjects completed the exercise. Blood flow

at the forehead gradually increased during running

for each velocity, with the 10 km/h session yielding

the highest. On the other hand, blood flow at the

fingertip of each velocity suddenly decreased then

gradually rose during running for each velocity, with

the 10 km/h session yielding the slowest increase.

Mean body temperature rose to over 37.0 °C in the

later stages of the 10 km/h running session.

Systolic and diastolic blood pressure, blood

lactate level, and body weight data are shown in

Table 1. The “Baseline” column contains the pre-

warm-up data, and the “35 min”, “55 min”, and “75

min” columns contain values obtained just after, 20

minutes after, and 40 minutes after running,

respectively. Differences between the baseline value

and the values at each time point were evaluated by

paired t-test, p<0.05 was considered statistically

significant. Systolic blood pressure showed a

statistically significant increase from baseline just

after running for each running velocity. Diastolic

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

Table 1: Systolic and diastolic blood pressure, blood lactate level, and body weight at each measurement time.

Baseline 35 min 55 min 75 min

Systolic blood

pressure

(mmHg)

6 km/h 114.6 ± 9.9 136.0 ± 11.3 ** 113.7 ± 10.4 113.6 ± 14.1

8 km/h 118.0 ± 8.6 133.3 ± 12.7 * 115.3 ± 10.5 116.6 ± 7.0

10 km/h 116.7 ± 13.9 147.6 ± 14.3 ** 119.1 ± 16.5 114.1 ± 13.1

Diastolic blood

pressure

(mmHg)

6 km/h 66.4 ± 8.6 74.3 ± 5.6 ** 68.4 ± 7.9 71.7 ± 7.7

8 km/h 67.0 ± 9.1 67.4 ± 7.3 68.6 ± 9.7 70.7 ± 6.0

10 km/h 68.0 ± 8.3 67.7 ± 4.8 66.6 ± 11.2 67.0 ± 8.5

Blood lactate

level

(mmol/L)

6 km/h 3.06 ± 2.09 4.97 ± 4.20 4.07 ± 4.51 2.76 ± 2.08

8 km/h 1.84 ± 0.44 5.23 ± 6.88 3.10 ± 3.50 2.57 ± 2.35

10 km/h 2.09 ± 1.48 7.11 ± 4.46 * 3.60 ± 2.35 2.40 ± 1.23

Body weight

(kg)

6 km/h 67.2 ± 9.1 - - 66.8 ± 9.1 **

8 km/h 68.1 ± 9.6 - - 67.5 ± 9.4 **

10 km/h 65.2 ± 4.7 - - 64.6 ± 4.6 **

Baseline= pre-warm-up; 35 min = just after running; 55 min = 20 minutes after running; 75 min = 40 minutes after running.

Differences between the Baseline values and the values at each time point were evaluated by paired t-test (*: p<0.05, **: p < 0.01).

blood pressure significantly increased from baseline

just after running only for when running velocity

was 6 km/h. Blood lactate level was significantly

higher than baseline only when subjects ran at 10

km/h. Body weight significantly decreased after

running for all velocities due to sweating, with the

dehydration amount reaching almost 1 percent of

body weight.

4 DISCUSSION

In this study, we succeeded in stably measuring

blood flow in running subjects using our previously

developed MEMS blood flow sensor. We found that

blood flow at the fingertips decreases just after

running commences and then gradually increases

during continuous running. Conversely, blood flow

at the forehead and earlobe showed a sudden rise

just after the start of running, gradually increasing

during running. Moreover, we found that blood flow

at the forehead increased the most when subjects ran

at 10 km/h, the only velocity at which mean lactate

level was significantly higher than baseline, and at

which some subjects complained of exhaustion and

retired. These increases in blood flow most likely

function to lower the body temperature, helping with

sweating to release heat. When subjects ran at 10

km/h, although compensatory blood flow increases

were large, body temperature still increased to over

37 °C; and, perhaps because the release of body heat

could not catch up with the exercise-generated

hyperthermia, some subjects couldn’t complete the

full time. In contrast to this, blood flow at the

fingertip actually decreased. Blood flow at fingertip

was likely decreased by neural vasoconstriction, or

because muscle tissue was given priority of blood

supply over peripheral perfusion at the beginning of

running. Blood flow was subsequently increased in

order to lower body temperature.

The MEMS blood flow sensor can measure

blood flow during running, but it also captures noise

caused by running motion. Some spectral waves

with the same frequency as running rhythm were

observed in a power spectrum distribution of blood

flow signal. This noise is likely due to shearing of

the skin caused by the running motion. When such

shearing occurs, blood vessels move relative to the

MEMS blood flow sensor, which then captures the

relative, shifted movement (Figure 11). The blood

flow signal at the earlobe, where sensors are difficult

to affix and the skin is soft and easily sheared, was

strongly affected by motion. In contrast, the blood

flow signal at the fingertip and forehead, where a

solid hold was made possible by medical tape and

headbands, were only slightly affected by the

running rhythm. Even then we could eliminate the

waves by monitoring the spectral pattern change

from moment to moment, and treating the pace as

constant. Therefore, we think the fingertip and the

forehead are suitable measurement sites for blood

StableMeasurementofBloodFlowwhileRunningUsingaMicroBloodFlowmeter

flow during running or other activities with

significant motion. We may also even be able to

eliminate the shearing noise at the earlobe, by

filtering it from blood flow signals using a FFT.

Figure 11: Schematic of blood flow measurement when a

shearing occurs.

In conclusion, we have succeeded in stably

measuring blood flow during running using the

MEMS blood flow sensor. The device also captures

the noise caused by running motion; however, we

can eliminate this and obtain the heart rate by

performing a FFT on blood flow power spectra,

cancelling out the steady rhythm of the pace, and

observing for an incremental frequency shift in the

spectrum peak that corresponds to heart rate. The

MEMS blood flow sensor would contribute to

researches in areas like sports science or health

science. Moreover, we found that the extent of the

observed changes to blood flow depend on the

intensity of exercise. These results suggest that the

MEMS blood flow sensor has potential as a new

portable device for detecting the running intensity,

and alerting runners to any dangers from excessive

exercise by detecting substantially increased or

decreased blood flow and heart rate.

ACKNOWLEDGEMENTS

We would like to express our special thanks to the

volunteers who participated in the study. We also

would like to thank Mr. Kazuto Mishima of

Vinciamo Incorporated and Mr. Takaaki Miyahara

of NEXIS Incorporated for the integration of the

MEMS blood flow sensor. This work was partially

supported by research grants from the Ministry of

Education, Culture, Sports, Science and Technology

of Japan.

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StableMeasurementofBloodFlowwhileRunningUsingaMicroBloodFlowmeter