Wearable Motion Tolerant PPG Sensor for Instant Heart Rate in

Daily Activity

Takanori Ishikawa

, Yasuhide Hyodo

, Ken Miyashita

, Kazunari Yoshifuji

Yota Komoriya

and Yutaka Imai

Interface Device Development Dept., UI Device Development Div., Device & Material R&D Group, R&D Platform,

Sony Corporation, 4-14-1 Asahi-cho, 243-0014, Atsugi-shi, Kanagawa, Japan

Intelligent Application Technology Development Dept., Application Technology Development Div., System R&D Group,

R&D Platform, Sony Corporation, 2-10-1 Osaki, 141-8610, Shinagawa-ku, Tokyo, Japan

Keywords: PPG Sensor, Motion Artifact Cancellation Framework, Heart Rate Variability, Daily Activity, Ambulatory.

Abstract: A wristband-type PPG heart rate sensor capable of overcoming motion artifacts in daily activity and

detecting heart rate variability has been developed together with a motion artifact cancellation framework.

In this work, a motion artifact model in daily life was derived and motion artifacts caused by activity of arm,

finger, and wrist were cancelled significantly. Highly reliable instant heart rate detection with high noise-

resistance was achieved from noise-reduced pulse signals based on peak-detection and autocorrelation

methods. The wristband-type PPG heart rate sensor with our motion artifact cancellation framework was

compared with ECG instant heart rate measurement in both laboratory and office environments. In a

laboratory environment, mean reliability (percentage of time within 10% error relative to ECG instant heart

rate) was 86.5% and the one-day pulse-accuracy achievement rate based on time use data of body motions

in daily life was 88.1% or approximately 21 hours. Our device and motion artifact cancellation framework

enable continuous heart rate variability monitoring in daily life and could be applied to heart rate variability

analysis and emotion recognition.

1 INTRODUCTION

A variety of applications using wristband-type,

photoplethysmography (PPG)-based heart rate

sensors have been proposed in recent years. Their

typical applications are heart rate monitoring in a

resting state for healthcare, feedback to efficient

training methods in walking and running state as

well as estimation of consumed calories. These

applications are based on averaged heart rate to

reduce motion artifact.

Though accurate monitoring of heart rate

variability (HRV) has not yet been achieved in

motion state, once it is achieved, it will open a

variety of applications including like music

recommendation based on user affective state, stress

monitoring and cardiac insufficiency detection

(Wijsman, 2013); (Shin, 2014); (Venema, 2015).

In comparison with chest-strap-type wearable

electrocardiography (ECG) monitors, wristband-type

PPG heart rate sensors can be comfortably worn

without placing a burden on the user, which gives

them a user-friendly advantage. On the other hand,

wristband-type PPG sensors suffer from a

superimposing of pseudo pulse signals (motion

artifacts) caused by user motion, which makes

accurate calculation of heart rate difficult (Tamura,

2014). Consequently, to accurately calculate pulse

rate from pulse signals superimposed with motion

artifacts, various types of motion artifact reduction

methods have been proposed to improve accuracy of

HRV from PPG raw data(Renevey, 2001); (Asada,

2004), and there have been many studies targeting

periodic motion artifacts caused by intense arm

motion while walking or running. So far many

studies have reported evaluation average pulse rate

using average heart rate by ECG as reference.

Focusing on motion in daily life, we have to take

into account random motion of arm, finger and wrist.

An experimental protocol was proposed that

includes activity of both arm, finger and wrist (Parak,

2014); (Binsch, 2016); (Tăuţan, 2015), but their

study was limited to evaluation and analysis of

motion artifact.

126

Ishikawa T., Hyodo Y., Miyashita K., Yoshifuji K., Komoriya Y. and Imai Y.

Wearable Motion Tolerant PPG Sensor for Instant Heart Rate in Daily Activity.

DOI: 10.5220/0006109901260133

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 126-133

ISBN: 978-989-758-212-7

In this paper, we report on a motion artifact

cancellation framework that we have developed

based on the results of analyzing arm and finger

motion artifacts assuming motions in daily life. We

also report on laboratory and ambulatory evaluation

of this framework using instant heart rate by ECG as

reference with our design of experimental protocols

simulating motions in daily life.

2 PROBLEM STATEMENT

2.1 Photoplethysmography

The reflective PPG method measures changes in

blood flow in subcutaneously distributed capillaries

by injecting light from a light source into the skin

and measuring the intensity of returning light by a

receiver after absorption and diffusion by blood flow

and skin tissue several mm under the skin (Renevey,

2001).















∙









∙



(1)

Here, 



is the intensity of the light incident on the

skin, 









is the temporal change in diffusion

and absorption by hemoglobin, etc. in capillary

blood flow, 



is the amount of diffusion and

absorption by body tissue, and 









is the intensity

of reflected light.

2.2 Motion Artifact

There are activities of arm, finger and wrist in daily

life affecting wristband-type PPG sensors. In the

case of finger and wrist motions, the muscles under

the wrist move causing the state of blood flow in

subcutaneous body tissue to change. This would

mean that 



in Eq. (1) is not fixed. We

therefore performed an analysis of motion artifacts

caused by arm and finger motion in the following

way.

While the thickness of each layer under the skin

differs by site, age, etc., body tissue lies at a depth of

1.0 – 2.0 mm (Boucsein, 2012). To measure the state

of blood flow in subcutaneous tissue in a non-

invasive manner, we adopted the PPG method.

Taking into account the subcutaneous penetration of

light (Bashkatov, 2005) and the optical absorption

and diffusion coefficients of hemoglobin, we

determined an optimal wavelength as follows. First,

we analyzed the optical wavelength dependency of

the signal-to-noise ratio (S/N) by measuring the

pulse signal when bending and stretching the index

finger periodically (2 Hz) and performing a

frequency analysis. In calculating S/N, we took the

heart-rate band as the signal band and defined the

finger-motion frequency band as the noise band.

Table 1: LED wavelength dependency of S/N when

bending and stretching the index finger periodically.

Table 1 lists the wavelength dependency of S/N

when bending and stretching the index finger

periodically as measured with three subjects. The

wavelengths used in this analysis are those of easily

obtainable LEDs. These results show that S/N is

smallest for a wavelength of 630 nm, so we chose it

to be the optical wavelength of the PPG signal that

reflects finger motion well.

However, for the pulse signal, we chose a

wavelength of 530 nm to measure the state of blood

flow in the capillaries of the dermic layer that is not

easily affected by body tissue (Faber, 2004); (Lee,

2013). Here, we simultaneously measured the arm’s

acceleration signal by having a 3-axis acceleration

sensor worn on the wrist.

Figure 2 (A) shows the results of a PPG (green)

and acceleration frequency analysis when moving

the arm back and forth with a 2 Hz period in a

standing position. Strong peaks are observed in the

PPG (green) spectral distribution near the arm-

moving frequency of 120 [bpm] and the heartbeat

frequency of 100 [bpm]. This shows that the arm-

related motion artifact is superimposed as a pseudo

pulse signal. Since a motion artifact is easily

superimposed when blood is flowing, it is thought

that body motion affects blood flow and a pseudo

pulse signal arises due to pseudo blood flow.

Figure 2 (B) shows the results of a PPG (green)

and a PPG (red) frequency analysis when bending

and stretching the index finger periodically at 1.5 Hz

while keeping the arm fixed in a sitting position.

Although the acceleration sensor measured no finger

motion, a strong peak is observed near the finger-

moving frequency of 90 [bpm] in the PPG (green)

and PPG (red) spectral distributions. In particular,

the PPG (red) spectral distribution shows that the

spectral intensity at the motion artifact frequency of

90 [bpm] is stronger than that at the heartbeat

frequency of 60 [bpm] compared with PPG (green).

This is thought to be that the hemoglobin diffusion

coefficient is smaller and the degree of subcutaneous

penetration deeper at 630 nm than at 530 nm.

Therefore, we suppose that intensity of returning

Subject 470nm 530nm 630nm 860nm 940nm

#1 1.90 7.75 -7.22 -5.66 -5.75

#2 2.25 7.61 -18.30 -10.20 -15.47

#3 7.72 4.67 -13.02 -7.95 2.83

Wearable Motion Tolerant PPG Sensor for Instant Heart Rate in Daily Activity

127

light from skin is smaller compares with wavelength

of 530nm. Furthermore, we have observed that

motion artifact caused by wrist motion has same

characteristic in frequency analysis.

Figure 3 shows the results of measuring PPG (green)

and acceleration signal in the fingertip direction

when moving the arm up and down relative to the

heart in a standing position. When the arm is moved

above the heart, the total amount of blood flow

decreases due to the effect of gravity. Therefore,

baseline of the intensity of return light decreases.

Figure 3: Up-and-down arm motion relative to heart.

Upper: PPG (green), lower: acceleration signal.

Conversely, when moving the arm below the heart,

it can be seen that this baseline increases. Since the

baseline stabilizes in approximately two seconds

after lifting the arm, noise frequency is assumed to

be under 0.5 Hz. The above experimental results

show that the intensity of return light from skin is

affected by activity of arm, finger and wrist. We can

therefore extend Eq. (1) as follows.















∙









∙









∙











∙









(2)

Here, 









is the amount of optical absorption

and diffusion in body tissue due to finger motion,











is the amount of optical absorption and

diffusion due to changes in blood flow caused by

arm motion, and 









is the amount of optical

absorption and diffusion due to the arm’s up or

down orientation. If we now take the logarithm of

both sides of this equation, Eq. (2) can be rewritten

as follows.















































(3)

Here, 







is the pulse signal, 









is the noise

signal due to finger motion, 









is the noise signal

due to arm motion, and 









is the noise signal due

to arm orientation. In other words, this equation

shows that all motion artifacts in the PPG method

are superimposed on the observed signal.

(A) When moving the arm back and forth at 2 Hz

(B) When bending and stretching the index finger at 1.5

Figure 2: Results of frequency analysis. (A) upper: PPG

(green), lower: acceleration signal (Y-axis means fingertip

direction); (B) upper: PPG (green), lower: PPG (red).

pulse

down

Pulserate

Motionartifact

Pulserate

Motionartifact

Pulserate

BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing

128

3 PROPOSED METHOD

A motion artifact cancellation framework based on

Eq. (3) that assumes an additive model for arm and

finger motion artifacts is shown in Figure 4.

Figure 4: Schematic diagram of motion artifac

cancellation framework.

To first step in this framework is to separate the

motion artifact due to the orientation of the arm

using a band-pass filter (BPF). Here, the pass band is

set to [0.5, 5] Hz taking into account the arm-

orientation noise band and the heart-rate frequency

band.

The next step is to separate arm-motion noise by

an adaptive filter using the acceleration signal as a

reference signal. It is important in noise reduction

processing using an adaptive filter to select a

reference signal having high correlation with the

noise signal superimposed on the observed signal

(Asada, 2004). For this reason, we modelled

beforehand the transfer function to body-motion

blood flow as a finite impulse response (FIR) filter

through system identification. The signal resulting

from the convolution of the acceleration signal with

the FIR filter is used as a reference signal for arm-

motion noise reduction processing.

Similarly, finger-motion noise is separated using

a reference signal. Here, we use the PPG signal as a

reference signal to make non-invasive measurements

of the state of blood flow in body tissue. We decided

that an optical wavelength is 630 nm based on

previous analysis results in Table 1.

However, a pulse signal component is also

included in PPG (red). To therefore make correlation

with the noise signal even higher, we use a BPF to

weaken the pulse signal component based on the

finally estimated heart rate. Furthermore, assuming

use in ambulatory environment and knowing that

noise will also be generated by changes in contact

pressure caused by deformation of the arm’s shape,

we also use a BPF to reduce this noise.

Next is the calculation of the instant pulse rate

from the pulse signal reduced of motion artifacts.

Although peak detection is applied here to calculate

the instant pulse rate, erroneous detections can easily

occur due to the effects of residual noise. To deal

with this noise, we introduce pulse rate detection by

an autocorrelation function exploiting the periodicity

of the pulse signal. Pulse rate calculation by an

autocorrelation function calculates the inter-beat-

interval (IBI) from the lag at which the correlation

function becomes maximum. However, while the

autocorrelation method is highly robust to noise, its

assumption of periodicity in the pulse signal can

result in lower accuracy than the peak detection

method if pulse variability are present. For this

reason, the integrated processing section in this

framework first calculates the instant pulse rate by

both the peak detection method and autocorrelation

method and the reliability of each result. It then

outputs the optimal instant pulse rate based on the

pulse signal and body motion information analyzed

from the acceleration signal.

4 EXPERIMENTAL VALIDATION

4.1 Heart Rate Sensor Prototype

Our prototype for a wristband PPG heart rate sensor

is shown in Figure 5. This device performs pulse

signal measurement by irradiating the human body

with green LED light and measuring reflected light

with a photodetector. It also performs reference-

signal measurement in the finger-motion noise-

reduction process by measuring reflected light from

a red LED likewise with a photodetector. In addition,

it obtains a reference signal in the arm-motion noise-

reduction process by measuring arm acceleration

with a 3-axis acceleration sensor built into the sensor

enclosure. The pulse signal obtained from measured

green-LED and red-LED light and the acceleration

signal are recorded in built-in eMMC flash storage.

The sampling frequency is 128 Hz for all sensors.

Wearable Motion Tolerant PPG Sensor for Instant Heart Rate in Daily Activity

129

The device incorporates a real-time clock (RTC) that

can be synchronized with a host PC and recorded

together with measured data.

Figure 5: Prototype of wristband type heart rate sensor.

4.2 Subjects

The experiment was conducted with 12 subjects

recruited from male employees in the workplace.

After obtaining approval from the Sony Life Ethics

Committee, the subjects were briefed about the

contents of this research project and their consent

was obtained in writing.

Table 2: Subjects.

4.3 Laboratory Protocol and

Evaluation

Evaluating motion artifacts in daily life requires

knowledge of user lifestyle for users wearing a

wristband-type device. Although there are various

types of user lifestyles, activity time use by types of

behavior for one week can be summarized as

follows according to material from the Statistics

Bureau of the Ministry of Internal Affairs and

Communications (MIC) (Ministry of Internal Affairs

and Communications, 2011). However, an

evaluation based on protocol that mimics these

activities as-is puts an unrealistic burden on subjects,

so we designed original protocols after reclassifying

activities into three main states of body motion.

These are (1) active state featuring periodic arm

motion of fixed intensity as in walking when

commuting to school or work or engaging in sports

(such as jogging or running), (2) semi-resting state

featuring non-periodic arm motion of random

intensity and finger activity as in schoolwork, work,

housework, meals, etc. and (3) resting state as in

sleeping. The results of reorganizing the above

activities are listed on Table 4.

Specifically, we designed three types of protocols

based on the above results of reclassifying activity

time use by types of behavior. First, to evaluate

periodic motion artifacts in an active state, we

designed a “run” protocol which is taken into

account for walking when commuting to school or

work or engaging in sports such as jogging or

running. Next, to evaluate non-periodic motion

artifacts in a semi-resting state, we designed “daily1”

and “daily2” protocols which are taken account for

habitual tasks in daily life and business-related work,

schoolwork, housework, etc. For each protocol, the

subject began by resting in a sitting position to

condition his heart rate. All three protocols were not

performed on the same day to lighten the load on

subjects.

We used the Shimmer3 ECG unit from Shimmer

to provide a reference measurement for instant heart

rate. The sampling frequency is 512 Hz. Electrode

positioning was also optimized for each subject to

enhance S/N of the ECG signal. The instant heart

rate was calculated from the time intervals of the R

Characteristic

μσ

Range

Age

35.00  6.34

26 – 43

Table 3: Detailed activity time use by types of behavior

for one week.

Activity

Duration

(hh:mm)

Sleep 07:42

Personal care 01:19

Meals 01:39

Commuting to and from school or work 00:31

Work 03:33

Schoolwork 00:39

Housework 01:27

Caring or nursing 00:03

Child care 00:14

Shopping 00:26

Moving (excluding commuting) 00:30

Watching TV, listening to the radio,

reading a newspaper or magazine

02:27

Rest and relaxation 01:31

Learning, self-education, and training 00:12

Hobbies and amusements 00:44

Sports 00:14

Volunteer and social activities 00:04

Social life 00:19

Medical examination or treatment 00:08

Other activities 00:17

Table 4: Results of reclassifying activity time use by types

of behavior.

Class Duration (%)

Resting state 32.8

Semi-resting state 65.1

Active state 3.1

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wave in the ECG signal.

The subject wore the prototype PPG sensor on

his non-dominant hand, and time synchronization

with shimmer3 was achieved through time

synchronization between the prototype device and

the host PC.

In comparing the instant heart rate calculated

from ECG and the instant pulse rate calculated from

the prototype device, we used resampled values at 1

Hz by linear interpolation for each. Furthermore, in

performing a quantitative evaluation of the accuracy

of detecting heart rate variability, we defined

reliability and accuracy as follows (Delgado-

Gonzalo, 2014).

Reliability is defined as the percentage number

of samples for which error is within 10% of the ECG

instant heart rate. Accuracy, meanwhile, is the mean

complement of error with the ECG instant heart rate.

Table 8 lists mean reliability and accuracy across

all subjects for each protocol in a laboratory

environment. Figure 6 shows Bland-Altman plots of

the ECG instant heart rate and PPG instant pulse rate

for each experimental protocol.

Based on the quantitative evaluation results for each

experimental protocol of Table 8 and activity time

use by types of behavior of Table 4, we estimated

the percentage of measurements within 10% of the

ECG heart rate for one day (referred to below as

“pulse-accuracy achievement rate”) in the following

way. Reliability of resting state was taken to be

reliability in a resting state over the run protocol,

daily1 protocol, and daily2 protocol. As a result,

subject mean of them was 97.5%. Next, reliability of

active state was taken to be reliability in the range

Table 5: Run protocol.

Task

Duration

(min)

Take deep breaths sitting down 2

Stand and rest 1

Walk on treadmill at 5 km/h 3

Walk on treadmill at 7 km/h 3

Walk on treadmill at 11 km/h 3

Take a break standing up 2

Table 6: Daily1 protocol.

Task

Duration

(min)

Take a break sitting down 2

Take deep breaths sitting down 2

Gesture that is supposed personal care 2

Gesture that is supposed meals 2

PC operation that is supposed office work 2

Smart phone operation 2

Gesture that is supposed housework 2

Take a break sitting down 2

Table 7: Daily2 protocol.

Task

Duration

(min)

Take a break sitting down 2

Gesture that is supposed reading book 2

Gesture that is supposed writing down 2

Gesture that is supposed conversation 2

Take a break sitting down 2

Table 8: Subject mean of reliability and accuracy of

estimated instant heart rate for each protocol in a

laboratory environment (*Accuracy = 100 - mean

percentage error).

Protocol Reliability (%) *Accuracy (%)

Run protocol 83.7 93.9

Daily1 protocol 82.7 93.8

Daily2 protocol 93.0 96.1

Mean 86.5 94.6

Figure 6: Results of Bland-Altman analysis of ECG instan

heart rate and PPG instant pulse rate. Top: run protocol;

middle: daily1 protocol; bottom: daily2 protocol. HR

ecg

and PR

grn

denote ECG instant heart rate and PPG instan

pulse rate, respectively.

Wearable Motion Tolerant PPG Sensor for Instant Heart Rate in Daily Activity

131

from standing to running tabulated for the run

protocol. As a result, subject mean of reliability was

81.4%. Finally, reliability of semi-resting state was

taken to be reliability outside the resting state

tabulated over the daily1 protocol and daily2

protocol. As a result, subject mean of them was

83.7%. Based on the above results, the day pulse-

accuracy achievement rate turned out to be 88.1% or

21.1 hours when converted to time.

5 DISCUSSION

We evaluated the reliability and accuracy of the

prototype wristband-type PPG sensor and motion

artifact cancellation framework in ambulatory

environment. In the experiment, we randomly

selected 4 of the 12 subjects and took pulse

measurements during their working hours. Table 9

lists reliability and accuracy values for each of these

subjects. Body motions from the subjects’ activity

records were typically computer keyboard

operations, note-taking/writing, lunch, and

smartphone use during breaks, which means

activities near those of the daily1 and daily2

protocols. However, reliability in an office

environment was found to be 77.9% or about 10%

lower than the 87.9% average reliability of the

daily1 and daily2 protocols.

Table 9: Subject mean of reliability and accuracy of

instant heart rate estimation in an office environment.

Subject

Duration

(hh:mm)

Reliability

(%)

Accuracy

(%)

#1 08:00 68.7 91.4

08:00

84.1 94.3

08:00

77.9 93.3

08:00

80.9 93.8

Mean (N=4)

08:00

77.9 93.2

Figure 7 shows Bland-Altman plots of the ECG

instant heart rate and PPG instant pulse rate.

Compared with the Bland-Altman plots for the

daily1 and daily2 protocols of Figure 6, the heart

rate band has broadened and estimation error of the

instant heart rate has increased. This broadening of

the heart rate band can be explained as follows. For

the daily1 and daily2 protocols in a laboratory

environment, subjects carried out their tasks in a

sitting position, but in the office-environment

experiment, their heart rates would increase as they

walked to conference rooms or cafeterias or engaged

in discussions during meetings. Next, the increase in

error is thought to be due to changes in contact

Figure 7: Results of Bland-Altman analysis of ECG instan

heart rate and PPG instant pulse rate in an office

environment. HR

ecg

and PR

grn

denote ECG instant heart

rate and PPG instant pulse rate, respectively.

pressure between the pulse sensor and subject’s

body due to deformation of the arm’s shape caused

by motion or twisting of the wrist. If motion artifacts

due to changes in contact pressure can be formalized

as an additive model, our proposed framework

should be able to incorporate them.

6 CONCLUSIONS

We proposed a motion artifact cancellation

framework for a wristband-type heart rate sensor. As

part of this framework, we derived a motion artifact

additive model based on the results of motion

artifact analysis.

First, to cancel arm-related motion artifacts, we

modelled the transfer function to blood flow as an

FIR filter through system identification and used the

signal resulting from convolution of the acceleration

signal as a reference signal to improve the arm-

related motion artifact reduction effect. Next, to

cancel finger and wrist-related motion artifacts, we

measured blood flow in body tissue by PPG at 630

nm, weakened the pulse-signal component in that

signal by a band-pass filter based on heart rate, and

used the result as a reference signal to improve the

finger-related motion artifact reduction effect.

Finally, for pulse rate calculation, we integrated

the results of calculating pulse rate with reliability

by both the peak-detection and autocorrelation

methods thereby achieving instant pulse rate

detection with high noise resistance and high

accuracy. The pulse-measurement accuracy-

achievement rate was estimated to be 88.1% or 21.1

hours, which indicates that the prototype device and

motion artifact cancellation framework can detect

variability in heart rate with high accuracy in daily

activity.

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