Ambient Light Contribution as a Reference for Motion Artefacts

Reduction in Photoplethysmography

Nicolas De Pinho Ferreira

, Claudine Gehin

and Bertrand Massot

INL, CNRS UMR5270, INSA Lyon, Univ. Lyon, Villeurbanne, France

Keywords:

Heart Rate, Photoplethysmography, Motion Artefacts, Ambient Light, Adaptive Filtering.

Abstract:

Measuring the heart rate from a convenient location such as the wrist is commonly achieved using photo-

plethysmography. As a consequence, this method is widely used on commercial wearable devices. Unfortu-

nately, it also highly suffers from motion artefacts superimposed into the cardiac frequency band which gener-

ally lead to incorrect heart rate estimation. In this paper we propose a new approach that uses the ambient light

contribution as a reference for motion artefacts reduction. Contrarily to accelerometer-based techniques, the

proposed method does not require any additional hardware. Moreover, it is especially efﬁcient for reduction

of micro-motions that can’t be addressed using conventionally used accelerometry. Using the ambient light

signal as a reference in association with adaptive ﬁltering has demonstrated promising results for the reduction

of artefacts during both periodic and random motion events.

1 INTRODUCTION

The most commonly used technique for heart rate

(HR) measurement with wearable devices is the

photoplethysmography (PPG). This non-invasive and

low-cost technique enables measurement from conve-

nient locations such as wrist, ﬁnger or earlobe. Ba-

sically, a photoplethysmographic measurement sys-

tem is comprised of two opto-electronic elements, a

photoemitter, usually a light-emitting diode (LED),

and a photodetector, usually a photodiode (PD). As

measurement front ends are readily available off-the-

shelf in tiny integrated circuits, it can be integrated in

small form factor devices and thus, used for ambula-

tory measurements.

The measurement principle of PPG is based on

the detection of volume change (plethysmography)

caused by cardiac activity. In this particular con-

ﬁguration, changes are evaluated by an optical tech-

nique. In regard to cardiac activity, during diastole,

blood is ejected through the aortic valve in the cir-

culatory system which causes a local increase of vol-

ume in elastic arteries, muscular arteries and arteri-

oles. When this volume increases, the local absorp-

tion coefﬁcient is augmented and consequently the

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variation can be measured, according to the Beer-

Lambert Law (Hu et al., 2013). Depending on the

relative position between the LED and the PD, a pho-

toplethysmographic acquisition system can be used

in reﬂection-mode or transmission-mode. When both

optical elements are placed on the same surface and

applied onto the measurement site (for example, upon

the wrist), this is called reﬂection-mode. On the con-

trary, in the transmission-mode, optical elements are

placed from either side of the measurement site (for

example, across the ﬁnger).

When using photoplethysmography for heart rate

acquisition, motion artefacts are one of the main is-

sues to be addressed, particularly in ambulatory con-

ditions. As long as the user stays still and thus no mo-

tion is induced at the measurement site, photoplethys-

mographic signal frequency is representative of car-

diac activity and heart rate (Jan et al., 2019). How-

ever when the user starts a physical activity such as

walking, running or any other activity that can imply

motion on the measurement site, this will induce un-

wanted noise on the PPG signal. Unfortunately, this

noise has a spectral content which overlaps with car-

diac band and thus, can’t be removed using conven-

tional linear ﬁltering techniques.

A common way of addressing this issue is to use

an auxiliary reference signal that contains correlated

information on motions that occur on the measure-

ment site. This reference signal can then be used with

De Pinho Ferreira, N., Gehin, C. and Massot, B.

Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography.

DOI: 10.5220/0008878800230032

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES, pages 23-32

ISBN: 978-989-758-398-8; ISSN: 2184-4305

a signal processing technique such as adaptive ﬁlter-

ing. In the literature, accelerometry is a commonly

used reference signal as it contains motion-correlated

information (Lee et al., 2010). Other proposed tech-

niques use dual wavelength PPG, where one channel

is used as reference (Zhang et al., 2019). In con-

ventional PPG acquisition systems, ambient light is

removed from PPG signal as it represents a noise

source and could lead to wrong physiological param-

eters evaluation. In this work, we attempted to use the

contribution of this ambient light received directly by

the PPG sensor, as a reference signal for adaptive ﬁl-

tering.

The paper is organised as follow : in section 2,

hardware developed for ambient light acquisition by

PPG sensor is described. Then in section 3, signals

acquired and their content are analysed; the high de-

gree of correlation obtained during motion artefacts

events is discussed. After demonstrating the inter-

est of ambient light for motion artefacts reduction, we

propose a signal processing toolchain developed with

MATLAB (The MathWorks, Massachusetts, US) in

section 4. This toolchain is used for addressing both

periodic and random motion artefacts.

2 MEASUREMENT SETUP

2.1 Hardware

Photoplethysmographic acquisitions were made us-

ing a MAX86140EVSYS (Maxim Integrated, Califor-

nia, US) evaluation kit and a custom designed optical

board. The original microcontroller board from the

evaluation kit and the custom designed optical board

were both linked with a ﬂexible cable, allowing for

convenient placement of optical elements. PPG ac-

quisition relies on a MAX86141 (Maxim Integrated,

California, US) Analog Front End (AFE) in associa-

tion with SFH 2201 wide-band photo-diodes and LT

P4SG-V1AB-36 528 nm LEDs. During all measure-

ments, the system was battery powered for a maxi-

mum freedom of movement. The overall architecture

of the system is presented on Fig. 1.

In addition to classic PPG measurements, the opti-

cal board featured a 3-axis accelerometer whose data

were simultaneously recorded. Acquired measure-

ments can be directly stored in an embedded ﬂash

memory or sent wirelessly in real-time via a Blue-

tooth radio-frequency communication. All recordings

presented in this paper were made on the left wrist,

and the optical board was placed on the bottom side

of the wrist while the microcontroller board was on

the upper side.

CPU BOARD

OPTICAL BOARD

SPI

PPG AFE

(MAX86141)

ACCELEROMETER

(BMA280)

CPU

(MAX32630)

BT RADIO

(NRF52832)

FLASH

(S25FS256)

LED

PMIC

(MAX20303)

23 mm

12 mm

BOTTOM

TOP

35 mm

17 mm

Figure 1: Acquisition system architecture.

2.2 Ambient Light

When normally used, the MAX86141 PPG AFE au-

tomatically removes the ambient light contribution on

acquired samples. The acquisition timings of the cir-

cuit are represented on Fig. 2. As a ﬁrst step, both

LEDs are driven on, and after a short settling time,

a sample from the photodiode is acquired and inte-

grated during the time T

INT

. Then, LEDs are turned

off and a new sample representative of the ambient

light is acquired from the photodiode. This process is

repeated periodically every T

SAMP

. The value stored

during the ambient exposure phase is then used to re-

move contribution of ambient light. Depending on

the analog front end used, different Ambient Light

Cancellation (ALC) strategies exist. For example,

the stored value can be used to drive an analog cir-

cuitry that will sink current corresponding to ambi-

ent exposure. Ideally, in this conﬁguration, the digi-

tized value is free of ambient light contribution. This

method has the advantage of preventing saturation of

transimpedance ampliﬁer and analog-to-digital con-

verters. In a digital approach, the measured ambient

sample value can be subtracted from the LED expo-

sure sample.

While internal ambient light cancellation is en-

abled, the PPG AFE can be conﬁgured to retrieve

the ambient light value measured. For this purpose,

the PPG front end conﬁguration was modiﬁed and

both ambient light and PPG exposure were recorded.

This technique does not require any additional hard-

ware from a conventional PPG setup. However, as the

MAX86141 can be used with two photo-diodes, this

feature could be used in our case to increase robust-

ness of developed motion artefact reduction technique

as discussed in section 3.

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

LEDs

EXPOSURE

AMBIENT

EXPOSURE

LED

Driving

LEDs

TSAMP

TINT TINT

Figure 2: PPG AFE Acquisition Timing.

3 ACQUIRED SIGNALS

3.1 First Approach

In a ﬁrst approach, both ambient and PPG signals

were recorded while doing multiple, periodic and

wide amplitude motions such as arm shaking; as well

as lower amplitude movements such as hand waving

and opening/closing. Plots on Fig. 3 are unprocessed

data coming from the evaluation kit. Only signal nor-

malisation has been applied on PPG and ambient light

recordings. For all measurements presented in this pa-

per, internal ALC system of MAX86141 was enabled.

0 20 40 60 80 100 120 140

Time (s)

0.7

0.8

0.9

RAW PPG

0 20 40 60 80 100 120 140

Time (s)

0.5

0.6

0.7

0.8

0.9

RAW Ambient light

0 20 40 60 80 100 120 140

Time (s)

-0.5

0.5

Cros-correlation

Cross-correlation

Figure 3: PPG and ambient recording during motion events.

A clear matching between PPG and ambient light

recordings during motion events can be observed.

Additionally, windowed cross-correlation bears out

the visual similarity between both waveforms during

motion events. The window size is 512 points, which

represents a duration of 4 seconds, for a 128 Hz sam-

pling frequency.

This high degree of similarity can be explained as

follows: when motion occurs, the relative distance be-

tween user’s skin and optical elements is modiﬁed,

which implies a variation of the ambient light amount

received by the photo-detector. As the induced mo-

tion is periodic, the ﬁne variation in amount of light

received follows a periodic pattern.

However, as shown on Fig. 3, ambient light can

also contain noise. Even if no motion occurs, the am-

bient light signal is still comprised of a low ampli-

tude high frequency noise. This noise can come from

sources such as ﬂuorescent light, ﬂickering or fast am-

bient variation and should also be removed before sig-

nal processing.

In addition, when wide amplitude motion occurs

(like arm shaking at the end of this dataset), cross-

correlation does not show an agreement as high as

during smaller amplitude motions. In this particular

situation, the amount of ambient light measured onto

the photodiode does not only represent a contact mod-

iﬁcation between optical elements and wrist, but is

also representative of real ambient light variations.

PD2 (UPPER)

PD1 (UNDER)

LED1 (UNDER)

Figure 4: Possible concept for handling wide MA.

A possible approach to overcome this issue would

be to use an auxiliary photodiode, mounted on the up-

per side of the device (PD2) as described on Fig. 4.

This would allow to remove the real ambient contri-

bution detected by the ﬁrst photodiode (PD1). An-

other approach would be to choose dynamically be-

tween ambient or accelerometry signals as a reference

for motion artefact reduction depending on the type of

movements detected using the accelerometer.

3.2 Micro-motions

For speciﬁc events such as micro-motions, the ambi-

ent light signal features a clear periodic component,

contrarily to conventionally used accelerometry. As

it can be seen on Fig. 5, small hand motions (ﬁngers

waving in that case) cause signiﬁcant noise on PPG

signal. Meanwhile, in this conﬁguration, the wrist is

Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

standing still and an accelerometer is not able to de-

tect this motion onto the measurement site, as the ac-

celeration recorded along all the three axes does not

show any clear periodic component.

32 34 36 38 40 42 44 46 48 50

Time (s)

0.65

0.7

0.75

0.8

0.85

0.9

Amplitude (AU)

RAW PPG

32 34 36 38 40 42 44 46 48 50

Time (s)

-0.6

-0.4

-0.2

0.2

Amplitude (AU)

RAW Acceleration

32 34 36 38 40 42 44 46 48 50

Time (s)

0.4

0.45

0.5

Amplitude (AU)

RAW Ambient light

Figure 5: PPG, accelerometry and ambient light.

On the particular signal window represented on

Fig. 5, the highest cross-correlation coefﬁcient was

obtained between PPG recording and ambient light

) with a value of 0.9641, rather than with the accel-

eration magnitude (A

) which led to a value of 0.2687

(the highest cross-correlation value was found along

the x-axis : A

= 0.1801, A

= -0.2029, A

= -0.0700).

3.3 Analysis

To identify what type of motion is most likely to be

removed by ambient light or by any another refer-

ence signal, we used cross correlation and mutual in-

formation as indicators of similarity (Tautan et al.,

2015). For this purpose, we used MATLAB (The

MathWorks, Massachusetts, US) in addition with a

speciﬁc toolbox for mutual information computation

(Brown et al., 2012). Preliminary results for this in-

vivo experiment were obtained from six healthy vol-

untary subjects (2 female, 4 male) whose written con-

sent was obtained. The age of the participants was

36 years old ± 11. Subjects were told to successively

perform each type of motion during 10 s each, sepa-

rated with 10 s of rest. The following situations were

considered :

1. Hand / ﬁst opening and closing

2. Finger tapping on table

3. Moving forearm from elbow up and down

4. Moving forearm from elbow right and left

5. Moving ﬁngers in a waving fashion

6. Motionless, in a resting situation

The cross-correlation coefﬁcient values obtained

between PPG recording and reference signals for each

situation listed are shown on Fig. 6. The results for

mutual information are shown on Fig. 7.

0 1 2 3 4 5 6 7

Motion type

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Correlation coefficient

Absolute value of cross-correlation

ACCX

ACCY

ACCZ

ACCN

Ambient

Std. Dev.

Figure 6: Cross correlation between PPG and references.

0 1 2 3 4 5 6 7

Motion type

2.5

3.5

4.5

5.5

Mutual Info. Value

Mutual Information

ACCX

ACCY

ACCZ

ACCN

Ambient

Std. Dev.

Figure 7: Mutual Information between PPG and references.

In most situations, ambient light (AL) reference

signal features the highest degree of correlation and

mutual information. Moreover, ambient light pro-

vides far better results for MA reduction during sit-

uations where micro-motions occur which can not

be detected using the accelerometer. On the other

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

hand, when wide amplitude motions occur, the ac-

celerometer signals contain a clear periodic compo-

nent whereas ambient light reference suffers from real

exposure modiﬁcations. In this conﬁguration, the am-

bient light reference does not only contain motion-

correlated information but also real ambient light cor-

related information. As a consequence, this situation

could reduce the performances of an adaptive ﬁlter

if using ambient light information only. In case of a

combination of micro-motions described previously,

which is a much more realistic situation than station-

ary and periodic motions, ambient light still indicates

a higher degree of correlation and mutual information

than accelerometry. For signal processing in section

4, we will focus on micro-motions that feature a high

degree of correlation and that can’t be addressed us-

ing conventional accelerometry.

Another parameter that inﬂuences degree of cor-

relation and mutual information between ambient and

PPG is time delay. As a function of the consid-

ered motion, a time delay exists between the moment

where motion artefact occurs on the reference signal

and the resulting alteration of PPG signals (Gibbs and

Asada, 2005). However for evaluation of correlation

and mutual information, there was no additional time

delay introduced.

4 SIGNAL PROCESSING

4.1 Requirements

In order to extract physiological parameters such as

Heart Rate (HR) or Peripheral Oxygen Saturation

(SPO

) from PPG signals whose integrity is compro-

mised by motion artefacts, several signal processing

techniques were proposed in prior art. These tech-

niques can be classiﬁed as follows :

• Time domain methods, which use Least Mean

Square (LMS) (Ram et al., 2012) or Recursive

Least Squares (RLS) adaptive ﬁlters;

• Frequency domain methods, such as Spectral Sub-

traction (SS) (Islam et al., 2019);

• Hybrid methods that feature a time-frequency

approach such as Ensemble Empirical Mode

Decomposition (EEMD) and adaptive ﬁltering

(Khan et al., 2016).

Contrary to multi-spectral techniques, the pro-

posed reference signal in this work (ambient light)

does not contain any cardiac component. Conse-

quently, it does not require any high computing cost

algorithm such as Continuous Wavelet Transform

(CWT) spectrum subtraction (Zhang et al., 2019). In

this section, we demonstrate the ability of ambient

light to reduce motion artefacts using both frequency

and time domain methods.

4.2 Frequency Domain

In a ﬁrst approach, the spectral content of both am-

bient light and PPG recording during motion events

are analysed. All signals are band-pass ﬁltered from

0.5 to 10 Hz using a 4

order FIR, to remove high

frequency noise present on ambient signal. On the

PPG signal, this removes the DC component and lim-

its contribution of noise outside of cardiac frequency

range. Once the signal is ﬁltered, Discrete Fourier

Transform (DFT) is computed to obtain the power

spectrum as shown on Fig. 8.

0 1 2 3 4 5

Frequency (Hz)

Power density (dB/Hz)

PPG PSD During motion event

0 1 2 3 4 5

Frequency (Hz)

100

120

140

Power density (dB/Hz)

Ambient PSD During motion event

X: 2.367

Y: 28.78

X: 2.367

Y: 120.6

X: 1.087

Y: 3.055

Motion peak

HR component

Figure 8: PPG and ambient spectral content during motion.

Although PPG spectrum contains HR related in-

formation with a small peak around 1.087 Hz (65

BPM), most of the power is located on the 2.367

Hz peak, corresponding to the elicited motion arte-

fact (MA). A high power peak at same frequency is

shown on ambient light spectrum. If we consider mo-

tion artefacts superimposed onto PPG spectrum as an

additive noise and a stationary process, we can use

Spectral Subtraction (Vaseghi, 2001). As a proof of

concept, both power spectrum were normalised and

ambient contribution subtracted from PPG.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Frequency (Hz)

0.5

1.5

2.5

Power density (dB/Hz)

X: 1.087

Y: 2.708

X: 2.239

Y: 0.3397

HR peak

Residual MA

Figure 9: Resulting spectral subtraction.

This approach led to the power spectrum shown

on Fig. 9. The cardiac component peak is now clearly

visible. By applying an Inverse Discrete Fourier

Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

Transform (IDFT), resulting time domain waveform

can be evaluated, see Fig. 10.

0 5 10 15

Time (s)

-0.015

-0.01

-0.005

0.005

0.01

0.015

Amplitude (A.U)

Restored PPG using spectrum substraction

0 5 10 15

Time (s)

-4

-2

Amplitude (A.U)

Original PPG

Figure 10: Time-domain PPG before and after spectral sub-

traction.

Although this technique has a relative low

computing-cost, it requires a stationary noise over the

considered window. In this particular case, window

was hand-selected, but in practice this might not be

suitable for online noise cancellation.

4.3 Adaptive Filtering

A possible signal processing approach for motion

artefact reduction is the use of adaptive ﬁltering,

whose principle is represented on Fig. 11.

LMS update

algorithm

D(n) = S(n) + x’(n)

X(n)

E(n)

Y(n)

PPG

AMBIENT

Filter

output

Variable

filter

ΔWN

Figure 11: LMS Adaptive ﬁlter content.

This type of ﬁlter is widely used for noise re-

duction when an additional noise reference correlated

with the original signal that has to be de-noised is

available. It is based on a stochastic gradient descent:

the general idea is to design an ideal ﬁlter whose co-

efﬁcients are iteratively computed using an optimisa-

tion algorithm. The Least Mean Squares (LMS) algo-

rithm applied here uses a reference input, X(n), as an

additional information source on noise contained in

D(n). For efﬁcient coefﬁcient optimisation and thus

ﬁltering, signal D(n) has to be correlated with noise

contained in X(n). As discussed in section 3.3, the

ambient light signal features a high degree of corre-

lation with PPG during MA events. Consequently, a

simple and low computing-cost LMS adaptive ﬁlter

should be able to produce an error output E(n) with

reduced motion artefacts.

To verify this statement, a signal processing

chain which implements all the required steps has

been developed in MATLAB (The MathWorks, Mas-

sachusetts, US). The architecture is described on Fig.

12.

dataset

ppg + ambient

*.csv

pre-

processing

cwt-based analysis

bandpass filtering

adaptive

filtering

time and frequency

domain filters

post-

processing

Singular Spectrum

Analysis (SSA)

heart rate

estimation

Peak finding

ecg record

*.csv

Figure 12: Signal processing toolchain architecture.

Ambient light and PPG recordings extracted from

the wrist-worn device are directly imported in MAT-

LAB. As a pre-processing step, both PPG and am-

bient are band-pass ﬁltered from 0.5 to 10 Hz using

order FIR in order to remove noise outside of the

cardiac band, thus ensuring that both reference and

PPG share the same spectral limits. In this same step

and for signal analysis in time-frequency domain, a

continuous wavelet transform (CWT) is used to anal-

yse the spectrogram. This CWT technique features a

higher resolution in both domain in comparison with

Short Time Fourier Transform (STFT). Next, during

the adaptive ﬁltering step, a time-domain LMS ﬁl-

ter is used for motion artefact reduction. Finally, ﬁl-

tered signal is post-processed using Singular Spec-

trum Analysis (SSA) to remove residual discontinu-

ities. The ﬁltered output spectral content is also anal-

ysed using CWT and compared with the previous

spectrogram obtained during pre-processing. Heart

rate estimation is performed in time domain method

by ﬁnding peaks on the SSA-denoised PPG signal.

For validation purpose, a simultaneous electrocardio-

gram (ECG) recording is used as the gold standard

method.

4.3.1 Periodic Motion

The ﬁrst dataset used for evaluating the performance

of the adaptive the LMS ﬁlter is a 80 s recording com-

prised of periodic hand motion (opening and closing).

Apart from motion events, the PPG spectrogram on

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

Fig. 13 features a clear cardiac component with a 2

harmonic corresponding to the dicrotic notch. When

no motion is induced, the spectrogram maximum en-

ergy, plotted in red, follows the resting HR of user of

69.36 BPM.

Figure 13: Pre-processed PPG and ambient signals.

During motion artefacts (MA) events, the maxi-

mum spectrogram energy is located at the frequency

of the artefact (1.87 Hz). Although the cardiac com-

ponent is no longer dominant, it can however still be

detectable on the spectrogram. Performing heart rate

evaluation on this simple band-pass ﬁltered PPG sig-

nal would lead to incorrect estimation. The spectro-

gram obtained by CWT conﬁrms that ambient light

features a clear matching with PPG in frequency do-

main during motion artefacts.

The output obtained from the adaptive ﬁlter is

shown on Fig. 14. Despite its simplicity (in com-

parison with others structures such as X-LMS, RLS

or variable step-size LMS), this ﬁlter is able to reject

motion artefact. On this dataset, a ﬁlter comprised

of L = 32 taps is used. Step size parameter µ = 0.006

that controls convergence speed and stability is manu-

ally tuned for best trade-off between convergence and

ﬁlter response. Smaller values of µ tend to reduce

convergence speed while larger values lead to impor-

tant misadjustment.

The maximum spectrogram energy, plotted in

dashed lines on Fig. 14 is representative of PPG sig-

nal fundamental frequency. However, using this in-

formation directly for estimating heart rate could lead

Restored PPG using LMS adaptive filter

Restored PPG spectrogram

Figure 14: Restored PPG recording using LMS ﬁlter.

to incorrect estimations. In order to reduce discon-

tinuities and high frequency noise locally present on

the ﬁltered PPG waveform, Singular Spectrum Anal-

ysis (SSA) is used. The aim of this method is to de-

compose the signal into principal components, each

of these extracted time series representing a trend of

the original signal : oscillatory mode, noise or peri-

odic pattern (Golyandina et al., 2001). The decom-

position is operated in MATLAB : a covariance ma-

trix is computed using Toeplitz approach, and once

eigenvalues and eigenvectors are extracted from the

covariance matrix, principal components (PC) are ob-

tained by performing matrix product between embed-

ded timeseries (windowed version of PPG signal) and

eigenvectors. The de-noised version of PPG is ob-

tained by re-summing the two ﬁrst PCs only, and the

result of this operation is shown on Fig. 15 with a

simultaneous ECG recording plotted altogether.

0 10 20 30 40 50 60 70 80

Time (s)

-0.6

-0.4

-0.2

0.2

0.4

0.6

Amplitude (A.U)

Denoised PPG (PC1 + PC2)

Simultaneous ECG

Figure 15: De-noised PPG and simultaneous ECG record.

An instantaneous heart rate detection is performed

on this waveform using a peak-ﬁnding method (a

zero-crossing detection method could also be used for

this purpose), and the time difference between suc-

cessive peaks is then calculated. This method is par-

ticularly efﬁcient on denoised PPG signal as its am-

plitude remains relatively constant with time. On the

Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

contrary, operating this peak detection directly onto

the LMS adaptive ﬁlter output could have been more

complex as it should have use a time-varying thresh-

old for maintaining a correct detection.

0 10 20 30 40 50 60 70 80

Time (s)

-10

-5

Amplitude (AU)

PPGs peakfinding

LMS Output Peaks LMS SSA Output Peaks SSA

0 10 20 30 40 50 60 70 80

Time (s)

100

120

140

160

Heart Rate (BPM)

Instantaneous HR

LMS SSA BPF PPG

Figure 16: Instantaneous heart rate evaluation.

Instantaneous HR for bandpass ﬁltered PPG, LMS

ﬁlter output and SSA denoised PPG are plotted on

Fig. 16. As expected, the denoised PPG shows a

slowly time-varying heart rate devoid of fast tran-

sients which are still present on the adaptive ﬁlter out-

put. However, as SSA acts as a smoother in case of

missing pulses on the LMS processed PPG, it could

potentially acts as a low-pass ﬁlter and thus compro-

mise the evaluation of instantaneous HR. In this sit-

uation, the calculation of parameters such as Heart

Rate Variability (HRV), which is a relevant indica-

tor for assessing cardiovascular system state, could be

erroneous. In order to evaluate the performances of

the proposed processing toolchain, the instantaneous

heart rate has also been extracted from a simultaneous

ECG (Fig. 17).

0 10 20 30 40 50 60 70 80

Time (s)

Heart Rate (BPM)

Reference ECG

Post-processed (SSA) PPG

Figure 17: Comparison with simultaneous ECG recording.

Instantaneous ground truth heart rate was ex-

tracted from ECG using a simple peak detection to

detect the R-waves. Over this particular dataset, this

method didn’t shown any signiﬁcant difference with

Pan and Tompkins detection method. Then, inter-

beats intervals from both processed PPG and ECG

were uniformly re-sampled. As discussed in prior art,

pulse rate variability (PRV) of PPG is representative

of HRV in resting conditions (Gil et al., 2010). How-

ever, in ambulatory conditions where motion occurs,

correlation between PPG PRV and ECG beat-to-beat

intervals is reduced and depends on measurement site

(Maeda et al., 2011). Consequently, ground truth HR

and estimated HR from processed PPG are compared

in an ’average level’, instead of pure beat-to-beat cy-

cle. Both tachograms were smoothed using a mov-

ing average ﬁlter over 4 points. Even during motion

events, heart rate follows the trend of ECG reference,

making ambient light and associated processing tech-

nique a suitable candidate for assessing HR and de-

rived indicators in an ambulatory use. The associated

Bland Altman diagram and scatter plot between gold

standard ECG and processed PPG shows good per-

formance and the limits of agreement are +1.4 / -2.0

BPM. On this particular dataset, Pearson’s coefﬁcient

is R = 0.85.

60 65 70 75

ECG (BPM)

PPG (BPM)

y=0.93x+4.12

=0.85

SSE=59 BPM

n=78

60 65 70 75

Mean ECG & PPG (BPM)

-6

-4

-2

PPG - ECG (BPM)

1.4 (+1.96SD)

-0.31 [p=0.00]

-2.0 (-1.96SD)

RPC: 1.7 BPM (2.6%)

CV: 1.3%

Figure 18: Bland Altman diagram between ECG and PPG.

4.3.2 Random Motions

Rather than periodic movements, a more realistic situ-

ation of motion artefacts can be evaluated by inducing

micro-motions occuring in a random fashion during

keyboard typing. In this situation, even if the wrist is

standing quite still, typing will cause ﬁngers tendons

to move, thus resulting in a modiﬁcation of the optical

contact and superimposition of motion artefacts.

However, in this situation, the considered noise is

no longer a stationary process and cannot be modelled

by a Gaussian distribution law, which is a fundamen-

tal hypothesis for the use of adaptive ﬁlters (Belge

and Miller, 2000). To overcome this limitation, a

variant of Recursive Least Squares (RLS) algorithm,

known as generalised sliding windows RLS (Sayed,

2003), was applied. Other methods such as expo-

nentially weighted RLS (W-RLS) are also suitable for

this purpose. The main drawback of RLS adaptive al-

gorithms is their higher computational cost compared

with LMS which can affect autonomy of the system if

used in a wearable device. A sliding window RLS

adaptive ﬁlter was thus implemented in MATLAB,

BIODEVICES 2020 - 13th International Conference on Biomedical Electronics and Devices

in place of the previously used LMS ﬁlter. Except

from that, the signal processing toolchain remains un-

changed. The considered dataset is comprised of PPG

signal together with a simultaneous ECG recording

over 120 s while keyboard typing (see ﬁg. 19).

0 20 40 60 80 100 120

Time (s)

-1

-0.5

0.5

Amplitude (A.U)

BPF PPG

0 20 40 60 80 100 120

Time (s)

-0.5

0.5

Amplitude (A.U)

BPF Ambient light

Figure 19: PPG and ambient light while typing.

The RMS adaptive ﬁlter used had a length of 4

weights and the sliding window size was 8 s. These

parameters were chosen empirically to obtain the best

possible results in terms of artefacts rejection. The

spectrogram was used for this purpose as a direct vi-

sual indicator. The output of the adaptive ﬁlter was

then smoothed using SSA as previously described,

and peak detection was used for evaluating heart rate.

The resulting signal in both time and frequency do-

main is shown on Fig. 20.

Restored PPG using RLS adaptive filter

Restored PPG spectrogram

Figure 20: Sliding window RLS adaptive ﬁlter output.

Following the SSA denoising process, the instan-

taneous heart rate is estimated from time domain us-

ing peak ﬁnding. As previously described in section

4.3.1, ground truth heart rate (ECG) and PPG are uni-

formly re-sampled. Then, a moving average ﬁlter

over 4 points is applied and the obtained HR is com-

pared to the simultaneous ECG (see Fig. 21). Over

this 120 s dataset, a Bland-Altman analysis shows a

limit of agreement of 6.1 / -7.5 BPM with a bias of

-0.7 BPM.

0 20 40 60 80 100 120

Time (s)

100

110

120

130

140

150

Heart Rate (BPM)

Reference ECG

Processed PPG

Bandpass filtered PPG

Figure 21: Heart rate during keyboard typing.

Time sections where motion artefacts are contam-

inating PPG can be localised using heart rate com-

puted from band-pass ﬁltered PPG. This makes clear

illustration of continuous motion happening while

keyboard typing. Regarding this result, even if the

measured limit of agreement is higher, it must be kept

in mind that the considered motion artefacts are no

longer the consequence of a periodic activity, making

signal processing more complex.

5 CONCLUSIONS

In this paper, we suggested the use of ambient light

as a reference for the reduction of motion artefacts

in PPG signals measured on the wrist. The relevance

of this signal as a reference has been demonstrated

on various motions types. Micro-motions, that could

not be addressed previously using conventional ac-

celerometry, are efﬁciently reduced using the ambi-

ent light reference. The signal processing toolchain

has shown a promising ability to correct both periodic

and random motions when compared to a simultane-

ous heart rate directly derivated from the ECG. The

proposed method is especially suitable for implemen-

tation in an embedded device as it does not require

any additional hardware. Moreover, the ambient light

reference can be used with low computational cost

adaptive ﬁlters such as LMS.

Further investigation should be conducted on the

existing time delay between reference and PPG as it

could inﬂuence efﬁciency of motion artefacts rejec-

tion. Future work will be focused on the implementa-

tion of the proposed method in an autonomous wear-

able device with embedded signal processing capabil-

ity. Although the proposed method is especially efﬁ-

cient for micro-motions, accelerometry remains use-

ful in case of strong physical activity. A decision

strategy for choosing optimal reference between am-

bient light and accelerometry will also be developed.

Ambient Light Contribution as a Reference for Motion Artefacts Reduction in Photoplethysmography

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