Machine Learning-Based Smart-Textile for COVID-19 Monitoring

Nkengue Marc Junior, Xianyi Zeng, Ludovic Koehl, Xuyuan Tao, François Dassonville

and Nicolas Dumont

Laboratoire Génie et Matériaux Textile (GEMTEX), Université de Lille, ENSAIT, F-59000, Lille, France

francois.dassonville@ensait.fr, nicolas.dumont@ensait.fr

Keywords: Signal Processing, Wearable and Mobile Devices, Artificial Intelligence, Health Monitoring Device,

COVID-19.

Abstract: We propose a new low-cost wearable system to guaranty patient mobility and robust monitoring of COVID-

19 using physiological signals. Considering the correlation between two key signals (ECG and PPG), the

proposed wearable system will integrate an Variational AutoEncoder (VAE) with self-attention block to

reconstruct robust ECG, PPG Red and IR signals from a noisy ECG time series. The model performance is

evaluated using the Mean Square Error (MSE), the root-mean-square error (RMSE), Mean Absolute Error

(MAE) and the Signal-to-Noise Ratio (SNRoutput) for the signals. With a low MSE, RMSE and MAE, as

well as good SNR, the model can generate robust and clean data from the noisy ECG waveform measured by

the wearable system. we believe that the proposed wearable system can not only help to provide robust online

COVID-19 symptoms monitoring but also for other applications.

1 INTRODUCTION

Three years after its emergence in late 2019, severe

acute respiratory syndrome coronavirus 2 (Sars-CoV-

2) or COVID-19 infected more than 630 million

people, causing more than 6 million deaths (2023).

However, the symptoms of COVID-19 patients differ

from one variant to another in this long duration. For

all reported variants and all periods, the most serious

symptoms are shortness of breath (blood oxygen

level<92%) and heart failure (heart rate>90 bpm)

(Dhadge and Tilekar 2020, 2021). Although the

intensity of infection and symptoms have attenuated

thanks to the vaccination and follow-up of barrier

gestures, we are still far from the termination of the

pandemic. This is mainly due to the high infection

rate, the proliferation of its variants that can escape

from vaccination coverage, and the inability to detect

the virus in real-time and thus, control its

proliferation. This situation promotes the emergence

of remote monitoring and diagnosis tools using the

IoT (Internet of Things), including wearable systems

(Cacovean, Ioana et al. 2020, Nasajpour, Pouriyeh et

al. 2020, Pozo and Berrezueta Guzman 2020).

Wearable monitoring systems effectively reduced the

pressure of medical resources (e.g., medical doctors,

healthcare staff, devices, materials, etc.). They

perform real-time detection of basic symptoms of

COVID-19 by monitoring skin temperature, blood

oxygen saturation level and heart rate(Cacovean,

Ioana et al. 2020, Nasajpour, Pouriyeh et al. 2020).

The SpO

and the heart rate are computed from PPG

signals (red light and infrared light) and ECG signal

respectively. Despite their advantages, several

limitations are observed: 1) Physiological signal

(ECG, PPG

Red

and PPG

) are highly sensitive to

noises (Chen, Li et al. 2017, Chatterjee, Thakur et al.

2020): Noise induced by patient motion (motion

artifacts), respiration (Baseline wander) and by the

sensors itself (Powerline Interference). The lack of

robustness against noises affects SpO

and heart rate

computation accuracy. 2) The patient daily activities

are heavily obstructed by the positioning of the pulse

oximeter or PPG sensor (tip of the finger); 3) The

most robust wearable sensors are not easily

affordable. The ECG and PPG signals are

intrinsically correlated since the variation of the

peripheral blood volume is influenced by the left

ventricular myocardial activities. Unlike pulse

oximeter or PPG sensor, the optimal positioning of

heart monitor sensor does not obstruct patient daily

activities and provide useful signals. Our idea is to

design and implement an effective low-cost wearable

system coupling with a supervised learning model, to

172

Marc Junior, N., Zeng, X., Koehl, L., Tao, X., Dassonville, F. and Dumont, N.

Machine Learning-Based Smart-Textile for COVID-19 Monitoring.

DOI: 10.5220/0012466200003657

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pages 172-180

ISBN: 978-989-758-688-0; ISSN: 2184-4305

monitor the patient’s symptoms and make appropriate

decision support in real-time. The supervised learning

model learns the representation of clean ECG signal

and corresponding PPG signals (Red and IR) from a

single measured noisy ECG signal. The clean signals

can be used for accurate heart rate and SpO

estimation.

The rest of this paper is organized as follows.

Section 2 gives an overview of the related works.

Section 3 offers a description of the proposed

wearable system. In Section 4, we analyze the results

obtained and discuss about the implication of our

work. A conclusion and future perspectives are given

in Section 5.

2 RELATED WORKS

Many researchers in IoT and artificial intelligence

have developed various tools for monitoring and

detection of the virus infection. a number of wearable

systems with tiny sensors integrated into garments or

accessories have been used to measure physiological

parameters (e.g., skin temperature, heart rate, and

SpO

) of infected patients (Cacovean, Ioana et al.

2020, Nasajpour, Pouriyeh et al. 2020, Pozo and

Berrezueta Guzman 2020). Skin temperature is

estimated thanks to temperature sensor, SpO

level is

estimated from PPG signals (Red and IR) measured

by pulse oximeter sensor, and heart rate is estimated

from ECG signal measured by heart monitor sensor.

These wearable systems will enable the detect the

gravity of symptoms by checking measured

parameters values (e.g., the skin temperature>38°C ,

corresponding to high fever, SpO2<92% associate to

shortness of breath, and heart rate>90 bpm associate

to heart failure). The wearable systems allow a quick

monitoring of infected wearer’s health state with real-

time data acquisition.

Despite these advantages, the current wearable

systems have several drawbacks: 1) Raw ECG signals

and PPG signals are highly sensitive to noises (Chen,

Li et al. 2017, Chatterjee, Thakur et al. 2020) (Motion

artifacts, powerline interference, Baseline wander).

Without a pre-processing step, the signals cannot be

exploited for heart rate and SpO

estimation; 2) The

patient daily activities are heavily obstructed by the

positioning of the pulse oximeter sensor (the tip of the

finger is the optimal position for SpO

monitoring, the

patient need to stay still for an optimal measurement)

; 3) Wrist-based wearable system, while more robust

and less restraining than traditional wearable systems,

appears to be less accurate (They incorporate wrist-

based pulse oximeter sensor, which are less accurate

than finger-based pulse oximeter (Lee, Ko et al.

2016)) . They are also not easy affordable (the

average smart-watch price is higher than 150$). Since

the peripheral blood volume variation is linked to left

ventricular myocardial activities, it is easy to

establish a correlation between The PPG and ECG

signals. By using GAN, (Zhu, Tian et al. 2019, Sarkar

and Etemad 2021, Vo, Naeini et al. 2021) estimate

the waveform of the ECG signal using PPG

measurements by learning a signal model related to

ECG and PPG. Despite the good results obtained, the

models are not trained to handle noisy PPG signals.

Therefore, generated ECG and PPG signals are still

sensitive to noise.

In this context, the proposed system has been

developed to overcome the daily activities

obstruction caused by the pulse oximeter sensor and

the signals (ECG, PPG

Red

and PPG

) vulnerability

against noise. We propose a low-cost smart textile

coupling with a supervised learning model. Instead of

learning ECG waveform representation from PPG

waveform, the model will learn three waveforms

representation (ECG, PPG

Red

and PPG

) from a noisy

ECG waveform. In the next section, we describe the

overall system, the supervised learning method for

PPG signals generation, and the experimental results.

3 MATERIAL AND METHODS

The architecture of our wearable system is heavily

based on (Tao, Huang et al. 2018). The proposed

electronic textile measured ECG signal and skin

temperature and transmit the data to a mobile

application thanks to the Bluetooth Low Energy

(BLE) protocol. BLE allows a lower power

consumption than other wireless transmissions

protocol (Bluetooth, Zigbee) and improves the system

energetic autonomy. The mobile application by using

the proposed supervised model, reconstruct from the

noisy ECG signal measured by the wearable device,

three clean signals:

- ECG signal: The ECG signal will be use to

estimate the heart rate.

- PPG Red and IR signals: The two signals

will be used to estimate the SpO

By checking the heart rate, SpO

and skin temperature

values (skin temperature>38°C, SpO2<92% and

heart rate>90 bpm), the system allow a quick

monitoring of the wearer health state in real-time.

The generated waveforms, heart rate, SpO

, skin

temperature and COVID-19 patient state are shown

Machine Learning-Based Smart-Textile for COVID-19 Monitoring

173

on the mobile application. Figure 1 shows the adopted

architecture.

Figure 1: Wearable system architecture.

3.1 Wearable Device

The microcontroller unit used is an Arduino Nano 33

BLE Sense. It integrates a SoC ARM ® Cortex ® -

M4 32-bit processor with a clock speed of 64 MHz. It

also integrates a high-performance professional grade

Bluetooth smart radio transceiver to ensure the

bidirectional communication between the wearable

system and the mobile device.

The heart monitor sensor used was AD8232

(Texas Instruments), connected to the microcontroller

via analogic pin.

The temperature sensor was integrated into MPU-

6050 chip, connected to the microcontroller via inter-

integrated circuit (I2C) bus.

Five pads were designed to realize the

interconnections using conductive threads to

peripherals. Two of them in form of snap button were

used to connect a battery and three of them were

considered as textile knitted electrodes to connect

with the heart monitor sensor. The conductive surface

dimension of knitted electrode was 3 cm × 5 cm. The

thread used for knitting the textile electrodes was

sliver-plate polyamide thread (Shieldtex 234/34-2 ply

HCB, Statex Produktions + Vertries GmbH), with

a linear resistance of less than 100 Ω.m

-1

. The

conductive thread between the sensors and the

electrodes is made of copper wires (Elektrisola,

Switzerland), and Lendzing Pro_len R PTFE

(Polytetra_uoroethylene) monofilament (Lenzing

Plastics GmbH, Austria) (Ismar, Tao et al. 2020).

The sampling frequency for heart monitor sensor

was set to 128 Hz for two reasons: 1) The developed

supervised learning model input length must be a

power of 2; 2) Sampling frequency above 125 Hz are

suitable for time-domain analysis and heart-rate

computation (Kwon, Jeong et al. 2018). The skin

temperature was set to 1 Hz (Skin temperature

evolution is slower than ECG signal evolution). The

wearable prototype is represented by Figure 2.

Figure 2: Smart textile prototype.

3.2 Signal Reconstruction Model

One of the main highlights of our contribution is the

signal reconstruction model developed. As we

mentioned earlier, a pulse oximeter integration in a

wearable t-shirt is highly difficult, since the sensor

placement is not optimal. It is a known fact that PPG

signals and ECG signals are heavily correlated.

Indeed the peripheral blood volume change

(describes by PPG signals) is influenced by cardial

muscles contraction and relaxation (which are

describes by the ECG signal). In addition, the PPG

signal peak-to-peak and the R-R peak are correlated,

as describes by Figure 3.

Figure 3: R-R peak and PPG peak-to-peak correlation.

Similar to (Zhu, Tian et al. 2019, Sarkar and Etemad

2021, Vo, Naeini et al. 2021), we propose to use the

correlation between ECG and PPG signals for signal

reconstruction.

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174

Unlike the other contributions however, instead of

reconstruct ECG signal from PPG signal, the model

developed reconstruct from a noisy ECG signal, three

clean signals: A clean ECG signal, a clean PPG

Red

signal, and a clean PPG

signal.

3.2.1 Data Description and Pre-Processing

The pre-processing consists of creating a new dataset

from existing datasets. The dataset use must contains

the three signals (ECG, PPG

Red

and PPG

) Two

publics datasets with ECG signals, PPG

Red

signals

and PPG

signals matched our criteria:

The BIDMC dataset (Pimentel, Johnson et al. 2016)

and Pulse Time Transit (PPT) dataset (Mehrgardt,

Khushi et al. 2022). All the datas were recorded

during human study, using wearable heart monitor

sensor AD8232 (The same sensor we use for our

smart textile) and pulse oximeter MAX30100. Five

steps were followed for data pre-processing.

• PPG Signal Red Retrieval

BIDMC provide ECG signal, PPG

signals and SpO

level. PPG

Red

signals of BIDMC were reconstructed

using the correlation between the SpO

level and the

ratio of ratios R, defined by the equation 2:

𝑅=



𝐴𝐶



𝐷𝐶







𝐴𝐶



𝐷𝐶





(1)

where AC represents the signal amplitude and DC the

signal baseline.

The correlation between 𝑅 and 𝑆𝑝𝑂



is described

by the equation 3 from (Tang, Li et al. 2022), with a

maximum error below 4%, and a confidence level

above 95%:

𝑆𝑝𝑂2= 11.78 𝑅3+55.92 𝑅2+28.84 𝑅 + 97.12

(2)

By normalizing PPG

Red

and PPG

signals

baseline value ( 𝐷𝐶



=𝐷𝐶



), we can retrieve

𝐴𝐶



from 𝐴𝐶



, and thus the PPG

Red

signal.

The resulting ECG-PPG data from BIDMC and

PPT were combined to form a large multi-corpus.

• Signal Resampling and Filtering

Signals were resampled using an interpolation

technique where the sampling rate for all ECG-PPG

records became 128 Hz. A bandpass FIR-filter, as

well as Butter worth filter (Jagtap and Uplane 2012,

Vidhya and Jerritta 2022), were applied to the ECG

and PPG signals. Each signal is split into intervals of

4 seconds each with 1 second of overlapping to avoid

data loss.

• Minmax Normalization. In order to prevent

outlier, min-max normalization was applied in each

signal to ensure that the network data inputs all lie

within the same range.

To preserve the Ratio of Ratios, each PPG

Red

and

PPG

signal has been normalized as follows:

For each sample x

red

and x

, respectively of a PPG

Red

signal red and the corresponding PPG

signal ir, the

normalized values are:

𝑥



𝑥



−max (𝑟𝑒𝑑)

max

(

𝑖𝑟

)

−min (𝑖𝑟)

(3)

𝑥



𝑥



−max (𝑖𝑟)

max

(

𝑖𝑟

)

−min (𝑖𝑟)

(4)

• Data Augmentation

While the obtained signals can be considered as more

than enough (10 000 signals of each category), a data

augmentation has been performed to prevent

overfitting or underfitting. A GAN model was used to

the task (Li, Ngu et al. 2022). The proposed

architecture is divided into two parts: A generator,

which generate synthetized signals by mapping real

signals features, and a discriminator which make sure

the generate signals are close as possible to real

signals. can generate multi-category synthetic time-

series. The model has been trained with our dataset to

generate more ECG and PPG signals. We were able

to generate 120 323 signals of each category (ECG,

PPG

Red

and PPG

)

• Input Signal Dataset Creation

An input dataset of noisy ECG signals was created by

adding a random combination of the three main ECG

noises (motion artifacts, powerline interference,

baseline wander) to the ECG signals.

Each noise signal can be described by the equation:

𝑁

(

𝑡

)

𝐴

.sin

(

2𝜋𝑓𝑡+ 𝜓

)

(5)

With A, the signal amplitude, f the frequency in Hz

and

the phase between [-π,π].

The noise signal is generated by randomly

variates A, f and

ψ.

Baseline wander is a low

frequency noise of 0.5 Hz. Powerline Interference is

a low frequency noise of 50 Hz and motion artifact is

a low frequency noise between 0.5 and 300 Hz.

Machine Learning-Based Smart-Textile for COVID-19 Monitoring

175

3.2.2 Deep Learning Model

The deep learning model architecture described in

Figure 4. The model architecture present as an

Autoeoncoders and can be divide in two neural

networks: An encoder and a decoder.

The encoder role is to learn efficient data

encoding from the signals and pass it into a bottleneck

architecture. In other words, the encoder estimates a

compressed version of the input signal by learning his

features. For this reason, we choose a CNN as an

encoder since the convolutional Layers can easily

extract the signals features. Each convolutional block

consists of:

• A convolution operation, to allow feature

extraction. The equation (6) describes the convolution

operation.

𝑦



=𝑥

(

𝑖−

𝑗

)

.𝑤



+𝑏







(6)

When

corresponds to the filter kernel,

the

filter weights and

the biases,

the feature extracted

and

the signal.

• A layer normalisation, to avoid outlier, speed

up the model training, reduce bias and avoid gradient

exploding (Ba, Kiros et al. 2016). By using the layer

normalization, we ensure that all the signal features

lie withing the same range.

The activation function used is LeakyReLU.

LeakyReLU has been chosen for his efficient

computation, a better gradient propagation, and help

to better handle the vanishing gradient problem, since

it allows a small positive gradient when the unit is not

active.

The decoder role is to establish to relationship

between the reduce representation and the desired

output signals, by minimizing the reconstruction error

(the error between the signals obtained and the real

signals). The decoder network architecture is the

same as the encoder network architecture, except for

using deconvolutional layer for the data mapping

between the reduce representation and the signals.

The deconvolution operation is describes by:

𝑦



=𝑦



(

𝑖−

𝑗

)

.𝑤





+𝑏









(7)

When

corresponds to the filter kernel,

w’

the

filter weights and

b’

the biases, 𝑦



the estimated

output and

the reduced representation sample.

Skipped connection with self-attention block

between layers of the encoder and layers of the

decoder are used for two reasons: Avoiding gradients

vanishing are helping to further learn the

correspondence between the signals.

The kernel size and the number of filters has been

chosen empirically to have the smallest and efficient

model possible (The target device is an edge device).

To our best knowledge, this is the first proposition

of ECG, PPG

Red

and PPG

signals reconstruction

from a noisy ECG signal.

Figure 4: Model Architecture.

3.3 Implementation Details

The TensorFlow library is used for model training

and evaluation. The Adam optimization method is

used for training, with a cyclical a learning rate

between 10

-6

and 10

-4

The learning rate decayed exponentially with a

decay factor of 0.95. Other training parameters

include the batch size (128) and the number of epochs

(100). To guarantee the best performances, the model

has been trained using a k-fold cross validation with

5 iterations. For each iteration, 80% of data were

using for training, 10% for validation, and 10% for

the test.

In this study, the Mean Square Error (MSE), the

Root Mean Square Error (RMSE), and Mean

Absolute Error (MAE) are used as qualitative

performances estimators for all signals.

The MSE represented the standard deviation

between the output predicted by the model and the

actual output. The MSE is defined as:

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176

The MSE describes the standard deviation

between the output predicted by the model and the

actual output. The MSE is defined as:

𝑀𝑆𝐸 =

𝑁



(

𝑥



−𝑥



)







(8)

The RMSE is the squared value of the MSE. It can

be define as the variance between the predicted output

and the desired output. A smaller value of RMSE

corresponds to a smaller difference and better

performance. The RMSE is formulated as follows:

𝑅𝑀𝑆𝐸 =

𝑁





(

𝑥



−𝑥



)







(9)

The MAE is defined as the absolute difference

between the predicted output and the desired

output. A smaller value of MAE corresponds to a

smaller difference and better performance. The

MAE is formulated as follows:

𝑀𝐴𝐸 =

𝑁



𝑥



−𝑥







(10)

Other used qualitative performances are the

Signal to Noise. The SNR is widely defined as the

ratio of signal power to noise power. In other terms,

SNR quantify the robustness of the signal against

noise (A higher SNR means a more robust signal

against noise). The 𝑆𝑁𝑅

in decibels (dB), is

described as the following expression:

𝑆𝑁𝑅



=10log





∑

𝑥

𝑖

𝑁

𝑖=1

∑

(

𝑥



𝑖

−𝑥

𝑖

)

𝑁

𝑖=1



(11)

N is the signal length, 𝑥



is a sample value of the

original signal at time i/N, 𝑥



is a sample value of

the denoised waveform at time i/N.

4 RESULTS

4.1 Cross Validation Results

Table 1 regroups the best cross-validation model

metrics. The results confirm our model can accurately

reconstruct both PPG waveforms and ECG waveform

form a noisy ECG waveform. Indeed, the MSE,

RMSE and MAE show that our model can reconstruct

the signals with a minimal error. The SNR for each

signal shows that the reconstructed signal is robust

against noise.

Table 1: Model performances.

4.2 Real-Time Demonstration

4.2.1 Offline Demonstration

To confirm the model efficiency and reliability, we

aim to recorded at the same time, noisy signals using

our wearable device and the PPG signals. Five

volunteers (Three male and two female) aged

between 20 and 25 years participated to the

experiment. The PPG

Red

and PPG

signals are

recorded from the pulse oximeter sensor MAX30102.

The PPG recording was done by placing the pulse

oximeter sensor at the tip of the finger. Both ECG and

PPG are recorded at the same time. The experiment

duration was 5 minutes for each volunteer. The model

was applied to the recorded data to reconstruct the

three signals.

Figure 5 shows ECG and PPG signals

reconstruction from the noisy ECG signal. The figure

shows that the model reconstructed ECG and PPG

signals correctly. The reconstructed PPG signals are

also robust against noises as shows in Table 2.

Table 2: SNR improvement in reconstructed signals.

Reconstructed

ECG

Reconstructed

PPG Re

Reconstructed

PPG I

SNR(dB) 13.27 8.71 5.36

Metrics ECG signal PPG

Red

signal PPG

signal

MSE 1.1.10

-3

2.7.10

-3

6.3.10

-3

RMSE 0.033 0.0519 0.0794

MAE 0.0185 0.0297 0.0466

SNRout(dB) 18.39 11.10 12.97

Machine Learning-Based Smart-Textile for COVID-19 Monitoring

177

Figure 5: Comparison between reconstructed signals and

real signals.

4.2.2 Online Demonstration

An Android application was developed to receive,

process, and display the data measured by the

wearable system. This decision is motivated by two

points : Using signal processing methods with ECG

and PPG signals, in addition of increasing the

microcontroller power consumption, do not provide

enough satisfying results.; The current

microcontrollers do not have enough memory to use

Deep ML models. By using an Android application as

a gateway since most current smartphones support

Deep ML models (as TensorflowLite file), we

achieve our goal to reconstruct ECG and PPG signals.

The wearable system measure ECG and skin

temperature and send the data to the Android

application using Bluetooth Low Energy. The

sampling frequency used is 128 Hz for the ECG, and

1 Hz for the temperature. The android application

receives the datas and extract the ECG signal and the

skin temperature. The ECG signal and the

temperature are stored , each to an array until the ECG

signal duration is equal to 4 seconds. Then, the

TensorflowLite model reconstruct the signals from

the ECG signal received.

The reconstructed signals are used to estimate the

heart rate (from the ECG signal), the SpO2 (from the

PPG signals). The average skin temperature is

estimated as the mean of the stored temperatures.

Heart rate and SpO

level are estimated from the

generated signals. Heart rate is estimated from the

equation (12). SpO

level is estimate from equation

(2):

𝐻𝑟=60

𝑁

𝑇



(12)

where N is the signal length in seconds, T

is the R-

peak interval length during N seconds.

Figure 6 shows that the Android application can

successfully monitor in real-time skin temperature,

ECG and PPG signals, and estimate heart rate and

SpO

Figure 6: Real-time monitoring.

4.3 Discussions

We believe our proposed solution has the potential to

make a significant impact in the healthcare and

wearable domains, notably for continuous health

monitoring. In addition to be lowcost, our proposed

wearable system assures patient comfort and

mobility, making it suitable for real-time and long-

term monitoring. The integration of the proposed

model offers many advantages to our system:

• A Real-time denoising of the ECG signal,

ensuring continuous signal quality.

• The ability to generate robust PPG signals,

eliminating the need for an oximeter. Initially design

for COVID-19 monitoring, the current system can be

used for other applications (Early diagnosis of

cardiovascular diseases for example).

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Despite its great performances, the current

wearable system has some limitations:

• The current wearable systems mainly realize

real-time detection for the basic symptoms of

COVID-19 from skin temperature, blood oxygen

saturation (SpO2) level, and heart rate. Since these

symptoms are common with other diseases (e.g. Flu),

it is impossible to distinguish COVID-19 from others

without further investigation.

• Human study test must be conducted, to

collect more data and assert the efficiency of the

model and the proposed system. A cross-validation

with practitioners is also collect more data and assert

the efficiency of the model and the proposed system.

The model architecture must be less complex to

be implemented directly to the microcontroller.

5 CONCLUSION AND

PERSPECTIVES

This paper presents a novel textile-based wearable

system for COVID-19 monitoring. By coupling to

wearable system with an AI framework, we can

obtain clean ECG signals and PPG signals for heart

rate and SpO

level estimation. The proposed AI

framework, reconstruct robust ECG and PPG signals

from a single noisy ECG signal measured by the

wearable device. The early experimental measures

confirm our wearable system can be used in a real-

time scenario. Considering that heart failure is one of

the most prominent symptoms of COVID-19, we

believe that the virus presence in the patient organism

can affect the ECG waveform. Our next work will

confuse on the implementation of an AI model that

can distinguish ECG waveform from a healthy patient

to a COVID-19. This information can improve

greatly our current wearable system and help to

monitor patient status in real-time.

ACKNOWLEDGEMENTS

This work was supported in part by the National

National Research Agency (ANR) of France, Ecole

Centrale Lille, and GEMTEX Research Laboratory

under AI_Engineering_PhD@Lille grant.

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