A Wearable System for Electrodermal Activity Data Acquisition in

Collective Experience Assessment

Patr

ıcia Bota

1 a

, Chen Wang

, Ana Fred

1 b

and Hugo Silva

1 c

Instituto Superior T

ecnico, Department of Bioengineering & Instituto de Telecomunicac¸

oes,

Av. Rovisco Pais n. 1, Torre Norte - Piso 10, 1049-001 Lisboa, Portugal

Future Media & Convergence Institute (FMCI), Xinhua Net, Jinxuan Building,

No. 129 Xuanwumen West Street, Beijing 100031, China

Keywords:

Electrodermal Activity Response, Wearable Sensors, Physiological Data, Signal Processing.

Abstract:

In the recent years, we have been observing an increase of research work involving the use of biomedical data

in affective computing applications, which is ever more dependent on data and its quality. Many physiological

data acquisition devices have been developed and validated. However, there is still a need for pervasive and

unobtrusive equipment for collective synchronised acquisitions. In this work, we introduce a novel system,

the Electrodermal Activity (EDA) Xinhua Net Future Media Convergence Institute (FMCI) device, allowing

group data acquisitions, and benchmark its performance using the established BITalino as gold standard. We

developed a methodical experimental protocol in order to acquire data from the two devices simultaneously, and

analyse their performance over a comprehensive set of criteria – Data Quality Analysis. Additionally, the FMCI

data quality is assessed over ﬁve different setup scenarios towards its validation in a real-world scenario – Data

Loss Analysis. The experimental results show a close similarity between the data collected by both devices,

paving the way for the application of the proposed equipment in simultaneous, collective data acquisition use

cases.

1 INTRODUCTION

The ﬁeld of Affective Computing focuses on the com-

puting that relates to, arises from, or inﬂuences emo-

tions (Picard, 1997). The ﬁeld encompasses many

applications, such as the analysis in real-time of the

collective response of an audience during a theatrical

play or show. Collective responses can be applied to:

(1) Testing a group in order to gauge the content mar-

keting success; (2) Direct an artist into new perform-

ing/narrative concepts, leading to futuristic mechanism

where the content narrative adapts to the audience re-

sponse; (3) Enhance remote distributed systems, cre-

ating feedback channels so the actors are in contact

with the audience and aware of their response, and the

audience feels immersed and not like it is watching a

recorded video instead of a live performance, (Wang,

2018); (4) Create personalised list content recommen-

dations; (5) Enhance health applications, and many

others.

https://orcid.org/0000-0002-0514-7517

https://orcid.org/0000-0003-1320-5024

https://orcid.org/0000-0001-6764-8432

However, in order to gauge the collective response,

researchers need pervasive and unobtrusive physiolog-

ical sensors (Bota et al., 2019). Although many have

been developed and validated (Abreu, 2020), for group

settings, such as a theatre or cinema, there is a need in

the literature for devices able to perform data collec-

tion from a group, using several devices synchronously

and simultaneously.

Therefore, in this work we: (1) Introduce the Xin-

hua Net Future Media Convergence Institute (FMCI)

device containing a Electrodermal Activity (EDA) em-

bedded sensor, describing the hardware development

path throughout its various versions, which culminated

in an architecture capable of acquiring EDA data with

quality at least of 20 devices in simultaneously; (2)

Validate the device performance, ensuring its scala-

bility in a real-world scenario, against BITalino, a

well-established and recognised system that we con-

sider as a gold standard. The BITalino device has

been independently recognised as the most prospec-

tive physiological pervasive wearable device for heart

rate (HR) and EDA measurements in affective comput-

ing applications (Batista et al., 2019; Kutt et al., 2018).

To accomplish this task, we follow an experimental

606

Bota, P., Wang, C., Fred, A. and Silva, H.

A Wearable System for Electrodermal Activity Data Acquisition in Collective Experience Assessment.

DOI: 10.5220/0009816906060613

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 2, pages 606-613

ISBN: 978-989-758-423-7

protocol to acquire data from the two devices simulta-

neously – Data Quality Analysis –, and compare the

obtained data using a diverse set of metrics on the

signal morphological structure, Power Spectrum (

score, Pearson Correlation (PC), Root Mean Square

Error (RMSE), Dynamic Time Warping (DTW) dis-

tance), and EDA – speciﬁc events to benchmark the

performance of the FMCI device. Lastly, we perform a

set of experiments to assess the FMCI device data qual-

ity under ﬁve different setup conditions towards the

validation of its performance in a real-world scenario –

Data Loss Protocol.

The remainder of this paper is organised as follows:

In Section 2, we introduce the BITalino and the FMCI

devices, and present a summary of the EDA signal. In

Section 3, we describe the data acquisition protocol

and, lastly, brieﬂy depict the metrics we use as eval-

uation criteria to compare both sensors’ signals. In

Section 4, we present and discuss the experimental

results; and ﬁnally, in Section 5, we outline the main

conclusions, along with future work directions.

2 BACKGROUND

In this section, we introduce the EDA sensor data, the

BITalino and FMCI devices, and describe the FMCI

hardware development path throughout its versions.

2.1 Electrodermal Activity Signal

The EDA typically measures the skin resistance by ap-

plying a negligible current/voltage and reading the volt-

age/current variation output between the two sensor

leads (

G = 1/R; R = V /I

). This is based on the princi-

ple that the electrical properties of the skin change with

the psychophysiological activity of the user (Boucsein,

2012). Moreover, the EDA signal is characterised

by a baseline tonic component – Electrodermal Level

(EDL) – expressing the baseline and thermal regula-

tion activities, from which phasic variations arise from

psychological-related responses. The latter is denoted

as the Electrodermal Response (EDR) signal, derived

from the increase/decrease of perspiration, piloerec-

tion, and vasomotor changes elicited by various emo-

tional states via the limbic system. Figure 1 displays

an example of an EDA signal.

Sweat is highly conductive, thus, modiﬁes the skin

resistance as a result of secretion / inhibition. For this

reason, the EDA electrodes are usually placed at areas

of high sweat gland density, such as on the

phalanx

of the index and middle ﬁngers, the index and ring

ﬁngers, the hand, or feet soles (Schmidt et al., 2019).

Since the EDA can be used as a non-intrusive window

into the ANS activity, it is widely used in biomedical

research for example in diagnostic (Poh et al., 2010),

or emotion recognition (Shukla et al., 2019), polygraph

tests, or stress/relaxation biofeedback.

Figure 1: Signal trace for the FMCI and BITalino devices.

2.2 FMCI Hardware Development Path

The FMCI EDA sensor is based on a operational trans-

conductance ampliﬁer (OTA) and a low-pass ﬁlter

(LPF). The former is used to increase the amplitude of

the weak potential differences generated from the bio-

logical electric signals. The OTA is ideal for measur-

ing signals from low level output transducers in noisy

environments, amplifying the difference between two

input signal voltages, and rejecting any signals that

are common to both input terminals, thus reducing the

undesired source errors. This is known as Common-

Mode Rejection Ratio (CMRR). The typical effective

frequency for the EDA sensors has been characterized

in the state-of-the-art to be between 0.01-1 Hz (Bouc-

sein, 2012). Nevertheless, after the OTA, a second

order low-pass Butterworth ﬁlter (gain = 2, cut-off

frequency = 5Hz) is applied to remove the frequencies

that lie outside the deﬁned range. So far, four proto-

type versions (Figure 2) have been explored through

a continuous development cycle consisting of a thor-

ough analysis of environment testing, user experience

and sensor performance, which we detail below:

• Version 1.

It was developed using Arduino boards,

Xbee wireless transmitters, and ﬁlters – built with

resistors and capacitors to reduce noise interfer-

ence. The measurement system consisted of 15

EDA sensors, 3 Arduino boards and 4 Xbee mod-

ules. Each Arduino board (1 Hz sampling rate)

carrying 5 EDA sensors uses one Xbee module to

send data to a Xbee coordinator, connected to a lap-

top server. The Xbee coordinator applies a polling

scheme to receive packets from the different Ar-

duino boards in order to minimise packet loss ratio

in the wireless communication channel. Some lim-

A Wearable System for Electrodermal Activity Data Acquisition in Collective Experience Assessment

607

Figure 2: The hardware development path showing genera-

tion 1 (left), 2 (top), 3 (right), and 4 (bottom).

itations were found in the ﬁrst generation sensor,

namely: it was not wearable, nor comfortable for

the users to use for a long time; the communication

range (10-20 meters) was rather short for a system

to be used in a collective environment; the battery

consumption was very high, being exhausted after

roughly half an hour of use.

• Version 2.

The JeeNode was selected for the devel-

opment board, as it was small, economic, and can

communicate using the 915 MHz, 868 MHz or 433

MHz band. As a result, the system integrated with

the JeeNode was much smaller and more econom-

ical than Version 1. The standard Lithium Poly-

mer battery (1100 mAh, 3.7 voltage) supported the

JeeNode attached with one EDA sensor (sampling

rate: 10 Hz) to work for over 50 hours. Besides,

the JeeNode supported up to 250 different groups,

each with up to 30 different node IDs. This meant

that large groups could be monitored simultane-

ously. However, the second version still showed

some limitations in a collective environment: the

users did not feel comfortable in terms of sensor

housing and the electrodes; the soldering work in

the lab did not yield reliability when sensors were

used in reality; some mechanical wires in the sys-

tem design created some connection problems in

the experiments.

• Version 3.

It initiated the industrialisation of the

device. Two versions were produced: one for

adults, and one for children. These sensors were

rather small, and each was ﬁtted in a 3D printed

box of 10x5x2 cm. The EDA sensors produced by

the factory could ensure that the quality was robust

and reliable. However, this version was shown not

to be convenient for experimental operators. More-

over, taking off/putting on a sensor housing when

testing a sensor, required high effort. In addition,

there was no way for the operators to monitor the

working states of the sensors.

• Version 4.

In the current version, the circuit de-

sign was upgraded, and the sensor housing was

removed. The circuit was ﬁrst prototyped on a

breadboard and connected to the JeeNode board.

After that, an EDA board was produced to obtain

a compact EDA sensor with the interfaces needed

by the JeeNode board. The JeeNode board and

EDA sensor were connected and mounted in a

customized box produced by laser cutting. Two

electrodes were connected via a cable. In this way,

operators could easily take off the sensor housing,

and test the sensor boards. Additionally, in this

version, a monitoring system to observe the work-

ing state of the EDA sensor was developed. In this

way, it could be seen which node is working or not,

so that it could be ﬁxed or replaced.

2.3 Devices Overview

To evaluate the proposed device, we compare the

FMCI sensor performance against the BITalino

(r)evolution (Batista et al., 2019).

Regarding the BITalino device, for the data acquisi-

tion, the OpenSignals (r)evolution software

was used.

On the other hand, for the FMCI device, a data logger

was developed by the authors, which we made publicly

available

. In both devices, the data was transmitted

wirelessly, and a 2-lead accessory was used during the

data acquisition. Table 1 presents some of the devices

main speciﬁcations; no further information was found

on the Xinhua Net device.

Table 1: BITalino and FMCI device speciﬁcations.

BITalino FMCI

Measurement Range 0-25µS (with VCC = 3.3V)

Bandwidth 0-2.8Hz 0-5Hz

Consumption ∼0.72mA

Input Voltage Range 1.8-5.5V

Sampling Frequency 1000Hz 1Hz

3 METHODS

In this section, we outline the overall methodology we

followed for the benchmark of the FMCI device.

3.1 Data Quality Protocol

As explained in Section 2.1, the EDA sensors measure

the body sympathetic nervous system (SNS) activity

https://bitalino.com/en/software

https://github.com/PIA-Group/python-fmci-datalogger

-example

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

608

through the skin resistance/conductance. To promote

reﬂex responses and enable the benchmarking of the

devices, we devised an acquisition protocol perform-

ing an isometric handgrip test, a common state-of-the-

art clinic test to elicit reﬂex SNS responses (Hilz and

utsch, 2006; Zygmunt and Stanczyk, 2010). Dur-

ing the acquisition, the subjects were asked to remain

seated while an isometric grip test was performed for

5 min. Each experiment was carried according to the

following protocol:

1. Sensors Placement.

Application of the FMCI

wearable, FMCI and BITalino electrodes. To avoid

cross-talk between the two devices, FMCI elec-

trodes were placed on the index ﬁnger and BITal-

ino’s were attached to the ring ﬁnger.

2. Isometric Handgrip Test.

The subject squeezed

a hand handgrip dynamometer at his maximum

contraction strength (calibration) and then had to

maintain above two-third of the maximum contrac-

tion strength for 5 min. The GripIT application

(Costa et al., 2019) was connected to a force sensor

and used by the subject to help them keep control

of the exerted force.

The aforementioned acquisition protocol was per-

formed by 15 subjects with ages 18 to 24 years old.

Three acquisitions were discarded. To synchronise

both devices, a BITalino Light (LUX) sensor was used

to capture the pulse emitted when the FMCI device

was turned on and turned off, i.e. when the device

started and stopped acquiring data.

3.2 Data Loss Protocol

Additionally, to evaluate the FMCI device in group

acquisitions, a test when multiple units are collecting

data simultaneously was performed for ﬁve different

environment conditions:

• Test 1 – 20 Devices in Simultaneous Acquisition

(60-min).

The device’s placement is displayed in

Figure 3. As it can be seen, the antenna was placed

so it was in the direct line of the centre of the

devices.

Figure 3: Devices and antenna placement setup for Test 1 –

20 devices in simultaneous acquisition.

• Test 2 – 10 Devices in Simultaneous Acquisition

(30-min).

In the second experiment, the number

of devices was reduced from 20 to 10, acquiring

data simultaneously, simulating a smaller audi-

ence. The antenna was moved, so it continued

to be placed in the centre of the devices’ group.

• Test 3 – 20 Devices at Different Ranges to the

Antenna (20-min).

With the same disposition as

Test 1, the antenna was moved in order to be at

a distance of: 270, 450, 630, and 900 cm to the

centre line of the devices.

• Test 4 – Occlusion (20-min).

In order to evaluate

the occlusion by the human body, two subjects sat

side-by-side, wearing one device in each hand.

• Test 5 – Random Movement (10-min).

The last

test consisted of the replication of common move-

ment artefacts such as: putting the bracelet on,

random movement, touching the nose and hair,

clapping, and taking the bracelet off.

3.3 Signal Pre-processing

The EDA data can be corrupted by powerline interfer-

ence, thus, in order to remove the noise, a low-pass

ﬁlter with 4Hz cut-off frequency was applied to the

BITalino device data. As stated in Section 2.3, the

BITalino has a sampling frequency of 1000Hz and the

FMCI device of 1Hz, therefore, a downsampling to

1Hz was performed using cubic interpolation. The

data was then passed through a smoothing ﬁlter.

Since scale-dependent metrics were going to be

obtained, and, to remove subjective bias, the data

was normalised per user to values between [0; 1] by

scaled

X−X.min

X.max−X.min

3.4 Performance Evaluation Metrics

In order to compare the waveform similarity of both

devices, we resorted to the following criteria:

• Coefﬁcient of Determination (R

-Score)

(Ross,

2010)

consist of the percentage of variation ex-

plained the correlation between the dependent and

the independent variables. The

score is given

by:

= 1 −

∑

−ˆy

)

∑

−¯y)

. Where,

consists on the

observed data,

¯y

its mean value, and

ˆy

the predicted

value at data point

i ∈ {0,..,N}

;

being the total

number of data points. It presents a range between

negative values and a maximum value of 1 – for

the utmost best score. Thus, a value of

equal to

1 indicates that the model is able to explain 100%

of the variation of the data, while a value of

near 0, indicates that the variance of the output is

A Wearable System for Electrodermal Activity Data Acquisition in Collective Experience Assessment

609

mostly due to error random variables. The

score

is largely used in literature to evaluate regression

models.

• Pearson Correlation

(Kirch, 2008)

measures the

linear correlation between two variables, given by:

ρ =

∑

i=1

−ˆy)(y

−ˆy)

√

∑

i=1

−ˆx)

√

∑

i=1

−ˆy)

. Where

and

rep-

resent two samples indexed at

;

the sample size

and

ˆx

ˆy

, the

and

variables sample mean, re-

spectively. It presents a value between

and

−1

where a value of

denotes total positive correla-

tion,

no linear correlation, and

−1

, total linear

correlation with a negative slope.

• Root Mean Squared Error (RMSE)

(Neill and

Hashemi, 2018)

consists of the aggregation of

the magnitude of the predicted errors, through the

square root of the second sample moment of the dif-

ferences between the predicted values and the ob-

served values. It is given by:

RMSE =

∑

−ˆy

)

Where

ˆy

is the predicted value at index

the

sample value, and

the sample size. It presents

non-negative values, with

denoting a perfect ﬁt

of the model to the data.

• Dynamic Time Warping (DTW)

(Niels, 2004)

measures the Euclidean distance optimal align-

ment between two time series. The optimal

alignment (warping path) consists of the min-

imum value in the

N x M

cost matrix given

C(S

)

, where

:= {s

,...,s

}

, and

:= {s

,...,s

}

are two time series of

length

and

;

M ∈ N

, respectively. The pa-

rameter

C(S

)

, yields a small value (low cost)

and

are similar, and a larger value (high

cost), otherwise.

4 RESULTS

In this section, we ﬁrst present a morphological and

event-based comparison analysis between the FMCI

and BITalino devices (Data Quality Protocol in Sub-

section 3.1). Then, we show an analysis of the FMCI

device data quality over ﬁve different environment se-

tups in order to evaluate its deployment in real-world

scenarios (Data Loss Protocol in Subsection 3.2).

Data Quality Results.

Table 2 and Table 3 show

the experimental results for the Data Quality Protocol

described in Section 3.1.

• EDA Morphology.

In Table 2, we observe that

both devices attain a reasonable correlation (0.65

0.28), displaying a very low value RMSE (0.19

Table 2: Experimental results for the morphological compar-

ison between the data from the two devices in terms of Pear-

son Correlation (PC); Root Mean Squared Error (RMSE);

score; and Dynamic Time Warping (DTW) distance. Nomen-

clature: EDA- Electrodermal Activity; EDR- Electrodermal

Response.

EDA Morphology EDR Morphology Power Spectrum

PC 0.65 ± 0.28 0.15 ± 0.28 0.81 ± 0.07

RMSE 0.19 ± 0.06 0.20 ± 0.03 0.14 ± 0.04

-score 0.54 ± 0.39 0.99 ± 0.65 0.42 ± 0.25

DTW 0.06 ± 0.49 0.09 ± 0.04 0.05 ± 0.03

Table 3: Experimental results for the detection of events

for the BITalino and FMCI devices in terms of: number of

events (N

of Events), offset between the number obtained

in both devices.

Nº of Events EDA EDR

BITalino 19.1 ± 7.0 34.6 ± 13.0

FMCI 18.5 ± 7.4 31.9 ± 12.4

Offset 3.4 ± 2.5 5.8 ± 5.0

0.06), reasonable

-score (0.54.

0.39), and

very low DTW distance (0.06

0.49). Thus, indi-

cating a high morphological correlation between

the data acquired by the two devices.

• EDR Morphology.

Regarding the EDR morphol-

ogy, the Pearson correlation decreases in compar-

ison to the EDA morphology analysis (0.15

0.28), the RMSE and the DTW distance maintain

their low values (0.20

0.03, and 0.09

0.04,

respectively), and the

-score increases to nearly

1. Therefore, although the high-frequency content

of the EDA corrupts the EDR information which

results in the detriment of the Pearson correlation,

both signals still maintain high correlation.

• Power Spectrum.

Additionally, for the power

spectrum the correlation is very high (0.81

0.07),

with very low RMSE and DTW distance (0.14

0.04, and 0.05

0.03, respectively), therefore, de-

noting a very high spectral correlation between

both devices signals. The

-score, is the lowest

from all the representations, possibly due to the

power spectrum being a very noisy signal with low

linear dependency.

• EDA Signal Characteristics.

Table 3 shows that

the EDA morphology, in the BITalino data ob-

tained 19.1

7.0 events, while in the FMCI de-

vice 18.5

7.4 events were found, thus resulting

in an offset of 3.4

2.5 events. On the other hand,

for the EDR morphology, in the BITalino data we

obtained 34.6

13.0 events, while for the FMCI

device we observed 31.9 ± 12.4 events.

We can conclude that overall a similar number of

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610

events is detected in both devices, with the exception

of a few users that present higher discrepancy, aug-

menting the standard deviation and introducing bias.

Better results would be expected if both devices were

locally and synchronised in time, i.e. the data were

obtained in the same body localisation, and perfectly

timely synchronised. By an empirical observation of

the data, overall, we also highlight that the same mor-

phological trends were noticed in both devices’ data,

however, with magnitude offsets and time warping is-

sues. This can also explain some of the discrepancies

in results.

Data Loss Results.

In this subsection, we present

the results for the ﬁve experiments in the Data Loss

Protocol described in Section 3.2.

• Test 1 – 20 Devices in Simultaneous Acquisi-

tion.

The experimental results showed that only

devices 109 and 110 exhibited transmission errors.

The devices were placed in the same chair, close to

each other, so interference could have caused the

malfunctioning. Device 110 displays the higher

error rate, with a value of 0.12 errors/s, while de-

vice 109 presents an error rate of 0.01 errors/s;

the remaining devices (18 devices) returned no er-

rors. Additionally, a timeline view of the errors

shows that these were systematic; i.e. consistent

throughout the acquisition.

• Test 2 – 10 Devices in Simultaneous Acquisi-

tion.

During the second experiment, the error rate

of device 110 persisted with a similar value (ap-

proximately 0.12 errors/s). However, new devices

(107, 111), started to return errors. The device 109

displayed an error rate of 0.2 errors/s, while de-

vice 111 an error rate of around 0.1 errors/s. Once

again, the errors were consistent throughout the

experiment.

• Test 3 – 20 Devices at Different Ranges to the

Antenna.

The results for a distance between the

devices and the router of 270, 450, 630, and 900

cm are displayed in Figure 4 to Figure 11, in Ap-

pendix. In the timeline view of the errors, as for the

previous experiences, the error rate was consistent

throughout the acquisition protocol. At the ﬁrst

distance (270 cm), closer to the antenna, devices

109 and 110, on the whole, maintained their error

rate. A new device, device 115, started to display

errors, with a high error rate (approximately 1.5 er-

rors/s). At the second distance iteration (450 cm),

the devices returning an error were maintained,

however, the error rate from each decreased. Next,

for the third distance iteration (630 cm), the num-

ber of devices displaying errors increased to 6. The

error rate in the devices showing errors at the previ-

ous iteration expanded signiﬁcantly. At the fourth

distance iteration (900 cm), a higher number of de-

vices have shown data loss (9 devices) with error

rates from 0.1 to 1.3 errors/s.

• Test 4 – Occlusion.

In a 20-minute experience,

no errors were obtained. Consequently, we can

conclude that body occlusion did not increase the

number of the devices’ errors.

• Test 5 – Random Movement.

Only one device

displayed errors, with an error rate of 0.08 errors/s.

Once again, the errors were consistent throughout

the experiment.

To conclude, after analysing the ﬁve setup experiments,

the FMCI device shows suitability to be used in simul-

taneous collective acquisitions, showing acceptable

packet loss.

5 CONCLUSION

Recent advances in wearable technology and its pro-

liferation into people’s daily living lead to a diversity

of devices focused on the acquisition of physiological

data. Additionally, we have been observing a transfor-

mation in entertainment, bringing new challenges and

possibilities to media providers and content creators.

Physiological data, namely the EDA, can be used to

measure an audience response and provide meaning-

ful information for both the audience, the artist and

producers, paving the way for futuristic shows and

entertainment experiences, in both co-located or dis-

tributed setting. Still, there are few practical options

for simultaneous data acquisition (e.g. in an audience

setting).

In this work, we (1) Introduce a new wearable de-

vice for EDA sensing, the Xinhua Net FMCI device,

which expands the current state-of-the-art by allowing

collective, simultaneous acquisition of data; (2) Eval-

uate its performance against the BITalino device, a

reference and recognised system. In order to perform

this task, we follow two protocols: (1) Data Quality

Protocol – replicating a methodological experiment

based on a common state-of-the-art test to elicit the

SNS activity, namely the isometric handgrip test; (2)

Data Loss Protocol – examining the device packet loss

during ﬁve different setup conditions.

In both protocols, the devices showed high simi-

larity between the acquired data, and no signiﬁcant

data loss was observed in a collective setting with

multiple devices acquiring data simultaneously and

synchronously. Therefore, we can conclude its applica-

bility for future research in collective data acquisition

A Wearable System for Electrodermal Activity Data Acquisition in Collective Experience Assessment

611

experiences.

Given the experimental results, we identiﬁed the

following research lines for further work: (1) Addi-

tion of further sensor modalities such as ECG, BVP

or HR; (2) Addition of a button or auxiliary channel

for synchronisation to further enhance the research

applicability; (3) Development of an online interface

for real-time visualisation of the data; (4) Data quality

index calculation, returning the usability/quality of the

data; (5) Increase of the sampling rate; (6) Application

of anonymization and data-privacy methodologies; (7)

Validation of the FMCI device over well-known data

and information-quality frameworks.

ACKNOWLEDGEMENTS

This work has been partially funded by the Xinhua

Net Future Media Convergence Institute under project

S-0003-LX-18, by the Ministry of Economy and Com-

petitiveness of the Spanish Government co-founded by

the ERDF (PhysComp project) under Grant TIN2017-

85409-P, and by IT - Instituto de Telecomunica

c¸

oes

in the scope of program UIDB/50008/2020. The au-

thors would also like to thank to Carolina Bento for

the support to the experimental part of the work, and to

all the volunteers that participated in the Data Quality

Protocol.

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APPENDIX

Figure 4: Number of errors per device for Test 3 – 20 devices

in simultaneous acquisition at a 270 meter distance.

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Figure 5: Timeline view of the devices’ errors for Test 3 – 20

devices in simultaneous acquisition at a 450 meter distance.

Figure 6: Number of errors per device for Test 3 – 20 devices

in simultaneous acquisition at a 270 meter distance.

Figure 7: Timeline view of the devices’ errors for Test 3 – 20

devices in simultaneous acquisition at a 450 meter distance.

Figure 8: Number of errors per device for Test 3 – 20 devices

in simultaneous acquisition at a 630 meter distance.

Figure 9: Timeline view of the devices’ errors for Test 3 – 20

devices in simultaneous acquisition at a 630 meter distance.

Figure 10: Number of errors per device for Test 3 – 20

devices in simultaneous acquisition at a 900 meter distance.

Figure 11: Timeline view of the devices’ errors for Test

3 – 20 devices in simultaneous acquisition at a 900 meter

distance.

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