An Experiment to Assess an Acquisition Platform and Biomedical

Signal Conditioning

Diego Assis Siqueira Gois, João Paulo Andrade Lima,

Marco Túlio Chella and Methanias Colaço Rodrigues Júnior

Computer Department, Sergipe Federal University,

Av. Marechal Rondon, S/n - Jardim Rosa Elze, São Cristóvão - SE, Brazil

Keywords: Acquisition, Conditioning, ECG, EMG, EOG, Usability.

Abstract: As physical computing has grown and the concept of “Do it Yourself” (DIY) increased, various open-source

electronics platforms emerged, such as Arduino and Raspberry pi. Still, these platforms aren't suited for

acquisition and conditioning of biomedical signals. Inspired by the DIY concept, this paper presents a

framework for acquisition and conditioning of biomedical signals composed of various interconnected,

interchangeable, inter-configurable and reconfigurable boards, called YouMake. Moreover, they are low cost

and have good documentation, making it easy for prototyping. The experimental evaluation of the platform

was performed in a group of people who used it to show the level of usability and the time spent. The results

showed that there are no statistical differences between the groups “with experience” and “without

experience”, and even more, that it can reliably be used for a low cost alternative for acquisition and

conditioning of biomedical signals.

1 INTRODUCTION

The Luigi Galvani (1737-1798), Alessandro Volta

(1755-1832), George Ohm (1787-1854) and Michael

Faraday (1791-1867) researches provided the basis

for the understanding of electrical potential and

electric current, which helped studies of the electrical

properties of cells and tissues, also known as

electrophysiology. They have also shown that living

tissues have electrical properties (Collura, 1993).

Collura (1993) also claims that the first scientists

to focus their work on the electrical phenomena were

Carlo Matteucci (1811-1868) and Emil du Bois-

Reymond (1818-1896). The first studied the muscle

properties of frogs and was the first to observe the

potential action that precedes the contraction and the

extent reduction of muscle during this contraction.

Meanwhile, Du Bois-Reymond built a galvanometer

of more than 4000 turns of wire in its coil, increasing

its sensitivity. Moreover, Du Bois-Reymond

developed non-polarizable electrodes made of clay

and understood the importance of their use.

This study and the use of physiological signals

increased in the engineering community. Thus, new

application fields were born in addition to the more

traditional areas of medicine. Such applications range

from monitoring of human activity, human-machine

interactions in games, and even biometrics, through

new systems based on electrocardiography

(Guerreiro, 2013).

As the high cost of professional equipment

prevents the use of such equipment for engineering

students in the field of physiological signals,

alternatives to acquisition and conditioning of these

signals are necessary.

In practice, it is often necessary to make

measurements of different electrical human signals

through simple devices. Although there are many bio-

amplifiers with excellent precision and multi-

channel, these are very expensive for general purpose

(Babusiak and Borik, 2013).

Physical computing has grown as a field in its own

right field (O'Sullivan and Igoe, 2004) and with the

increasing concept of “Do it Yourself” (DIY) various

open-source electronics platform emerged, such as

Arduino and Raspberry pi.

However, until now, physical computing has been

mainly used with equipment designed to meet

requirements which are not compatible with the

acquisition of physiological signals, such as relatively

high noise tolerance and low sampling rate (Silva et

Gois, D., Lima, J., Chella, M. and Júnior, M.

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning.

DOI: 10.5220/0006293100750086

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 3, pages 75-86

ISBN: 978-989-758-249-3

al., 2014). In addition these requirements,

physiological computing requires a circuit for

acquisition of biomedical signal, which is not suitible

through equipment designed for physical computing.

Thus, equipment commonly used in physical

computing such as Arduino or Raspberry Pi are not

viable in physiological computing, because they

interact with simple actuators and sensors, not having

channels for acquisition of biomedical signals and

besides having few signals requirements.

Since then, many researchers turned to the study

of physiological signals, improving the acquisition

and conditioning of signals obtained and making it

possible to find high-precision apparatus for use in

modern medicine. However, such devices are

expensive as they are meant for professional use in

hospitals and clinics.

Thus, continuing the "DIY (Do-It-Yourself)"

idea, this work presents a platform for acquisition and

conditioning of physiological signals with low cost,

versatile, generic and easy prototyping. This platform

has the characteristics the fact of being composed of

interlocking, interchangeable, inter-configurable and

reconfigurable boards. It also has a strong

documentation, enabling easy prototyping and

manipulation.

The evaluation of this tool was made by the SUS

scale developed by Brooke (1996) in what concerns

the usability of the system. For comparison, it was

used the usability of “experienced people” with “less

experienced people” in the studied area, and a

comparison was made with another work which used

the same scale in its context.

The development of a tool such as presented here

may be of interest to the Hardware laboratory in the

computer department of the Sergipe Federal

University (UFS) and other engineering such as

electronics or electric, as well as people who work

with biomedical engineering. The technology domain

enables its flexibility and adaptation in several

different surveys, enabling the possibility of

integrating hardware with various laboratory

equipment, allowing undergraduate and postgraduate

students work in biomedical engineering.

The results of this experiment showed that there is

no statistical difference between the prototyping time

obtained for the "experienced people" and

"inexperienced people" groups, also showing the

platform usability note with a value A+ (on a scale

going from F to a A+). It was also shown that the

average usability of the "experienced" group is not

different from the average usability of the "no

experience" group, thus showing that a person with

no experience in the field makes the prototyping with

the same ease of a person with experience due to the

platform's usability.

The work is divided into eight sections, the first

introduction, and the second related work. The third

section presents the methodology of the work

focusing on describing how the board is designed and

assembled. The fourth section contains the

experiment planning and the fifth section details the

operation of the same. The sixth section presents the

results and discussion of the experiment. The seventh

section highlights the threats to the validity of the

experimental study and eighth section presents the

conclusion and future work.

2 RELATED WORKS

Due to mismatch between the relevance and

timeliness of biomedical engineering and the

structure of electrical engineering courses in Brazil

(Andrighetto et al., 2008), a postgraduate team of

biomedical engineering institute of UFSC has

developed a platform called SPSB-MD (Biomedical

Signal Processing System - Teaching Modules) for

acquisition and digitalization of electrocardiogram,

electromyogram, electrooculogram and

electroencephalogram signals, in order to fill the gap

in biomedical engineering disciplines in

undergraduate and postgraduate in UFSC. Despite the

platform developed by Andrighetto et al. (2008)

allow user access to analog components, it has

distinct modules for acquisition and conditioning of

each type of physiological signal. Unlike YouMake

that has a module of acquisition and signal

conditioning that can be modified by the user through

prototyping to the specifications of the signal of

interest.

The BITalino, developed by Silva et al. (2014)

consists of a hardware card type framework for

acquiring physiological signals focused in all in one,

low cost. This cost is €149 in a configuration "Board

Kit", €159 in the configuration "Freestyle Kit" and

169 euros in the configuration "Plugged Kit"

(Bitalino, 2016). This platform is for general purpose

and able to acquire electromyography signals,

electrocardiography, electrodermal activities and

accelerometry by fitting sensors blocks in the control

block. While BITalino does not allow the change and

adjustment of frequency bands and gains in filters and

amplifiers, YouMake allows the user to modify these

values freely, adapting the platform to the signal of

interest. Furthermore, in BITalino it is not possible to

connect a sensor block in another sensor block, in

order to integrate the filter and improve them. In

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YouMake it is possible to connect the boards to

integrate filters and gains of the amplifiers. There is

still a difference in cost, since the BITalino has

estimated cost of between €149 and €169 and

YouMake can be mounted with only €3,06.

Babusiak and Borik (2013) developed a four-

channel amplifier for measuring the

neurophysiological signals of humans, able to acquire

the electrocardiogram, electroencephalograms and

electrooculogram signals. This amplifier features

variable gain and programmable through digital

potentiometers, and allows you to change the lead in

the measurement, considering the type and

characteristics of the signal to be measured. However,

in this device it is not possible to learn through

prototyping and even interconnection between filters,

which can be found in YouMake.

Meanwhile, Zanetti (2013), decided to develop a

platform focused in the acquisition of

electroencephalogram called RITMUS, having high

performance, but because of its robustness, the price

is high, around $495.36. Besides the high price, it is

not as versatile and general as YouMake.

Finally, much of the work related to the

acquisition of biological signals uses it for an

application, such works develop a platform specific

to acquire the desired signal, this is the case of Silva

et al (2008), which develops a platform to acquire the

signals of heart rate, respiration, and galvanic skin

response in order to detect anxiety levels. Thus,

Vijayprasath, Sukanesh and Rajan (2012) also

focused on creating a platform for a specific

application. This platform performs the acquisition

and amplification of electrooculogram signals in

order to use such signals in mouse cursor control

through the eyes.

3 METHODOLOGY

As this work is to develop a low-cost platform for

acquisition and conditioning of biomedical signals,

specifically ECG, EOG and EMG, the first step was

to design the acquisition and conditioning circuits,

paying attention to the use of cheap and commercial

components the manufacture of the boards.

With the boards ready and the components

purchased, a guide and video was made for the user,

in which show how to mount the platform and use it.

For evaluation of the work, usability study for the

platform was performed, checking the easiness of and

the average time spent by users to mount. This

usability study was done experimentally and with

humans, for this reason, the study was submitted to

the ethics committee and approved under the number

CAAE: 58536416.6.0000.5546.

The block diagram in Figure 1 illustrates the

YouMake’s modules.

The following sections describe how it was

designed and assembled the proposed platform.



3.1 Materials

The necessary low cost materials for the assembly of

the platform are:

• Set of printed circuit boards with the project that

will be shown in the following sections: each plate

cost $1.4 and it took only two plates in this work;

Figure 1: Platform block diagram.

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning

• Integrated circuit LM324: only one was used and

cost $0,32;

• Integrated circuit AD620: It was not used in the

experiments, but could be used as an option for

LM324 in the platform. The average cost is $11,94;

• Integrated circuit LM741: two were used in this

work. About $0,32 each;

• Electronic Components: Resistors and

capacitors costing a few cents each.

Thus, the estimated cost for the assembly of the

platform in this paper was about $3,23 using LM324,

3.2 Supply

The supply of the whole circuit was made with two

9V batteries connected in series forming a

symmetrical source of + 9V and -9V. However, it can

be powered by any source of symmetrical voltage 5V

to 15V. Special care should be taken, since the

maximum voltage of the supply is the maximum

value that the signal output voltage can reach, due to

saturation of the operational amplifiers.

3.3 Biomedical Signals Acquisition

Circuit

Figure 2 shows the schematic of the data acquisition

board divided into blocks for better viewing. In

addition to the blocks, the terminals were named

curtly and standardized so that would fit on the board.

The following shows the nomenclature of each

terminal:

• IN1.AD: electrode input 1 to the circuit using the

AD620 and must be connected to an electrode;

• IN2.AD: electrode input 2 to the circuit using the

AD620 and must be connected to an electrode;

• TO.AUX.AD: AD620’s circuit output that must

be connected to the reference circuit (IN.AUX

terminal).

• SAIDA.AD: output of the acquisition circuit

formed by AD620 and must be connected at the input

of the conditioning plate;

• IN1.LM: electrode input 1 to the circuit using the

LM324 and must be connected to an electrode;

• IN2.LM: electrode input 2 to the circuit using the

LM324 and must be connected to an electrode;

• TO.AUX.LM: instrumentation amplifier output

formed by the LM324 and must be connected to the

reference circuit (terminal IN.AUX);

• SAIDA.LM: output of the acquisition circuit

formed by the LM324 and must be connected at the

input of the conditioning board;

• IN.AUX: reference circuit input and must be

connected to TO.AUX.LM terminal if the user is

using the acquisition circuit formed by LM324, or

TO.AUX.AD terminal if the user is using the

acquisition circuit formed by AD620;

• SAIDA.AUX: the reference circuit output and to

be connected to an electrode;

• V+: supply positive voltage;

• V-: supply negative voltage;

• TERRA: supply reference (ground).

Block 1 of Figure 2 shows AD620 integrated

circuit, a circuit suitable for acquisition of biomedical

signals. It has three resistors, where R13 and R14 are

set to 22K forming a voltage divider in TO.AUX.AD

terminal and a resistor R12 which is the resistor which

can be varied to obtain different gain values. The

terminal TO.AUX.AD must be connected to IN.AUX

in block 4 to use the reference circuit with AD620.

The circuit’s gain is given by formula 1. Such gain

should not be too high, otherwise a significantly

increase in gain at this stage may adversely affect the

signal with noise. The maximum gain achieved,

without much noise, was 100, being advisable a gain

smaller than 10 at the early stage. This paper uses a

gain of 6.26.

= (49.4K)/(G—1) (1)

The block 2 of Figure 2 shows the instrumentation

amplifier mounted with LM324, where the values of

the resistors R2 and R3 were selected to be 47K (these

values enable a wide range of gain variation

according to the value of R1). The value of the gain

of this amplifier is given by (2), such gain is varied in

accordance with the resistor R1 (also called gain

resistor) as it can be seen in Figure 2. This gain must

not be too high, since a high gain at this stage may

harm the signal with noise. The maximum gain

achieved, without much noise, was 8, being advisable

not to exceed this value. This paper uses a gain of

7.26.

G = 1 + ((R3 x R2)/(R1)) (2)

Block 3 of Figure 2 is rejection circuit for

common mode signal, where all resistors have the

same value, 10K (R4 = R5 = R6 = R7 = 10K). These

values mean that there is no gain in this block.

For acquisition of biomedical signal a reference is

required to measure between IN1 and IN2. This

reference is provided by the circuit in block 4, as

shown in Figure 2.

Resistor values in the block 4 were chosen

according to the datasheet of AD620: R10 = 10K and

R11 = 1M. The capacitor C1 has its value set at

100nF. The input circuit (IN.AUX) must be

connected to the terminal TO.AUX.LM, which is

located at the voltage divider formed by R8 and R9

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Figure 2: Acquisition board of biomedical signals.

(both resistors have the 22K value) if the user decides

to use the LM324 circuit. If you want to use the

AD620 circuit, the input of the reference circuit

(IN.AUX) must be connected to the terminal

TO.AUX.AD, which is located at the voltage divider

consisting of R13 and R14 (both with 22K values).

Observing Figure 2, note that there are two

separate and distinct acquisition circuits, one formed

by AD620 and another by LM324, and blocks 2 and

3 are connected, because together they form the

acquisition circuit through LM324. Thus, when one

of the two is chosen to be used, you only need to

solder the components referred on the chosen circuit,

including the electrodes. Also, the block 4 is shared,

so if you are using the acquisition circuit formed by

AD620, it is necessary that the input of the reference

circuit formed by block 4 is connected with block 1.

However, if you are using an acquisition circuit

formed by LM324, it must connect the block 4 input

to terminal TO.AUX.LM between the blocks 2 and 3.

3.4 Biomedical Signals Conditioning

Circuit

Figure 3 shows the schematic of the conditioning

board. It is divided into blocks for better viewing. In

addition to the blocks, the terminals were named

curtly and standardized so that they fit on the board.

The following shows the nomenclature of each

terminal:

• ENTRADA.INVERSOR: inverting amplifier

input. Must be connected to the output of the

acquisition board;

• S.AMP.I: inverting amplifier output;

• ENTRADA.FILTRO: filter input;

• S.F: filter output;

• V+: supply positive voltage;

• V-: supply negative voltage;

• TERRA: supply reference.

Figure 3: Conditioning circuit schematic.

Figure 3 shows the schematic of the conditioning

board. Block 1 consists of an inverting amplifier

formed by a LM741, where the gain can be seen in

(3), for being an amplifier in the inverting

configuration, it inverts the input signal. In this block

there is a coupling capacitor C1, since it is positioned

at the signal input, it also functions as a high-pass

filter with a cutoff frequency defined by (4).

G = -R1/R3 (3)

= 1/(C1 x R3)

(4)

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning

It is in block 1 of Figure 3 that is applied the

largest gain in the signal, in the order of tens or

hundreds. Thus, R1 and R3 values are not fixed, and

the values depend on the signal gain. The resistor R2

is used to minimize the effect of operational amplifier

input bias current, so the resistors R2 and R1 must be

equal.

Block 2 of Figure 3 shows the Butterworth type

active low-pass filter and following the Sallen & Key

setting that is applied to the signal, this is a first order

filter and can be easily changed to a high-pass filter

only inverting the position the capacitor with the

resistor. R4 and C2 values were not fixed, as these

depend on the value of the cutoff frequency of the

filter (5).

= 1/(2 x π x R4 x C2) (5)

4 EXPERIMENT PLANNING

4.1 Objective Definition

The purpose of this experiment is to evaluate, through

a controlled experiment, the acquisition platform of

biomedical signals using the Brooke systems (1996)

usability scale as a measuring tool. This experiment

will target two groups of participants, a group with

experience and one without experience in electronics.

The goal was formalized using the GQM model

proposed by Basili (1984): analyze the biomedical

signals platform in order to evaluate with respect to

the usability and time prototyping in the point of

view of students and former students of

undergraduate and postgraduate with and without

experience in the study area, in the context of people

interested in the study area.

4.2 Hypothesis Formulation

In this experiment, we want to answer the research

questions QP1, QP2 and PQ3:

 QP1: The group with the most knowledge and

experience in the area has an average value of

usability higher than the group with less experience

and knowledge?

 QP2: The platform can be used as a low cost

alternative for the acquisition and conditioning of

biomedical signals?

 QP3: The group with the most knowledge and

experience in the area has the lowest average value of

prototyping time than the group with less experience

and knowledge?

To assess these questions, three metrics will be

used:

 Average usability of each group

(SUS_Valor);

 Overall average value of the usability of the

experiment;

 Average time prototyping of each group.

With the research questions and metrics defined,

the following hypotheses were defined:

a. Hypothesis 1

 H

0SUS

: The average usability value of the

"experienced" group is equal to the average usability

value of the "no experience" group.

 H

aSUS

: The average usability value of the "with

experience" group is higher than the average usability

value of the "no experience." group.

b. Hypothesis 2

 H

0tempo

: The average prototyping time of the

"experienced" group is equal to the average

prototyping time of the "no experience." group.

 H

atempo

: The average prototyping time of the

"experienced" group is higher than the average

prototyping time of the "no experience" group.

4.3 SUS Usability Scale

Usability is a quality suitability of any device, for a

particular purpose, and their ability to be used in a

given context. For being a subjective and complex

evaluation, Brooke (1996) developed the Systems

Usability Scale (SUS), which is a scale of ten simple,

fast and reliable items that provides an overview of

subjective usability reviews and is used as a tool to

measure the usability of a wide variety of products

and systems.

Composed of 10 questions that evaluate the

effectiveness, efficiency and satisfaction of the user

in relation to a particular product or service, the SUS

scale has three characteristics that make it quite

attractive in usability measurement. First is a scale

with few questions, which makes it quick and easy for

both respondents and the research administrator to

calculate the values. Secondly, it is a free scale, which

can be used without the need for any payment. Third,

the SUS is an agnostic of technology, and can be used

by a large group of professionals in the evaluation of

almost any type of interface or product. Finally, the

result is a single score, ranging from 0 to 100, which

is relatively easy to understand by people from

different study areas (Bangor, Kortum and Miller,

2009).

SUS works as follows: the user reads a statement

about the system he wants to evaluate and then

immediately must choose from a five-point scale

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ranging from strongly disagree to strongly agree of

the statement.

With the questionnaire completed, the user’s

opinion is converted into a score that represents the

system usability for that individual.

4.4 Variables

We describe in this section the independent,

intervening and dependents variables of the

experiment in this paper.

For independent variables, there are the tool used

in case the biomedical signals acquisition and

conditioning platform YouMake and its electronic

components.

For dependent variables, there are two metrics:

the average prototyping time, which was obtained by

means of a chronometer and the average usability of

SUS scale (Brooke, 1996).

As intervening variable, there is the emotional

state of the participants, as they might be nervous

during of the experiment.

4.5 Participants

Tullis and Stetson (2004) claims the use of SUS scale

permit to obtain a system usability mean with a small

sample number (8-12). Besides that, the authors assert

this sample number has a confidence enough of a

good evaluation of how people see your system or

product. Thus, it was chosen twenty four participants

for the study.

The question P1 was asked at twenty-four

participants, mixed among students and former

students of undergraduate and postgraduate in UFS,

with the sole purpose to classify the participants into

more experienced or less experienced in the study

area.

• P1: Have you ever studied electronics?

Thus, the participants were divided into two

groups, the group G1, with experience, represented

by the people who answered yes and the second group

G2 that have only basic knowledge of circuits,

represented by those who answered no.

4.6 Pilot Study

Before the experiment, a pilot study was conducted

with student with a master in electrical engineering

and a degree in electrical engineering. This student

has experience in electronic circuits and is currently a

Professor of in the Electrical Engineering Department

of the Federal University of Sergipe - Campus São

Cristóvão (Sergipe, Brazil). This study was

conducted in a laboratory at the Federal Institute of

Sergipe - Campus Itabaiana (Sergipe, Brazil). It was

given to the participant a user guide and a video

showing how to prototype ECG, EMG and EOG.

Soon after, the participant performed the

experimental work described in the following

sections.

The participant got a 12 minute prototyping time

and 95 points in SUS usability scale. The pilot study

was used to better understand the study procedures. It

also helped to assess the usability obtained by a

person with considerable experience, and the time

required to perform the task.

Thus, the pilot study was useful to show that it

was possible to prototype the experiment in a timely

manner. It also showed that people with enough

experience possibly will feel easily in prototyping.

4.7 Experiment Design

4.7.1 ECG Acquisition and Conditioning

For acquisition and conditioning of the ECG, which

is the potential heart rate, it is needed to position the

surface electrodes that will acquire the signal of the

first derivation of the ECG in the chest, just below the

shoulder and the reference in the right arm, as shown

in Figure 4. This figure also shows the acquisition

circuit using the AD620. Since, with the LM324 it is

similar.

In this paper, the ECG is detected through surface

electrodes, which requires a passing frequency range

between 0,67-40Hz (Prutchi and Norris, 2005). Also,

one needs a high gain, since the ECG signal

amplitude is around 1 mV (Babusiak and Borik,

2013).

As has already been performed several tests in this

work, commercial values of components that enable

the display the ECG with a good degree of acceptance

were found. They are:

• Gain of 6.26 in the acquisition board in case of

using the AD620 circuit, also possible with a gain

resistor of 6,8KΩ;

• Gain of 7.26 in the acquisition board in case of

using the circuit LM324, this gain is also possible

with a gain resistor of 15KΩ;

• Gain of 150 in the conditioning board through

the inverting amplifier in block 1 of Figure 3, using

R1 = R2 = R3 = 1KΩ 150KΩ;

• High pass filter of about 0.5 Hz with a 2200uF

capacitor C1 shown in block 1 in Figure 3;

• Low pass filter of about 34Hz through a 47K

resistor in R4 and a 100nF capacitor C2 in block 2 in

Figure 3.

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning

Figure 4: ECG acquisition schematic.

If the signal appears inverted, there is no problem,

since the voltage difference between the inputs is

acquired to observe the signal, reverse the electrodes

on the chest, placing what was on the left side goes to

the right, and what was on the right side goes to the

left side. This will make the signal before appeared

inverted and it will shown correctly.

In addition to these values, it is possible to vary

both the gain and the frequency passband and special

care should be taken with the 60Hz interference from

the power grid. For this, it is interesting to acquire the

signal away from wires connected to the electricity

grid and, if necessary, apply a band pass filter to

eliminate the interference (this was not applied in this

paper).

4.7.2 EMG Acquisition and Conditioning

For acquisition and conditioning of the EMG, it is

necessary to position the electrodes that will acquire

the muscle signal. For this, one electrode is placed in

the middle of the muscle and other electrode in

muscle base as shown in Figure 5. These positions

serves for both the AD620 and for the LM324, simply

connect the electrodes wires as in ECG.

The procedure is equal to ECG, but the passband

of the EMG signal with surface electrodes is

generally between 2-500Hz frequency and amplitude

between 50μV and 5mV amplitude (Cohen, 2006).

So the cutoff frequency of the low pass filter was

changed to 498Hz and the high pass remained the

same, in 0.5Hz, which had a good answer. The gain

in the conditioning board in Figure 3 was changed to

56, since the EMG signal has higher amplitude than

ECG.

4.7.3 EOG Acquisition and Conditioning

For acquisition and conditioning of EOG, one needs

to position the electrodes that will acquire the small

signal of the eye movement. The positioning depends

on which parts of the eye movement is wanted. If it is

the signal from moving to the left or to the right, the

electrodes are positioned at the side of the right

eyebrow and the other at the side of the left eyebrow.

If it is the signal from moving up, down, or blinking,

the electrodes must be placed as in configuration 2 in

Figure 6. The reference electrode is always at the

bone behind the left ear. Such placement serves for

both AD620 and LM324, simply connect the

electrode wires as in ECG.

Figure 5: EMG electrode positioning. SOURCE: adapted

from (Backyard Brains, 2016).

Figure 6: EOG electrode positioning. SOURCE: adapted

from (Backyard Brains, 2016).

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The value of the EOG signal varies from 50 to

3500µV with a frequency range between 0.001-

100Hz (Barea et al., 2002). The procedure is the same

as the ECG, but the low pass filter passband changed

to 1.5Hz and the gain to 220 times in the conditioning

board in Figure 3. Although there are frequencies up

to 100Hz in EOG, a 1.5Hz filter exhibits an

acceptable signal.

5 EXPERIMENT STEPS

In the following sections, the steps for the operation

of the experiment are presented, ranging from

preparation and implementation to validation of data.

5.1 Preparation

To prepare the participants for the experiment, a

quick assembly guide and a video showing how to

prototype EMG, ECG and EOG were provided.

Furthermore, a framework with the components (the

fixed ones) and connectors at the locations of the

variable components were provided already welded.

5.2 Execution

The experiment was conducted in the hardware

laboratory of the Federal University of Sergipe -

Campus São Cristóvão and in the electronics

laboratory at the Federal Institute of Sergipe –

Campus Itabaiana. After watching the video, reading

the user guide and answered some questions from the

participants, the experiment was started. It was the

prototyping of the acquisition and conditioning of

EMG, ECG and EOG signals, it is worth mentioning

the electrodes were placed on the body of the author

of this paper.

• Data Collection

After the experiment, the participants answered

the platform evaluation questionnaire (SUS) (Brooke,

1996). In the end, the authors performed the

calculations for the SUS usability score (SUS_Valor

metric) of each participant.

5.3 Data Validation

For the experiment, it was considered a factor

(prototyping of the acquisition and conditioning

platform of biomedical signals), and a treatment

(prototyping by the participants with more and less

experience). Given this context, the average

prototyping time and the mean of SUS usability scale

(Brooke, 1996) were computed.

To aid the analysis, interpretation and validation,

four types of statistical tests were used: Kolmogorov-

Smirnov (K-S), Shapiro-Wilk (S-W), Student’s t-test

(for independent samples), and Levene. K-S and S-W

tests were used to verify the normality of the samples.

The Student’s t-test was used to compare the average

of two independent samples, and finally, Levene's test

was used to evaluate the homogeneity of variances.

All statistical tests were performed using the

SPSS - IBM (2013) tool.

6 RESULTS AND DISCUSSION

With twenty four samples obtained at the end of the

experiment, the SUS score properly calculated, and

the classified participants, the experiment resulted in

G1 "with experience" with twelve samples and G2

"No Experience" with twelve samples. According to

Lopes et al. (2013), a great number of statistical tests

assume that the data follow a normal distribution to

be applied. Therefore, the Kolmogorov-Smirnov (K-

S) and Shapiro-Wilk (S-W) tests were applied to

assess the normality of the sample through the

Statistical Package for Social Sciences (SPSS)

(2013).

The K-S and S-W tests provide the p-value, which

according to Lopes et al, (2013), can be interpreted as

the degree of agreement between the data and the null

hypothesis (H0) and H

being the hypothesis that the

distribution is normal. Figure 7 shows the result of

normality test conducted in SPSS with a confidence

level of 95% (significance level α = 0.05). Note that

the group with experience obtained a p-value (sig)

larger than the alpha in both K-S (0.171> 0.05) and

S-W (0.425> 0.05), which means that there is no

evidence to reject the H

. This also happens to those

without experience, sig is greater than the alpha in

both K-S (0.062> 0.05) and S-W (0.081> 0.05). So

the normal distribution is a possible distribution for

this sample set and thus the T-student statistical test

can be applied. This test evaluates the hypothesis that

two population means are identical, in which case,

0SUS

claims the G1 average is equal to G2 average.

Figure 10 shows the result of the T-student test in

the SPSS with a confidence level of 95% (α = 0.05).

Note that the value of sig on Levene's test is greater

than alpha (0.107> 0.05), which means that there is

homogeneity of variance, and in this case one uses the

0.954 sig which is greater than the alpha and so there

is no evidence to reject the H

0SUS

. Thus, there is not

enough significance for the group means to differ.

From this, it can be concluded that the average

usability from the "experienced" group does not differ

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning

from the "no experience" group, which answers the

research question QP1. Thus, there is evidence that a

person with no experience in the field makes the

prototyping with the same ease of a person with

experience, due to the platform's usability.

To answer the research question QP2, the overall

average of usability of the platform this paper was

compared to BITalino platform (Silva et al., 2014),

which is a framework that works by fitting sensors

blocks in the main control block and has an overall

average of 84.62 and rated A+ for usability by the

rating scale of Sauro and Lewis (2012). It is also

characterized, according to the authors, as a user-

friendly platform.

Regarding the platform of this experiment, figure

8 shows the average from the "experienced" group

and "inexperienced" group and from it we can

identify the average overall usability of the

experiment, which is 84.479. According to figure 9,

the correlation of the SUS score is performed with the

note from the scale built by Sauro and Lewis (2012)

and it shows that, from a scale F to A+, the system

note is A +. Comparing these values, it can be seen

that both YouMake and BITalino, have grade A+ and

a general average of 84.479 and 84.62 respectively.

Thus, to have similar values, we can infer that

YouMake can be used as a low cost alternative for the

acquisition and conditioning of biomedical signals,

answering QP2.

To answer QP3 focused on the prototyping time

for each group.

Figure 7: Sample normality test of SUS.

Figure 8: Groups’ averages of SUS.

Figure 9: Grading scale interpretation table for SUS score.

SOURCE: Adapted from table 8.6, page 204 from (Sauro

and Lewis, 2012).

Figure 11 shows the result of the normality test

conducted in SPSS with a confidence level of 95% (α

= 0.05) and with H

the hypothesis that the

distribution is normal. Note that the "experienced"

group got a higher sig than alpha in both K-S (0.200>

0.05) and S-W (0.597> 0.05), which means that there

is no evidence to reject H

. This fact also happens

with the "no experience" group, the sig is greater than

alpha in both K-S (0.200> 0.05) and S-W (0.110>

0.05). Then the Normal distribution is a possible

distribution for the set of samples and thus the t-

student statistical test can be applied.

Figure 12 shows the result of the t-student test in

the SPSS with a confidence level of 95%, with H

which states that the prototyping time of G1 is equal

to G2. Note that the value of sig on Levene's test is

greater than alpha (0.155> 0.05), which means that

there is homogeneity of variance, and in this case one

uses the sig 0.201 which is higher than alpha and,

therefore, there is no evidence to reject the H

0tempo

Thus, there is not enough significance for the groups

prototyping time to differ.

From this, it can be concluded that the prototyping

time from the "experienced" group does not differ

from the "no experience" group, which answers the

research question PQ3.

Figure 10: T-student test for independent sample of SUS.

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Figure 11: Normality test for the time variable of time.

Figure 12: T-student test for independent samples of time.

7 THREATS TO THE

EXPERIMENTAL STUDY

VA L I D AT I O N

7.1 Internal Threat

The internal threat defines if the relationship between

treatment and result is casual, without the influence

of other factors that may not have been measured.

Participants answered the usability scale without

supervision, so there is the possibility of them not

having understood well some of the issues and may

have marked wrongly, besides the scale subjectivity.

However, care was taken so that the participants

didn’t talk among themselves, mitigating the insider

threat.

7.2 External Threat

The external threats are the conditions that limit the

ability to generalize. The experiments were

performed in two different laboratories because the

samples have been collected at the IFS and at the UFS

and, therefore, in different environments. Moreover,

the oscilloscopes (equipment used for signal viewing)

used in each laboratory were different in brand and

model. Thus, there is the possibility of users

answering the questions differently. Although the

overall number of samples are sufficient according to

Tullis and Stetson (2004), a larger number of samples

could better represent the general population of

students interested in biomedical signals.

7.3 Construction Threat

The construction threats are related to the design and

human factors. Such threat can be characterized by

the participants time spent. Perhaps the time is not the

best metric, because some users spend more time just

viewing the signal than others that are more objective.

7.4 Conclusion Threat

The conclusion threats are related to the ability to

reach a correct conclusion about the relationship

between about the treatment and the outcome. To

avoid hypotheses infringement, we used the

normality test, Shapiro-Wilk, and a parametric test, t-

test, for data analysis. To reduce the confiability

impact to the implementation of the treatment, we

followed the same experimental setup in both cases.

8 CONCLUSION AND FUTURE

WORK

From the results obtained in the experiment, we can

see that the platform YouMake can be used as a low

cost alternative for the acquisition and conditioning

of biomedical signals, in addition to showing that a

person with no experience can use the platform with

equal ease, and same prototyping time a person with

experience in the field.

As future work, we intend to integrate the capture

platform with the interfacing and digitalization

An Experiment to Assess an Acquisition Platform and Biomedical Signal Conditioning

platform that is being developed in another research.

ACKNOWLEDGMENT

We thank the Sergipe Federal Institute and the

computer department of the Sergipe Federal

University for provide the laboratories for the

experiments.

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