BITtalino: A Biosignal Acquisition System based on the Arduino

Ana Priscila Alves

, Hugo Silva

, Andr

e Lourenc¸o

1,2

and Ana Fred

Instituto de Telecomunicac¸

oes, Instituto Superior T

ecnico, Avenida Rovisco Pais, 1, 1049-001 Lisboa, Portugal

Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Em

ıdio Navarro, 1, 1959-007 Lisboa, Portugal

Keywords:

Biomedical Instrumentation, Arduino, Signal Acquisition, ECG, Biometrics.

Abstract:

Our work presents a low-cost biosignal acquisition system, BITalino, based on the Arduino hardware platform;

both the hardware and software components are detailed, together with experimental evaluation. This system

was designed to be integrated in a biometric platform based on Electrocardiographic (ECG) signals, that will

be used for identity recognition. The experimental evaluation revealed that this system is not only capable

of ECG signal acquisition, for biometric purposes, but it can also be used as a generic platform for other

biomedical applications, greatly extending its applicability. In this paper we describe the proposed platform,

with special emphasis on the design principles and functionality. Future work will focus on further developing

our hardware, targeting its integration in a prototype system for ECG-based biometric recognition.

1 INTRODUCTION

Biosignal acquisition has been a topic of increas-

ingly growing development, since it constitutes the

basis for diagnostic systems, and contributes to a bet-

ter understanding of the body functions. Nowadays,

novel applications of biosignals are emerging in areas

where they are not traditionally found, such as the use

of Electrocardiographic (ECG) signals for biometric

purposes (Biel et al., 2001) (Lourenc¸o et al., 2011).

Therefore, the main objective of our work was to de-

velop a low-cost acquisition system, capable of cap-

turing vital signs, using the ECG as a testbed.

There are multiple hardware choices available,

however the Arduino is currently the most ﬂexible and

easy-to-use hardware and embedded software plat-

form, with low cost, easy communication, and soft-

ware running on a computer or other devices. Our

goal was to integrate the Arduino board with the An-

droid Operating System, targeting the development of

a mobile biometric system; as such, the chosen com-

munication protocol was Bluetooth, since it is avail-

able in almost every mobile device, and also in com-

puters. The integration between Arduino and Android

is not new (Google ADK, 2011), but very few initia-

tives are based on Bluetooth.

The ﬁrst use of our acquisition system targets the

integration in a biometric platform, allowing real-time

recognition. However, it can be extended to numerous

other important applications, depending on which sig-

nals are to be acquired.

Nowadays, the use of the Arduino in medical and

health applications has been widely explored for sim-

ple usage scenarios. One particular example is avail-

able at (Medicarduino, 2012), where the Arduino is

used to acquire EEG signals, measure pulse, detect

alcohol levels, and many other examples. Therefore,

this project will contribute to the creation of a new

system, modular, multi-purpose, easily accessible and

with the possibility to be assembled by anyone inter-

ested on biosignal acquisition.

In the following sections we describe the acquisi-

tion system, designed especially for ECG data collec-

tion. The document is organized as follows: Section 2

focuses on the global system architecture, and in Sec-

tion 3, 4 and 5 the hardware, ﬁrmware and software

are detailed; Section 6 presents experimental results

obtained with this system. Finally, Section 7 outlines

the main conclusions and future work.

2 SYSTEM ARCHITECTURE

The proposed acquisition system has two main parts:

Hardware and Software. In this particular work, the

hardware is composed by the Arduino platform, Blue-

tooth transceiver and ECG sensor.

In the case of ECG acquisition, the electrical activ-

ity of the heart is captured using electrodes placed on

two ﬁngers of opposed hands. Those signals are ac-

261

Alves A., Silva H., Lourenço A. and Fred A..

BITtalino: A Biosignal Acquisition System based on the Arduino.

DOI: 10.5220/0004243502610264

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2013), pages 261-264

ISBN: 978-989-8565-34-1

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Diagram of the Android Biometric Platform.

quired through the analog input ports on the Arduino

board, and subsequently converted using the internal

analog-to-digital converter. Then, the digitalized data

is sent via Bluetooth to the base station (e.g. Android

mobile phone). The diagram represented in Figure 1

synthesizes the overall architecture of the hardware

subsystem, showing a schematic of the main compo-

nents.

Regarding the software, there are two main pro-

grams developed: the Arduino Firmware, which con-

trols its operation, and an Application Programming

Interface (API) in Java, which communicates with the

Arduino, controls the acquisition process, allows the

access to the collected raw data and enables high-level

applications to access both the device and the data.

These two parts of the system will be detailed in the

next sections.

3 HARDWARE

The main component of this section is the Arduino.

Figure 2 shows the ﬁnal prototype, with main com-

ponents: one Arduino Pro Mini (3.3V and 8MHz),

directly connected to a Bluetooth Mate module; and

one Lithium Ion Polymer battery with nominal volt-

age of 3.7V at 400mAh, as power supply. The ECG

sensors are connected to one of the BITalino analog

input pins, and its speciﬁcations are detailed in (Silva

et al., 2011).

Figure 2: BITalino prototype.

4 FIRMWARE

The ﬁrmware development performed in our work

was designed to deﬁne the behavior of the Arduino

microcontroller, setting its parameters, such as sam-

pling rate, baud rate and communication protocol. In

this section we will describe the major objectives,

problems encountered, and solutions achieved.

The main purpose of the ﬁrmware is to control

the analog and digital acquisition, using a pre-deﬁned

sampling rate; all the data acquired is sent to another

device via Bluetooth or USB connection. The open-

source Arduino environment makes it easy to write

code and upload it to the I/O board, which is one of

the main reasons why this platform was chosen as the

base for our system. The global operation is repre-

sented in Figures 3 and 4.

Figure 3: State diagram of the ﬁrmware operation.

Figure 4: State diagram of the ISR operation.

There are three modes in which the Arduino can

be operating:

a) Live: In this mode, the system continuously sam-

ples the physical analog and digital channels,

packs all the data, and sends it through the UART.

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All these tasks should be concluded within a time

frame, which is imposed by the sampling rate.

b) Simulated: Although it is similar to what happens

in acquisition mode, in this mode, the system will

simulate the acquisition, transmitting synthesized

signals. These correspond to sinusoidal, square

and sawtooth waves. This way, the communica-

tion and interaction between the base station and

the device can be tested.

c) Idle: The system disables any mode in which it is

in, and stays in stand-by until it receives a com-

mand to start the Live or Simulated modes.

When in Live mode, 2 analog and 4 digital in-

put pins are read, and their data saved in an array.

In order to make the most efﬁcient of the available

bandwidth on the communication channel, those val-

ues are packed into 4 bytes, including also a sequence

number, and a 4-bit Cyclic Redundancy Check (CRC)

value to detect possible errors in the message. This

packing process is done using bitwise operators, and

its schematic can be seen in Figure 5.

(a) Unpacked. (b) Packed.

Figure 5: Data packets schematic.

After concluding this step, all data is sent to the

computer through the UART.

One important requirement of this system is the

accuracy in the sampling rate. In the live and simu-

lated mode, all the tasks are completed inside a func-

tion called at a speciﬁed frequency. The approach

used was based on timer interrupts.

An interrupt is an external event that stops the run-

ning program to execute a special interrupt service

routine (ISR). After completing the ISR, the running

program is resumed to execute the next instruction.

These interruptions can occur at a speciﬁc frequency,

when associated with timers.

The Arduino Pro Mini has a clock speed of 16

MHz and 3 timers: Timer 0 (8 bits), Timer 1 (16 bits)

and Timer 2 (16 bits).It can be conﬁgured in 16 oper-

ation modes. In this work, we used the Clear Timer

on Compare match (CTC) mode, based on compari-

son, that is, the timer compares a counter value with

an output compare register deﬁned by the user, and

every time they match, an interruption occurs and the

ISR function is called.

5 SOFTWARE

An Application Programming Interface (API) was de-

veloped in Java in order to control the Arduino. Its

main purpose is to establish Bluetooth connection,

and then start or stop the acquisition, receiving the

acquired samples, and conﬁguring the device.

An abstract class Device was created, which was

subdivided into 2 main subclasses: Bluetooth and

Test.

a) Bluetooth: This subclass establishes a Bluetoothl

connection with the Arduino, and control its oper-

ation sending commands that activate start or stop

methods. When start is activated, all data received

from the device is saved in a text ﬁle for further

processing.

b) Test: The Test subclass does not communicate

with the device, but is used to test the API func-

tionality.

The conﬁguration parameters used in these sub-

classes, such as baud and sampling rates, are speci-

ﬁed in a document with a standard notation based on

JSON (JavaScript Object Notation). It creates an easy,

standard, Human-readable and structured way to rep-

resent diverse information, and works regardless of

the adopted programming language. An example of

setup parameters deﬁned using the JSON notation is

as follows:

{ "BaudRate":115200,"Mode":"Live",

"Sampling Rate":1000}

Focusing on the particular operations that can be

performed using the Bluetooth subclass, the main

methods are:

1. Setup: A JSON Object containing information

about Sampling Rate, Acquisition mode (Live or

Simulated), and Baud Rate is parsed and, with

this information, a Bluetooth connection is estab-

lished.

2. Start: The mode number corresponding to start

acquisition, Live or Simulated, is sent to the sys-

tem, which will activate its acquisition state.

3. Acquire: After activating the acquisition state,

this method reads the incoming data, and saves

each sample in a text ﬁle, using the test applica-

tion described in the next subsection.

4. Stop: When this method is called, the acquisition

state is stopped, and the system returns to the idle

state.

To test the API functionality and benchmark the de-

vice, a test application was developed; it creates an

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applet with Start and Stop buttons, and calls the corre-

sponding methods. Thus, when the application starts,

the Bluetooth communication is established, and it re-

mains waiting for a button to be pressed, executing the

method Start and Acquire when the button “start”

is pressed, and calling Stop method when the button

“stop” is pressed.

6 EXPERIMENTAL EVALUATION

Tests were performed to the ﬁnal system, to check

the sampling rate and the quality of ECG signals ac-

quired. Therefore, to verify if the Arduino was ac-

quiring at the speciﬁed sampling rate of 1000 Hz, a

synthesized square wave with a frequency of 10KHz,

duty cycle of 50%, 4 V

and offset of V

/2 was

acquired, and the data was analysed using Matlab.

The signals were generated using an Agilent 33220A

function generator. The synthesized wave revealed

a square wave, as expected, but after measuring the

number of samples in each pulse we veriﬁed an av-

erage loss of 5 samples per second. However, since

our main purpose is to acquire ECG signals, and its

bandwidth is approximately 100Hz, a much lower

sampling rate of 200Hz can be used for data acqui-

sition, leading to a maximum loss of 1 sample per

second, which is negligible for biometric recognition

purposes.

(a) Raw Waveform. (b) Filtered Waveform.

Figure 6: ECG signal acquisition.

This behaviour was already expected due to the

Arduino’s clock accuracy error of 0.2%. However,

there are some possible solutions to overcome this

problem, which will be a part of future work, and de-

scribed in section 7.

In what concerns the ECG signals, the acquisition

was performed using 2 ﬁnger electrodes on opposed

hands, with a sampling rate of 1KHz. In Figure 6

the raw 6(a) and ﬁltered 6(b) signals are represented.

The ﬁltering was done with a low-pass kaiser ﬁlter be-

tween 2.5 and 30 Hz. Therefore, it was concluded that

this system is able to acquire these signals with qual-

ity, being possible to distinguish the different com-

plexes of a characteristic ECG signal, when ﬁltered.

Moreover, the battery lifetime was tested, and it oper-

ates, on average, 6 hours in constant acquisition.

7 CONCLUSIONS AND FUTURE

WORK

We have designed and implemented a ﬁrst prototype

of an ECG acquisition system, applicable to biomet-

ric applications. Experimental results have shown that

the data collected through the proposed system, pre-

serves the waveform properties that are used by the

ECG-biometric systems. Although this system was

designed to integrate a biometric platform, it can also

be used to acquire other types of biosignals, becoming

a more generic acquisition system.

Future work will be focused on the integration of

an external oscillator in the system, separating the ex-

ecution lines for data acquisition and the data trans-

mission, and increasing the resolution of the system,

through an external ADC. However, in it’s current

state, this prototype system is already prepared for de-

ployment in real-world test beds, and is an adequate

low-cost alternative for large-scale data acquisition.

ACKNOWLEDGEMENTS

This work was funded by the Fundac¸

ao para a

encia e Tecnologia (FCT) under grants PTDC/EIA-

CCO/103230/2008, SFRH/BD/65248/2009 and

SFRH /PROTEC/49512/2009 whose support the

authors gratefully acknowledge. The authors would

also like to thank the Institute for Systems and

Technologies of Information, Control and Communi-

cation (INSTICC), the graphic designer Andr

e Lista,

Prof. Pedro Oliveira, and the Instituto Superior de

Educac¸

ao e Ci

encias (ISEC), for their support to this

work.

REFERENCES

Biel, L., Petterson, O., Phillipson, L., and Wide, P. (2001).

ECG analysis: A new approach in human identiﬁca-

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Google ADK (2011). Accessory development kit.

http://developer.android.com/tools/adk/adk2.html.

Lourenc¸o, A., Silva, H., and Fred, A. (2011). Unveiling

the biometric potential of Finger-Based ECG signals.

Computational Intelligence and Neuroscience.

Medicarduino (2012). Medical and health related projects

with arduino. http://medicarduino.net/.

Silva, H., Lourenco, A., Lourenco, R., Leite, P., Coutinho,

D., and Fred, A. (2011). Study and evaluation of a sin-

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