A Low Cost IoT Enabled Device for the Monitoring, Recording and

Communication of Physiological Signals

Borja F. Villar

, Ana C. de la Rica

, Miguel M. Vargas

, Javier P. Turiel

and Juan Carlos Fraile M.

ITAP - Instituto de las Tecnologías Avanzadas de la Producción, University of Valladolid, Valladolid, Spain

University of Valladolid, School of Industrial Engineering, Valladolid, Spain

Keywords: IoT, Arduino, Data Acquisition, Biosensors, Cloud, ECG, GSR, Weareable Device.

Abstract: The physiological information obtained from patients during rehabilitation tasks with robot-assited platforms

is essential to carry out them properly. It has been shown that an environment adapted to the needs of each

patient favors their involvement and leads to a reduction in rehabilitation times. In order to be able to control

the degree of involvement of the subjects at all times and subsequently adapt certain parameters of the

rehabilitation task, physiological signals such as ECG, GSR (Galvanic Skin Response) or SKT (Skin

Temperature) are used. A low-cost device that integrates sensors for reading and recording the ECG and GSR

signals, which subsequently communicates via WiFi to a cloud-based environment is proposed in order to

carry out online data processing and dynamically adapt upper-limb rehabilitation tasks.

1 INTRODUCTION

In recent years, different biomedical devices have

been used to analyze certain physiological signals

used to control rehabilitation environments with

robotic platforms.

Activity trackers and other wearable electronic

devices have gained popularity due to users’ desire to

monitor, measure, and track using various real-time

features related to their fitness or health, including the

number of steps,heart rate, heart rate variability, body

temperature, activity and/or stress levels, etc.

(Conchel, 2018).

The importance of analyzing this type of

physiological signals in our study lies in the direct

relationship that exists between the stress level of a

subject and the HRV (Hate Rate Variability) (

Domen, 2011) and GSR (Galvanic Skin Response)

(Guerrero, 2013) measurements. Stress is a physical,

a mental, or an emotional factor that causes bodily or

mental tension. Stresses can be external

(environmental, psychological, or from social

situations) or internal (illness or caused by a medical

https://orcid.org/0000-0003-0090-3800

https://orcid.org/0000-0002-1556-7179

https://orcid.org/0000-0002-1952-0853

https://orcid.org/0000-0002-7731-2411

https://orcid.org/0000-0002-2877-7300

procedure). There are different methods to detect and

determine the stress level, being the most used:

measuring the cortisol level, the heart rate variability,

or the electrodermal activity (Wu, 2018). In other

way, the study of Kutt supports the use of Heart Rate

(HR) and Skin Conductance/Galvanic Skin Response

(GSR) signals to validate semantic emotional

descriptors based on valence and arousal

measurements, linked to the user’s involuntary

reactions transmitted by the Autonomic Nervous

System (ANS) (Kutt, 2018).

In our case, the heart rate (determined by the

electrocardiogram, ECG) and the electrodermal

activity (obtained from the galvanic skin response,

SR) have been chosen to determine the stress level of

a subject and their emotions during upper limb

rehabilitation tasks. An example of an application to

record the ECG and GSR in a wearable device can be

found in (Rosa, 2019) and (Crifaci, 2013) applied to

anorexia nervosa adolescents. The results of this

study determined that wearable sensors used were

feasible, unobtrusive and therefore extremely suitable

for young patients.

Villar, B., C. de la Rica, A., Vargas, M., Turiel, J. and M., J.

A Low Cost IoT Enabled Device for the Monitoring, Recording and Communication of Physiological Signals.

DOI: 10.5220/0010302901350143

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 1: BIODEVICES, pages 135-143

ISBN: 978-989-758-490-9

135

After a systematic review of the most popular

physiological signal recording devices, we

determined the following ones are the best choices

right now on the market:

• Consensys Bundle Development kit (Shimmer):

an 'all in one' solution which enables the user to

experience the full sensing capabilities of the

Shimmer3 platform which includes the complete

set of our Shimmer sensors (IMU, ECG, EMG,

GSR), hardware and software (Oreto, 2006).

• AutoSense: an unobtrusively wearable wireless

sensor system for continuous assessment of

personal exposures to addictive substances and

psychosocial stress as experienced by human

participants in their natural environments (Ertin,

2011).

• Ring: a mobile and robust bio-signal

measurement device for monitoring the skin

conductance (using an electrodermal sensor,

EDA and agalvanic skin response meter, GSR)

and the cardiovascular activity (using a blood

volume pulse sensor, BVP) (Mahmud, 2019).

• E4 wristband: a wristband medical-grade

wearable device for real-time physiological data

acquisition and visualization, enabling

researchers to conduct in-depth data analysis

(Mccarthy, 2016).

• Zephyr Performance Systems: measure six key

inputs that report on more than 20 biometrics. Is

a wearable technology built on clothes made for

sport challenges (Nazari, 2018).

• Checkme Lite: a monitor for measuring,

monitoring, reviewing and storing three

physiological parameters in the home: ECG,

pulse oxygen saturation (SpO2) and blood

pressure variation (Drzazga, 2018).

• BITalino: a hardware and software toolkit that

has been specifically designed to deal with the

requirements of body signals (Da Silva, 2014).

A more exhaustive comparison can be seen in

Sumit Majumder's review (Majumder, 2017).

Usually, the devices for recording these signals

are expensive and are not wearable, which makes it

difficult in many cases to carry out rehabilitation

tasks in a simple and comfortable way for both the

subject and the therapist. So, what makes a device fit

for the purpose of affective health research?

(Coghlan, 2009):

• Accuracy. The devices should be low-cost to be

accessible to almost anyone without

compromising the fidelity of the results.

• The device is expected to collect data

continuously without interfering with the user’s

day-to-day tasks. The platform must be mobile,

comfortable, robust in regards to prolonged

sensor contact, and have a sufficient battery-life

to last throughout the day.

• Connectivity to other devices e.g. through

Bluetooth or WiFi.

• Access to raw data so it can be processed post-

recording without losing any data from the

original signal.

• Easy to clean and disinfect between subjects.

This work focuses on the design and manufacture

of a low-cost device made with 3D printing, making

use of basic electronics based on an Arduino-like

microcontroller, the AD8232 sensor module for

measuring the physiological signal ECG and Grove

GSR v1.1 for measuring the GSR. The

microcontroller integrates a WiFi Soc solution

providing a reliable performance in the IoT, which is

used for communication with the Thinger.io platform.

A Nextion 3.2” resistive touch screen is used for the

configuration of initial parameters and online

visualization of the signal records. We have called

this device Trazein (registered trademark).

2 DEVICE SPECIFICATIONS

2.1 Design and Materials

When designing the device, the specifications shown

in Table 1 have been taken into account, complying

with the requirements cited in (Coghlan, 2009) and

expanding with what we have considered certain

innovative improvements that will be justified in the

following sections.

Table 1: Device Specifications.

It is clear that today one of the most widely used

rapid prototyping technologies is additive

manufacturing. The flexibility obtained when making

any model with 3D printing is much greater than

making molds for plastic injection (Cano, 2019). For

this reason, it is very important to take the design into

account in the preliminary phase, paying special

attention to both the benefits of this technology and

its drawbacks.

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The design of this biomedical device has been

made with Fusion360, an Autodesk distribution that

covers the entire process of planning, testing and

executing a 3D design, being able to export the final

model in a .stl file ready for laminate with a software

of 3D printing, in our case Cura. Finally the

impression was made on an Ultimaker 3s.

Figure 1: Exploded Device with Fusion360 CAD Software.

“Fit to wrist” configuration.

The design is seen in Figure 1, where the different

parts that make up the device are shown. The housing

(2) is printed on Medical Smartfil material, a high-

quality filament specially designed for medical

applications. This filament has a USP Class VI or ISO

10993-1 certification, which guarantees that it is

biocompatible with the human body (Ferrás, 2020),

(Reeve, 2017). This type of technology with these

materials has given good results previously, as shown

in the study by (Aguado-Maestro, 2019).

Figure 2: “Desktop” configuration as an alternative to “Fit

to wrist” configuration.

The device housing is completed by a lower cover

(1), also made of Medical SmartFil and with a

completely ergonomic design adapted to the anatomy

of most wrists. It has holes in its lateral projections to

be able to adjust the device to the user, thus acquiring

the quality of wearable. Inside it houses the

electronics components: a microcontroller (7), an

ECG signal reader (5), a GSR signal reader (6) and an

HMI (Human Machine Interface) consisting of a

touch screen (4). The electronics are separated by a

plate made in 3D printing (3) and all components are

properly screwed to the housing (2) or to the

separation plate (3).

2.2 Comparision to Other Devices

The devices that currently exist for reading

physiological signals, such as those that concern us in

this case, can be divided into two large blocks: those

that have the category of medical devices and those

that do not.

Starting with the latter, they are generally known

as wearables that can then be divided according to

their use into sports-oriented or everyday wearable.

These devices are characterized by a small and

ergonomic size, at the same time they have an internal

battery that usually lasts for several days between

each charge and finally the cost is not usually high.

On the contrary, these devices do not usually record

any physiological signals except, in some cases the

ECG, without offering the possibility of exporting the

data to be able to store or process them externally to

draw conclusions. Some examples are those shown in

Table 2.

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Continuing with those devices that fits the

category of medical devices, they offer a greater

range of signals to be read and recorded, with much

more precision and more processing capacity. On the

contrary, learning its use and operation is more

laborious, having to resort, at times, to the presence

of a specialist. They are not portable for the most part

and require an external power supply, in addition to

lacking Bluetooth or WiFi connectivity, so data

export has to be done through a cable to a computer.

The price is quite high as seen in Table 3.

Table 2: Most successful wearable devices on the market.

At Trazein, an ergonomic and portable design has

been prioritized, with a reduced weight of 80 grams

and dimensions of 110x80x35 mm. There are two

types of configurations, either with a strap to place as

a smartwach on the wrist, such as a blood pressure

monitor, or with a rigid cover to place on a table or a

horizontal surface. It has a switch on one of the sides

to interleave between the two phsyiological signals

(ECG and GSR) to be recorded, which are connected

to the same analog input port of the microcontroller.

On the other side, there are three connectors, two for

each of the sensors and another for a micro-USB

cable that is used to connect an external power

supply.

Table 3: Most successful medical devices on the market.

It has a touch screen that acts as an HMI, which is

located below the level of the casing in its upper part,

to avoid breakage or scratches in case of falls or

bumps during transport. We consider that having a

touch screen such as HMI is an added value for the

subject when it comes to being able to view their

physiological signals in real time, configure their

record history or connect to the WiFi network without

having to have a computer.

2.3 Manufacturing Specifications

Within the field of additive manufacturing, or

manufacturing using 3D printing, there are different

types of technologies (SLA, SLA and FDM). It is

important to choose the appropriate technology based

on the compromise that must exist between the cost

of the device, the final properties of the product part

and the manufacturing time. The choice of this

manufacturing mode has been selected for several

reasons (Ngo, 2018): low cost of the prototype,

flexible and personalized manufacturing when

obtaining a model designed using a CAD program,

ease of manufacture and material savings.

Table 4: Parameters for 3D printing manufacturing.

For all this, our device has been manufactured

using FDM technology using PLA as a construction

material. As it is a device for use with people, this

material is the one that best adapts, offering excellent

properties at a low price compared to other traditional

biodegradable polymers used in medical applications.

Besides being environmentally friendly it is not toxic

for use in contact with the human body (Lasprilla,

2012). Manufacturing conditions are also taken into

account when selecting the material. By raising the

temperature to get the material to be in a fluid state

and to be extruded through the nozzle built into the

print head, it causes certain volatile particles to be

emitted into the environment. Specifically, with the

material used in this project, studies have been carried

out on the harmfulness of these gases emitted on

human health. Determining that finally that these

volatile compounds emitted into the environment

during the printing of a 3D model do not pose any

health problem (Azimi, 2016), (Riya, 2019).

In this way, we obtain a low-cost [50 eur] device

with a reduced manufacturing time [6 hours] and

technical specifications that make it very convenient

to be able to house the necessary electronics and be

used in any rehabilitation environment. Thanks to its

size and weight, it is a portable device, and due to its

simplicity, it can be used without the need for prior

knowledge. The design made specifically for 3D

printing makes it easy to manufacture in any type of

printer, optimizing the amount of material used by not

requiring hardly any support material.

The parameters used in the Ultimaker Cura

v.4.7.1 lamination software that we consider to be

optimal to obtain a high-performance device and also

taking into account the conditions recommended by

the manufacturer are those shown in Table 4.

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2.4 Electronic Specifications

When selecting the electronics that the device should

include to meet the specifications, we chose the

following: AD8232 module for reading the ECG

signal, Grove GSR v1.1 module for reading the GSR

signal, a WEMOS D1 MINI development board

based on ESP8266EX microcontroller and finally an

HMI composed of a Nextion NX3224T024. The

scheme is shown in Figure 3.

• ECG Module. AD8232 Heart Rate Monitor

(Sparkfun

) module is used for measuring the

ECG and determinate the heart rate of the user. The

ECG module is based on the AD8232 (Analog

Devices), an integrated circuit with specially

calibrated signal amplifiers and noise filters for

ECG signals. The module suppresses the 60Hz

noise generated by household electricity. The

module provides an analog output so an analog-to-

digital conversion must be performed to process and

display the ECG on the HMI. Examples of

investigations that have used this module are

(Gifari, 2015), (Lu, 2014), (Mishra, 2018) obtaining

promising results:

Table 5: ECG bandwith specification.

According to (Rachit, 2020), ECG bandwidth

specification is dependent with its application and it

is presented in table 5. Table 6 shows a comparison

of different chips for ECG measurement. AD8232

have been chosen over other chips for the following

reasons: HM301D is three channels, while we only

need a single channel ECG for QRS detection;

ADS1191 doesn’t provide high enough gain to get

good resolution; AD8232 has the best output

impedance and gain.

Table 6: Comparison between most used ECG microchips.

• GSR Module. The Grove – GSR sensor v1.1

module (Seeed) is used for measuring the electrical

conductance of the skin to determinate the GSR.

Grove – GSR sensor allows to spot emotions by

simple attaching two electrodes to two fingers on

one hand. These emotions can cause stimulus to the

sympathetic nervous system, resulting more sweat

being secreted by the sweat glands. Grove – GSR

has been used in emotion related projects, as shown

in (Anzanpour, 2015), (Zhang, 2019) and (Saputra,

2017).

• WEMOS D1 MINI Development Board. It is

based on the ESP8266EX, which is a low-cost WiFi

microchip, with a full TCP/IP stack and

microcontroller capability, produced by Espressif

Systems. It is commonly used for a wide variety of

projects, among others those related to

communication in home healthcare environments,

as shown in (Rachit, 2020) and (Mesquita, 2018).

To tackle all the specification we design the hardware

using a step-by step method. The design starts with

level 0, continued by level 1, and so on until every

level can be implemented using specific hardware.

Figure 3: Electronic scheme for the Trazein device.

2.5 Connectivity and IoT

One way to introduce the project into the IoT

environment is by incorporating a data collection and

storage platform. This application is Thinger.io,

presenting itself as open source and free of charge.

The establishment of the connection between the

device and the platform is through WiFi. Thinger.io

is an open source software, and it offers an Arduino

client library for connecting almost any Arduino

board and other supported boards like ESP8266 for a

simpler integration. It is possible to store time series

data, identity and access management, even access by

third-party applications, one of the advantages being

A Low Cost IoT Enabled Device for the Monitoring, Recording and Communication of Physiological Signals

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the possibility of two-way communication between

the elements in real time, with the REST-API method.

The communication between the platform and the

device does not imply a high consumption of energy,

moreover, it is used efficiently.

All this is presented in a web interface where the

user can manage all the resources, and even the

possibility of making use of a mobile application

where can be viewed the data collected in a simple

and immediate way (Bustamante, 2019).

The maximum number of devices that can be

connected to for free is 2, with 4 dashboards and 4

endpoints. Writing the collected data at least every 60

seconds in the storage bucket while the endpoint is

every 10 seconds, storing a total of up to 10 value

fields. All stored data can be exported in two very

common formats for further analysis: text and comma

separated values files (.txt and .csv).

Figure 4: Functional Block of ECG/GSR Trazein device.

3 IMPLEMENTATION, TEST

AND ANALYSIS

3.1 Manufacturing Results

The manufactured device looks like the one shown in

Figure 5, with the ECG and GSR sensors connected

as has been done in the patient trials.

The device has a highly ergonomic finish, which

adapts easily to the wrist of the subjects regardless of

their anatomy. The upper part has been coated with

epoxy resin, to preserve the material after different

uses. At the same time, the epoxy resin gives it greater

resistance to impacts or accidental falls. The part that

comes into contact with the skin has not received this

treatment, to maintain the conditions described in the

USP Class VI or ISO 10993-1 regulations.

Figure 5: Final 3D printed manufactured device.

Subject 1: Age 22, Rest, 50 seconds.

Subject 2: Age 57, Rest, 65 seconds.

Subject 3: Age 59, Rest, 22 seconds.

Subject 4: Age 82, Rest, 22 seconds.

Figure 6: ECG signal sample acquisition and QRS detection

for different subjects.

3.2 Experimental Protocol

When performing the proof of concept of the device,

we have carried out an experimental study with 4

healthy subjects. The subjects ranged in age from 22

to 87 years, 2 males and 2 females. The tests were

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carried out under laboratory conditions, at an ambient

temperature of 25ºC, in the FabLab facilities of the

University of Valladolid. These tests consisted in

measuring the heart rate and the galvanic response of

the skin, in a resting situation in order to validate the

correct functioning of the device in reading, sending

and storing physiological signals, observing the

different values obtained and comparing them with

similar studies (Mochan, 2011), (Villarejo, 2012).

3.3 Test and Analisys

The results obtained from the recording of the ECG

are those shown in Figure 6, while in Figure 7 the

device can be seen in full operation with one of the

subjects, showing the instantaneous recording of the

ECG signal and the beats per minute (BPM) at rest.

The implementation of the Pam-Tompkins

algorithm (Pan J., 1985), commonly used to detect

QRS complexes in electrocardiographic signals, was

carried out. In this way, we are able to obtain the BPM

shown on the screen. It should be mentioned that the

signal shown on the screen has not yet applied the

algorithm and that its processing is carried out online

on the ESP8266.

The results obtained from reading and recording

the GSR are shown in Figure 8, where the horizontal

axis is in units of time (seconds) and the vertical axis

in units of resistance (ohms).

Figure 7: Trazein HMI showing ECG signal and BPM real-

time acquisition with one subject.

Subject 1: Age 22, Rest, 373 seconds.

Subject 2: Age 57, Rest, 373 seconds

Subject 3: Age 59, Rest, 22 seconds.

Subject 4: Age 82, Rest, 22 seconds.

Figure 8: GSR signal sample acquisition for different

subjects.

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Figure 9: Trazein HMI showing GSR signal real-time

acquisition with Subject 2.

4 CONCLUSIONS AND FUTURE

WORKS

In this paper we have shown a new device for the

acquisition, recording and sending of the ECG and

GSR physiological signals. The novelty lies both in

the design and manufacturing process, as well as in

the concept of working in real time with cloud

storage. The first refers to an ergonomic design,

wearable type that adjusts to the wrist of any subject.

Also, the configuration can be changed to be a

desktop device. On the other hand, it is manufactured

in 3D printing, in a time of 6 hours and with

biomedical material that ensures safe contact between

the device and the human body.

The second refers to a touch HMI that displays the

physiological signals and displays the key value on

the screen. While recording, it sends data packets to

the cloud that are stored in the form of tokens, to be

downloaded later in .csv or .txt format for later

medical analysis. The implementation of the Pam-

Tompkins algorithm in the ESP8266 microcontroller

allows to process the acquired signal in real time and

detect the QRS complex for the subsequent

acquisition of the beats per minute.

The results obtained with the four subjects show

the correct functioning of the device, being still

necessary a validation against a commercial medical

device to contrast results under the same conditions.

Future lines of work go through experimentation with

healthy subjects, being monitored at the same time by

Trazein and Biopac MP150, in order to determine the

precision of a low-cost device compared to a

commercial one.

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