Heart Monitoring System based on NFC for Continuous

Analysis and Pre-processing of Wireless Vital Signs

Antonio J. Jara, Pablo López Martínez, David Fernández Ros, Benito Úbeda,

Miguel A. Zamora and Antonio F. G. Skarmeta

Department of Information and Communication, Faculty of Computer Sciences

University of Murcia, Murcia, Spain

Abstract. Continuous and wireless transmission of vital signs is taking a high

relevance in ubiquitous computing, ambient intelligence and Ambient Assisted

Living (AAL). Integration of wireless communications technologies and em-

bedded systems into health monitoring systems are tending towards solutions

defined under the denominated Internet of Things (IoT). Specifically, IoT is

based on technologies such as Radio Frequency Identification (RFID) to pro-

vide the capabilities for identification of devices/sensors, and the evolutions of

RFID with Near Field Communication (NFC). NFC presents machine to ma-

chine (M2M) communication capabilities between sensors and personal devic-

es. Thus, this allows to carry out the communication with just approaching the

reader to the devices, i.e. contactless. This offers advantages mentioned in terms

of easy use for elderly people in AAL environments, in addition to the men-

tioned ubiquity. For that reason, it is highly interesting for the development of

AAL solutions, but this also presents challenges for the performance and effi-

cient data transmission because the constrained resources and capabilities from

the devices, and the latency introduced by the NFC technology with the refresh

readers to exchange NDEF records. These challenges appear since it was origi-

nally considered for identification, and not for continuous data transmission.

This paper discusses the feasibility of developing a monitoring system for con-

tinuous data transmission from an electrocardiogram (ECG). ECG has been

considered among the available clinical sensors because its complexity. This

presents an example of NFC communication via a USB NFC reader and an An-

droid OS Smart Phone with NFC support. Over that scenario are analyzed the

problems found with the original data from the ECG, and consequently it is

prepossessed a pre-processing technique for the ECG wave trace. This pre-

processing analyzes the signal in order to detect possible arrhythmias and main-

ly to reduces transmission overload (compression) in order to make suitable the

transmission of continuous data through NFC.

1 Introduction

New generation of technologies based on Internet of Things (IoT) provides a mean

through which obtain a larger amount of data with higher accuracy and context

awareness. This enrichment of the information allows the development of more so-

phisticated monitoring solutions and systems. Our work is focused on the integration

J. Jara A., López Martínez P., Fernández Ros D., Úbeda B., Zamora M. and F. G. Skarmeta A..

Heart Monitoring System based on NFC for Continuous Analysis and Pre-processing of Wireless Vital Signs.

DOI: 10.5220/0003886500890099

In Proceedings of the 2nd International Living Usability Lab Workshop on AAL Latest Solutions, Trends and Applications (AAL-2012), pages 89-99

ISBN: 978-989-8425-93-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

of continuous data communications, in different contexts which covers from hydro-

logical monitoring solutions [1] to assisted living environments. Particularly, this

work is focused on the evaluations of NFC capabilities for the transmission of contin-

uous vital signs from an ECG. This has been chosen, since ECG presents high com-

munications requirements and challenges.

Flexibility in conjunction with ubiquity are the key requirements for data acquisi-

tion and monitoring solutions, such as the located at the clinical and Ambient Assisted

Living (AAL) environments. This flexibility and ubiquity is what we can find in the

Future Internet capabilities, and that is why those requirements are satisfied with the

new capabilities to link sensors, devices and exploiting captured data that presents the

so-called Internet of Things [2].

The evolution of consumer devices with high-capacity such as personal devices,

smart phones and the evolution of wireless communications interfaces such Bluetooth

2.1, the new Bluetooth Low Energy (4.0), 6LoWPAN and NFC, make possible to

extend the Internet to small sensors and devices, in order to identify and connect all

the things, people and systems located around us.

Internet of Things is therefore considered one of the greatest advances in commu-

nications in recent years, since it provides the foundation for the development of au-

tonomous applications and services that enable make a more scalable operation and

maintenance. Currently, there are related jobs in areas such as home automation [3],

intelligent transportation systems [4] and personalized healthcare [5].

Our research work is focused on analyze the different IoT technologies available,

which are suitable for the integration of sensors and clinical devices. After, of the

already integration found for Bluetooth 2.1 technology through the Health Device

Profile (HDP) within the framework of the Continua Alliance, the previous study

carried out for 6LoWPAN technology [6]. The present work is focused on analyze the

capabilities from NFC. Finally, future work focused on Bluetooth Low Energy (4.0).

Specifically, this presents an analysis of the communication capabilities offered by

NFC, with the communication protocol defined over NFC Data Exchange Format

(NDEF) and the NDEF Push Protocol (NPP) to transmit data continuously from an

RFID/NFC reader connected via USB (ACR 122 from ACS [7]) to a smart phone

with NFC supports (Google Nexus S from Samsung). Fig. 3. presents both terminals

running with the ECG application module.

The rest of the paper is distributed in the following way. Section 2 describes the

capabilities for real-time communication from NFC technology. Section 3 presents

the requirements from the continuous data transmission from the electrocardiogram.

Section 4 and 5 present the pre-processing of the gathered data, in order to analyze the

possible arrhythmias, compress the wave trace, and generate the NDEF messages.

Section 6 presents the heart monitoring system developed. Finally, Section 7 analyzes

the suitability of the solution and concludes the paper.

Table 1. Records in an NDEF message.

NDEF Message

MB=1 … R

… R

ME=1

2 NFC Capabilities for Continuous Communications

In this section is carried out a short review about the main technical properties and

considerations about NFC, NDEF records and NPP protocol. For an extended version

of NFC, NDEF and NPP, it is recommend the whitepapers from NFC Forum [8].

NFC is a contactless or proximity communication medium, which is based on

magnetic induction. This works on the 13,56Mhz frequency. The theoretical distance

of standard antennas (embedded in cards, tags or readers) is around 10 cm, with a

practical working distance of 4 cm. The bandwidth/speed for data transmission is 106,

212 or 424 Kbits/s depending on the mode of transmission and hardware capabilities.

The communication in a NFC System is composed of two elements:

• Initiator: This starts the commutation and controls/manages the data exchange.

An example of initiator is a reader.

• Target: The device that respond to the requirements of the initiator. An example

of target is a card or a tag.

NFC devices can operate in two modes, passive and active.

• Passive Mode: In this mode, one device generates an electromagnetic field (read-

er), while another device modulates this field for data transmission (tag). It is founded

on the conventional RFID technology. NFC technology allows to emulate a HF card

in a smart phone, in order to act as a passive RFID HF card. NFC can also acts as a

reader for HF RFID tags.

• Active Mode: In this mode, both devices generate a magnetic field and modulate

the opposite magnetic field. This mode supports machine to machine (M2M) commu-

nication. NFC operates in this mode to talk between a reader and a smart phone, or

between two smart phones.

2.1 NDEF Messages and NDEF Records

NDEF is a lightweight binary message designed to encapsulate one or more payloads

in a single message. NDEF messages can be nested, and are composed by NDEF

records. See Table 1 with the format and composition for a NDEF message.

The minimal NDEF message is a unique NDEF record with MB and ME flags set

to 1, but a NDEF message can contains various NDEF records. MB and ME mark the

start and end of a NDEF message. Table II shows the NDEF record format.

An NDEF record is not numbered, the application is responsible to respect the or-

der of the records. NDEF records can be chained in order to support longer payloads

(fragmentation), Chunk Flag (CF) marks the fragmented payload, SR the payload

length which is between 0 and 256 bytes, and ID length (IL) indicates that the

ID_LENGHT field is present in the NDEF record header with one byte.

NDEF record has three parameters describing the payload. Naming, TNF (Type

Name Format) provides a context for the payload, Type length, Type to describe the

type of payload. The value of the TYPE field must follow the structure and format

encoding implicit in the TNF field value. ID field value is an identifier of URI form

(RFC 3986). The referenced URI can be relative or absolute. The intermediate and

final segments must not have ID field. Finally, it is defined the payload.

Table 2. NDEF Record format.

MB=1 ME=1 CF=0 SR=1 IL=0 TNF=001=0x01 (NFC Forum well-known types)

TYPE LENGHT = 0x01

PAYLOAD LENGHT

TYPE = ‘T’ (Text/plain)

PAYLOAD (see Table III for the presented use case)

2.3 NDEF Push Protocol (NPP)

The communication between the ACS ACR 122 USB reader and the smart phone is

based on NPP protocol. NPP is a protocol built on top of Logical Link Control Proto-

col (LLCP) [8]. It is designed to push NDEF messages from one device to another.

NPP itself offers a simple one way communication, pushing NDEF messages from

a client to a server. A device that supports NPP always run an NPP server (listening),

and may also run the NPP client procedure when it has an NDEF message available to

push. Thereby, this allows bi-directional NDEF exchange between NFC devices.

Although, the NDEF record can be until 255 bytes. It has been found a limitation

with NPP, where it only can be sent 128 bytes of payload length. Therefore, in case

that it is required to send more than 128 bytes, it is required more than one NDEF

message, which means that it needs to reconnect using Connect APDU from APDU

commands [9]. Therefore, such as it is presented in the following sections, in order to

send wave traces from an ECG, it is required more than 127 bytes per heartbeat (see

Table III). This introduces a high latency and consequently a cumulative delay.

Table 3. Wave trace from a wearable electrocardiogram. This frame has a total of 257 bytes to

define the wave of a single heartbeat.

(

WAVEMARKER

)

: ECG wave trace -> 78 77 77 77 78 79 79 79 7A 7A 7A 7A 7B 7B 7B

7B 7C 7C 7C 7D 7D 7D 7E 7E 7F 7F 7F 7F 80 80 80 80 81 82 82 83 84 85 85 86 86 87

88 89 89 8A 8B 8C 8D 8D 8E 8F 8F 8F 90 90 90 90 8F 8F 8E 8D 8C 8B 8A 88 87 86 85

83 82 81 80 7F 7E 7D 7C 7C 7B 7B 7B 7B 7A 7A 7A 7A 7A 7A 7A 7A 7A 7A 7A 7A 7A 7B

7B 7B 7B 7B 7B 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C 7C

7C 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7B 7C 7C 7C 7C 7C 7C 7C 7C 7C

7C 7B 7B 7B 7B 7B 7C 7C 7C 7C 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D

7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7E 7E 7E 7E 7D 7D 7D 7D 7D 7D 7D 7D

7D 7D 7D 7D 7D 7D 7D 7D 7D 7D 7E 7E 7E 7E 7E 7E 7E 7E 7E 7E 7E 7E 7F 7F 7F 7F 7F

7F 7F 7F 80 80 81 82 82 83 84 85 85 85 85 85 85 85 85 85 85 84 83 82 81 81 80 7F

7F 7F 7E 7E 7E 7E 7E 7E 7E 7E 7D 7D 7D 7D 7C 7B 7A 7A 7B 7D 82 88 91 9C A7 B1 B8

BB BA B4 AB A0 94 8A 82 7C FA (BPM) 44 -> 68 beats per minute

3 Native Model Sensor Communication

Clinical sensors present native protocols for communications, which provide from

RAW data to sensors which formatted format following a standard such as Health

Level 7 (HL7) and IEEE 1073 (X73). All of them require to be pre-procedssed from

their original protocol to NDEF records, in order to make feasible the data transmis-

sion via NFC. This pre-processing and adaptation tasks allow to carry out complex

data analysis for anomalies detection, data compression and security techniques appli-

cations. For example, the inclusion of Cyclic Redundancy Codes (CRC) for integrity,

digital summary and signatures for authentication, and encryption for protection [6].

The sensor considered is an electrocardiogram (ECG). Specifically, the ECG

module chosen is the EG 01000 from Medlab (see Fig. 2). This is characterized by

providing a continuous data channel through a serial interface. This transmits the

wave trace of the called V2 in cardiology. The original protocol has a sampling rate of

300 samples/second (Hz), and a high resolution mode with an accuracy of 150 values

per mV.

Thus, let a sampling frequency (ω), with a value of 300 Hz. It is required in total

for each pulse of χ bytes, equal to 236 bytes for the case of 76 ppm following the

equation 1. Table III presents an example of the raw data.

60*

(1)

In addition, it is important to determine how many time is required for each byte, in

order to calculate the relevant medical intervals, which are able to be used for a pre-

diagnosis analysis. Such as, it will be presented in the Section 4. The time the time

required per byte is determined following the equation 2.

1 byte/sec / 300 bytes/sec = 3,3 ms/byte (2)

4 Arrhythmia Analysis

It is carried out an analysis of the ECG wave in order to determine medical condition

of patient, through a heart arrhythmia and anomalies classification [10]. This does not

replace the diagnosis process from a specialist, but this offers an initial approximation

of the patient's health status.

The arrhythmia analysis is carried out through the reconstruction of the PQRST

complex (see Fig. 1). It is pre-processed in order to obtain the segments, ranges, am-

plitudes and polarities for each one of the curves from the PQRST complex.

Fig. 1. Trace representation of pre-processed ECG. In the upper left is presented the reference

wave. The points are P: green, Q: yellow, R: Pink, S: blue, and T: dark blue.

Specifically, the segments considered to reconstruct the wave are: starting of P

(P), difference between P and Q (PQ), difference between S and Q (QS), difference

Wave trace

of reference

ECG curve

between S and T (ST), segment T which presents from the beginning of wave T until

its end (T), and finally the segment between T and P from the next heartbeat (TP).

In addition, it is considered a set of intervals with clinical relevance. The intervals

considered are the PR interval, which is the combination of the P and PQ segments.

The PQ interval is similar to the former is the addition of the segments QS, ST and T.

Finally, the QRS interval coincides with QS segment, which is calculated for the

reconstruction. You can distinguish these intervals in the Fig. 1.

Thereby, this analysis can be carried out directly over the reconstruction parame-

ters, and consequently this does not require a more complex analysis. The described

intervals, theirs respective amplitudes and polarities are evaluated following the next

rules, which determine the possibility of that patient suffers of some arrhythmia or

cardiological diseases [10]:

• interval QRS > 0.12 secs Ventricular hypertrophy, necrosis, BCRD,

BCRI, pacemakers, cardiomyopathies, electrolyte abnormalities.

• Sign U <> Sign T Ischemic heart disease, hypokalaemia.

• interval PR > 0.20 secs First-degree AV block.

• interval PR < 0.12 secs Tachycardia, WPW, manners or headphones low rates.

• interval QT > 0.45 secs Antiarrhythmic medicines, ischemic heart

disease, cardiomyopathies, hypocalcemia, mixedema, long QT syndrome.

• interval QT < 0.35 secs hypercalcemia, hyperkalemia, early

repolarization, digoxin.

5 Communication Model Optimized for NFC

The data collected from the ECG module presented in Section IV can be transmitted

directly, where approximately 250 to 300 bytes are required to transmit every beat.

This direct transmission means a high overload and impact in the quality of service

(delay and latency) and lifetime of the personal device. For that reason, it is defined

an optimized communication model for NFC, in order to increase the lifetime of the

system and considering the requirements of a personal system of this kind should

reach a duration greater than hours, even days. The communication model considered

to perform the pre-processed is the YOAPY module [6]. YOAPY has been already

used with 6LoWPAN technology and offer a suitable solution to reduce dramatically

the number of bytes required to transmit for each heartbeat.

The pre-processing and compression of an ECG trace are methods and techniques

investigated throughout the literature. Some of the most relevant studies are based on

methods based on “wavelet”, which achieves compression of about 18:1 [11]. These

methods are focused on the complex formed by the QRS, which is the group of waves

that are marked on the signal from the wave of the electrocardiogram. The QRS com-

plex contains more clinically relevant data from the cardiology point of view. This

will determine whether the patient´s condition is normal, or is occurring abnormality

(arrhythmia) in the patient´s heart. Fig. 1 has shown the identification of the QRS

complex and the other significant points in the captured waveform from the module

shown in Fig. 2. The problem encountered is that wavelet-based methods are not

entirely suitable for devices with limited experience as the NFC adapter electrocardi-

ogram developed in our previous works and for embedded intelligent systems [12].

For that reason, this work is based on a more simple pre-processing solution,

based on the representation of the waveform from its amplitude and time difference

between each of the significant points of the curve [10], i.e., P, Q , R , S and T, since

this is really relevant information. Thus, most of the points as close to the value

“0x7F” in Table III corresponding to no signal, can be omitted.

Fig. 2. Right: ECG module connected to a voluntary patient. Left: Evaluation environment

formed by a wearable 3 leads electrocardiogram.

YOAPY module is based on detect maximums and minimums values in each

wave of the PQRST complex, i.e.: P maximum, Q minimum, R maximum, S mini-

mum and T maximum. In addition we get the more descriptive segments, what permit

us transport, and redraw the PQRST complex. The segments can be differentiated on

the presented and described Fig. 1 in Section IV, where the consecutive segments

showed in the bottom of figure are for their reconstruction, and the intervals showed

above the segments corresponds with the medical interest intervals.

YOAPY format data contains the most significant fields to represent and for the

development of embedded intelligent systems for the detection of abnormalities. The

data obtained information through YOAPY module defines the payload for the NDEF

message, which is transmitted through NFC. YOAPY format contains five maxi-

mum/minimum values, six segments, the heart beats, and one byte for describe de-

tected anomalies founded by a simple analysis about the length of some segments,

this are 13 bytes.

The meaning of the fields is:

• maxP: Represents the height from the P wave onset (iniP), to the maximum value

of the P wave. Ie: (P– iniP) 137 – 127 = 10.

• minQ: Represents the height from the Q wave onset (iniQ), to the minimum value

of the Q wave.

• maxR: Represents the height from the R wave onset (iniQ), to the maximum

value of the R wave.

• minS: Represents the height from the S wave onset (iniQ), to the minimum value

of the S wave.

• maxT: Represents the height from the T wave onset (iniT), to the maximum value

of the T wave.

• segP: Represents the length of the segment P, from the init P wave, to the final P

wave, as shown in Fig 2.

• segPQ: Represents the length of the segment PQ, from the final of P wave, to the

init of Q wave, as shown in Fig 2.

• segQS: Represents the length of the segment QS, from the init of Q wave, to the

final of S wave, as shown in Fig 2.

• segST: Represents the length of the segment ST, from the final of S wave, to the

init of T wave, as shown in Fig 2.

• segT: Represents the length of the segment T, from the init of T wave, to the final

of T wave, as shown in Fig 2.

• segTP: Represents the length of the segment TP, from the final of T wave, to the

init of P wave, as shown in Fig 2. (Represented as unsigned byte)

• BPM: Represents de beats per minute (ppm). (Represented as unsigned byte)

• Diagnostic Byte: This byte indicates through his bits some diagnostics.

o The 1st less representative bit a PR<0.12sec.

o The 2nd less representative bit indicates PR>0.20sec.

o The 3st less representative bit indicates QRS>0.12sec.

o The 4st less representative bit indicates QT<0.35sec.

o The 5st less representative bit indicates QT>0.45sec.

All values except the TP segment (S_TP) and BPM are represented by signed integers

because their values never overflow the limit of 127.

Table 4. Pre-processed format (real values).

0 1 2 3 4 5 6 7

P Q R S T S_P S_PQ S_QS

6 0x44

-4

0xFC

0x35

-4

0xFC

0x0F

0x22

4 0x04

0x23

S_ST S_T S_TP BPM DIAG

4 0x04

0x3F

0x38

0x52

0x00

6 NFC Real Time Monitoring System

The system is composed of two parts with their respective software. On the one hand,

the PC has a Java program that reads, analyzes, compresses, and encapsulates the

received frames from the ECG module through the serial interface in a NDEF mes-

sage. This NDEF message is sent through the NFC USB reader via the smartcardio

library. On the other hand, an application for Android OS has been developed, for

processing the received frames through NFC and presents it in a plot. Fig. 2 also pre-

sents the wearable ECG connected to a voluntary patient, and transmitting data con-

tinuously to the PC and Fig. 3 presents a capture of the smart phone receiving com-

pressed frames and represented in a plot. In addition, it is available a video for watch-

ing the monitoring system running

DEMO video of the monitoring system: http://www.clitech.eu/ECG_continuous.mp4

The part of the PC corresponds to the data acquisition phase by a PC, which is fo-

cused to be replaced by an embedded device such as personal device previously de-

veloped and presented in [12], called Movital. For that reason, it has been also con-

sidered the mentioned constrains for the processing of the ECG wave trace.

The application for the Android OS is called AppAndroidECGPlotter. This appli-

cation consists of a simple package that contains a Java class, whose functionality is

responsible for obtaining from the Android Intent dispatcher, the NDEF packages

received from the PC. NDEF record has the format presented in Table II, where the

payload corresponds to the package described in Section V and Table IV. Finally note

that the payload after pre-processing with YOAPY module is only13 bytes.

Fig. 3. Google Nexus S receiving data from ECG via NFC.

Fig. 3 shows the Android mobile device over the RFID/NFC reader. This shows

the vital sign from the electrocardiogram, the top of the screen also presents the de-

tected physical problem of the heart detected, if they were found. Remark, the picture

corresponds to a real failure from one of our volunteers. Finally, this plots the wave-

form.

Finally, we have made a comparative and evaluation of the time and delay for

pushing NDEF messages for a continuous monitoring from the USB RFID/NFC read-

er to the smart phone. It has been compared between a version of the solution based

on the full wave trace transmission, i.e. the 250-300 bytes per heartbeat from the

RAW mode, and the YOAPY pre-processed mode of sending only 13 bytes per heart-

beat.

It has been found that for sending the 250-300 bytes received in RAW mode from

the ECG, it is required to send from 2 to 3 frames, each of them is a complete frame

as the Table IV, note that cannot chuck the payload for the NPP protocol restrictions

(limited to 127 bytes such as mentioned in Section II). The average for the times for

transmissions measured are 2372,25 ms (2 seconds) for RAW mode, and 22,5 ms

(0,02 seconds) for the solution based on the YOAPY mode.

In conclusion, the RAW mode transmission produces a delay for real-time and

continuous monitoring of vital signs. Since this requires more than 2 seconds for

delivering a sample which is obtained each less than 1 second (76 bpm, means a

heartbeat each 0,79 seconds). Therefore, the use of RAW mode is not feasible, since it

produces an accumulative delay. This high time is because the time required between

the pushing of the multiple NDEF messages. However, the use of YOAPY mode, and

its compression to send this information allows to reach a short delay, around 0,02

seconds, which is under the threshold of the 0,79 seconds.

7 Conclusions and Future Work

This work presents the integration of a clinical device with continuous data transmis-

sion requirements in NFC technology. This has been concluded that the direct trans-

mission of the collected data from an electrocardiogram is not feasible, since the de-

lay introduced for the transmission of multiple NDEF messages, i.e. the time required

for pushing a new NDEF message is excessive. But, this is suitable when the required

frame size is compressed to fill in a single NDEF record, i.e. less or equal to 127

bytes because the NPP constrains. For that reason, it has been also presented a pre-

processing module called YOAPY, which compresses and analyzes the vital signs

making feasible its continuous and real-time transmission.

Future work is focused on extend de analysis of the capabilities for real-time

transmission of NFC communications, and the extension of the integration to Blue-

tooth Low Energy technology.

Acknowledgements

This work has been made possible by the means of the Excellence Researching Group

Program (04552/GERM/06) from Foundation Seneca, FPU program (AP2009-3981)

from Education and Science Spanish Ministry, with funds from Science and Technol-

ogy Program (PCTRM 07/10), through the project “Modelización Hidrológica en

Zonas Semiáridas” from the Autonomous Region of Murcia (CARM), and in frames

of IoT6 European Project (STREP) from the 7th Framework Program (Grant 288445).

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