Literature Survey of Wireless Body Area Networks with a focus on Cardiac Sensor Networks

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Nov 16, 2013 (3 years and 8 months ago)

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Literature Survey of Wireless Body Area Networks with a focus on
Cardiac Sensor Networks


Abstract

This paper aims to provide an overview of Wireless Body Area networks(WBANs) .The paper
will be divided in describing some of the most important aspects like network architecture, signal
processing and data analysis, use of Ultra Wide Band (UWB) and Govern
ment issues and the
and implementation challenges faced in such networks.
Finally, the paper attempts to outline of
Cardiac Sensor Network

(CSN),

a specialized form of WBAN.


Keywords
:

WBAN,

CSN, Network Architecture, Implementation Challenges, Data
analysis


Introduction

Wireless body
-
area network (WBAN) is a wireless
-
sensor network that incorporates different
networks and wireless devices to enable remote monitoring of
the human body functions

and the
surrounding environment.

Technological advanceme
nts in sensors, low
-
power integrated
circuits, and wireless communications have enabled the design of economically viable
miniaturized sensor nodes that can measure vital physiological parameters. These sensor nodes
can be seamlessly integrated into wirele
ss body networks
WBANs
for remote health monitoring.
WBANs
can transform

health care by

providing
inexpensive, non
-
invasive, continuous,
ambulatory health monitoring with almost real time updates of medical records via the Internet.
Though there are many socio
-
economic issues about WBAN, yet there are many technical issues
to be considered in order to have flexible, reliable, secure, and power
-
efficient WBANs suitable
for medical applications. The focus of this paper will be to provid
e an overview of such technical
aspects like
network architecture, signal processing, data analysis, use of Ultra Wide Band
(UWB), Government issues and the implementation challenges faced in such networks. To
provide a better insight into the network arch
itecture of WBAN details system architectures,
protocols, design layers and integration of hardware and software would be explained. However,
setting up such a network comes with many challenges like power management, reliability, QoS,
time synchronization

and energy efficiency and these challenges would be discussed elaborately.

The
last

section would highlight
a
novel application

of WBAN in

Cardiac Sensor Network
(CSN)
that is dedic
ated to monitor heart condition parameters from sensors implanted on or
in
side the patient’s body
.

One of the primary focuses of this paper is to understand the working
principle
of such a network with the model of a

[1]
cardiac sensor that
measures
important

physiological parameters within the constraints of miniaturization, low power and designated
bandwidth
.





Network Architecture


One of the
[2
]
proposed wireless body area sensor network for health monitoring is illustrated in
Figure 1. The system span
s

a network comprised

of individual health monitoring systems

in
WBAN(first tier) that connects with a Personal Server

(second tier) which in turn communicates
to a medical server tier that

resid
es at the top of this hierarchy, an example of WBAN
integrated

into a broader multitier

telemedicine system
.



Each user wears a number of
strategically placed
sensor nodes

that make up the Wireless Body
Area Network that serves tier 1.
It

comprises a number of intelligent
sensor
nodes, each capable
of sensing,
sampling, processing, and

communicating of physiological signals. For example, an
ECG sensor can be used for monitoring

heart activity, an EMG sensor for monitoring muscle
activity, an EEG sensor for monitoring brain

electrical activity, a blood pressure s
ensor for
monitoring blood pressure, a tilt sensor for monitoring

trunk position, and a breathing sensor for
monitoring respiration, while the motion sensors can be used

to discriminate the user’s status and
estimate her or his level of activity.

[3
]These

nodes can have different topologies such as star,
tree,

and mesh topologies. However, the most common is

a star topology where the nodes are
connected to a

central coordinator in star manner
.

Each
sensor node receives initialization
commands and responds t
o queries from the personal

server. The

nodes continuously collect and
process raw information, store them locally, and send

processed event notifications to the
personal server. The type and nature of a healthcare application

will determine the frequency
of
relevant events (sampling, processing, storing, and communicating).

Ideally, sensors periodically
transmit their status and events, therefore significantly reducing power

consumption and
ex
tending battery life. T
he relevant data

is transferred to the se
cond tier, a personal server,
through wireless

personal network implemented using
protocols like
ZigBee (802.15.4) or
Bluetooth (802.15.1).


The personal server,

tier 2, is
implemented on a personal digital assistant (PDA), cell phone, or
home personal com
pute
r.

It

sets up and

controls the WBAN

through a network coordinator
and
provides graphical
or audio interface to the user. To transfer

the information

about heal
th status
to the medical server,
the personal server employs mobile telephone

networks (2G,
GPRS, 3G)
or WLANs to reach an Internet access point

.
This

is particularly convenient for in
-
home
monitoring of elderly patients.
The interface to the WBAN includes the network configuration
and management. The network

configuration encompasses the followi
ng tasks: sensor node
registration (type and number of sensors),

initialization (e.g., specify sampling frequency and
mode of operation), customization (e.g., run user

specific

calibration or user
-
specific signal
processing procedure upload), and setup of
a secure

communication (key exchange). Once the
WBAN network is configured, the personal server manages

the network, taking care of channel
sharing, time synchronization, data retrieval and processing, and

fusion of the data. Based on
synergy of informatio
n from multiple medical sensors the PS application

should determine the
user’s state and his or her health status and provide feedback through a user

friendly

and intuitive
graphical or audio user interface.

The personal server holds patient authentication

information
and is configured with the medical

server IP address in order to interface the medical services. If
the communication channel to the

medical server is available, the PS establishes a secure
communication to the medical server and sends

reports

that can be integrated into the user’s
medical record. However, if a link between the PS and

the medical server is not available, the PS
should be able to store the data locally and initiate data

uploads when a link becomes available.
This organization al
lows full mobility of users with secure and

near real time health information
uploads.



The top tier is centered on a medical
server that

is optimized to service

thousands of individual
users, and encompasses a complex network of interconnected

services,
medical personnel, and
healthcare professionals. The medical server keeps electronic medical records of registered users
and provides various

services to the users, medical personnel, and informal caregivers. It is the
responsibility of the medical

server
to authenticate users, accept health monitoring session
uploads, format and insert this session

data into corresponding medical records, analyze the data
patterns, recognize serious health anomalies

in order to contact emergency care givers
.



Another p
roposed network architecture of WBAN is a more simplifie
d model [4
] as outlined in
Figure 2.

It consists of a num
ber of sensors
/actuator

nodes
, a body gateway and a host.

The
nodes

and the body gateway are connected

wirelessly within the body zone in a sta
r or mesh
network

topology and relaying data packets

to or from each other.

Most bi
-
directional

communication is between the gateway

and the
nodes
, but two
nodes

could also communicate

directly, e.g. in a sensor actuator setup where a measured

parameter by

sensor
-
node
-
A (drop of
glucose level) implies

a consequent action to be performed real
-
time by actuator

node
-

B
(injection of insulin). Likewise, the gateway is

connected wirelessly to the host in a bi
-
directional
point
-
2
-
point connection.

In this archite
cture, to satisfy the
key

attributes for WBAN like
r
eliability, scalability,

interoperability,

security,

power efficiency, and ease of use and
configuration,
Time Synchronized Mesh Protocol (TSMP)
has been
proposed in

[

5
], now being
integrated into the
emerging IEEE 802.15.4E

standard
.

Key components of TSMP are:


∙ Time synchronized communication


∙ Frequency hopping

∙ Automatic mode joining and network
formation

∙ Fully
-
redundant mesh routing

∙ Secure messag
e transfer


In TSMP each transmission, transacted
in a synchronized

specific timeslot,
contains a single packet and

acknowledgements which are generated when a packet has

been
received unaltered and complete. Use of frequency

hopping reduces the impact of
interferences
and increases the

effective bandwidth. Aggressive use of duty
-
cycle and timeslot

based
principles makes the protocol very power

efficient. A key attribute of TSMP is its self
-
organization

mesh routing that makes it easy to add/remove
nodes
.

Finally, TSMP support
encryption, authorization and

integrity with regards to secure message transfer.


The gateway performs
the following main functions:


∙ Configuration and synchronization of the BAN

∙ Controlling and monitoring of the
nodes

∙ Collectio
n and further processing of the sensors data

∙ Forwarding of processed and aggregated data

wirelessly to the host for further processing and

interpretation

∙ Reception of commands from the host


This r
equires hardware that includes
a transceiver

supporting

the communication within the
W
BAN (
nodes
), a

transceiver support
ing the communication with the h
ost, a

powerful processor
including memory and storage, and a

power supply unit including a (rechargeable) battery.

The form factor and weight of the gateway s
hould be

tailored to be wearable with minimal
impact on the body

comfort, e.g. like a modern Smartphone or smaller.


The second network does not strictly differentiate the hierarchies as the first one but the
functions of each level in both are essentially the same. Also, the second network refers to a
body gateway(which can also be implemented on a PDA like smartphone)
at the intermediate
level which performs the same function as the personal server in the first structure .Thus the
above two network architectures are almost similar except the use of different protocols: the
second network uses TSMP(and mesh network topol
ogy) as opposed to the first one first
network using Zigbee or Bluetooth(that uses mainly star topology).TSMP has the added
advantage of time synchronization though it might have the disadvantage of slightly higher costs
of setting up the higher number of
links in a mesh topology than that of the star topology in
Zigbee/Bluetooth. Also, TSMP being a more recent protocol has not yet undergone mass
deployment and hence its
performance evaluation still needs further assessment.



Implementation Require
ments an
d Challenges


Wireless medical sensors should satisfy the main requirements such as
wearability
,
reliability
,
security
,
inte
roperability
, functionality

and some corresponding challenges like time
synchronization, energy efficiency (low power constraint),
interference, physical layer
communication,

bandwidth constraint, regulatory issues and low cost.


Wearability

To achieve non
-
invasive and unobtrusive continuous health monitoring, wireless

medical sensors should be lightweight and small. The size and weight of sensors is
p
redominantly

determined by the size an
d weight of batteries. In turn

a battery’s capacity is
directly

proportional to its size

which leads to the challenges of energy efficiency and

miniaturization

discussed in later sections.



Reliable communication

in

WBANs is of utmost importance for

med
ical applications as these
are subject to very sensitive information about patients.
It co
uld be dangerous, even fatal,

for false readings to appear on a patient's glucose monitor

output. The same is true of err
oneous
images being pro
jected in the eye or for any other function of the biomedical

sensors
.

M
edical
sensors vary with required sampli
ng rates,
[6]
from less than 1 Hz to 1000 Hz. One approach

to
improve reliability is to move beyond telemetry by performing on
-
sensor signal processing. For

example, instead of transferring raw data from an ECG sensor, we can perform feature extraction

on
the sensor, and tr
ansfer only information about a particular event like Arrythmia

. In addition
to reducing heavy demands for the

communication channel, the reduced communication
requirements save on total energy
,
expenditures, and consequently increase battery life. A careful
trade
-
off between
c
ommunications

and computation is crucial for optimal system design.


Security

The problem of security arises at

all three tiers of a
WBAN
-
based telemedical system.
At the lo
west level, wireless medical

sensors must meet
confidentiality

requirements mandated
by the law for all medical devices and must

guarantee data integrity. Though key establishment,
authentication, and data integrity are

challenging tasks in resource constr
ained medical sensors, a
relatively small number of nodes in a

typical
WBAN and short communication ranges make
these tasks achieva
ble.

Although it is important that the physician or

the patient see the feedback
from the sensors, it is not in
-
formation tha
t necessarily should be broadcast publicly. In

addition,
it is not desirable for an outsider to gain access to

the sensors or the display

.Hence
strict security
mechanisms
are desirable that would pre
vent malicious interaction with these systems. Although
it

may seem attractive to en
crypt all of the data, [7] any mean
ingful, strong encryption
would be
too computationally in
tensive to be practical for these uses
.


Interoperability
Wireless medical sensors should allow users to easily assemble

a robust

WBAN
depending on the user's state of health. Standards that specify interoperability of

wireless medical sensors will help to integrate different types of medical services and also
promote vendor competition that would eventually result in more affordable syst
ems.


Functionality

most of today's
bio sensors
act as simple gateways, passing on the information to a

central hub where the data is converted into actionable

information. By adding intelligence to the
sensors, they

can take decisions locally and the sign
aling overhead in

terms of data and latencies
can be reduced. In addition,
[8]
intelligence is required for the system to make

decisions
depending on the status of the environment,

thus enabling context awareness. Finally, embedded

intelligence opens the d
oor to closed
-
loop systems,

providing action or feedback to the user.


Physical Layer Connection in WBAN

T
he main challenge in
ensuring
communication comes
in establishing the Physical Layer Communication

(Signal Propagation) in WBAN
.


If the sensors are

implanted inside the body, they can communicate through electromagnetic
coupling or radio frequency (RF) communication d
epending on the applications. [3
]In EM
coupling, the implant is powered by the coupled magnetic field and requires no battery for
commu
nication. Data is transferred from the implanted device by altering the impedance of the
implanted loop that is detected by

the external coil and electronics. It achieves the best power
transfer when using large
transmit

and
receive

coils. However, it is i
mpractical when space is an
issue or devices are implanted deep within the patient. In such cases RF communication enables
a two
-
way data link that allows an implant to initiate a communication session. This requires an
implanted battery, electronics, and
a suitable antenna that can operate on the MICS band which
is more widely used for in
-
body communication systems. This band has a power limit of 25
μW
in air

and is split into ten wide channels where each channel has 300 KHz bandwidth. The human
body is a
medium that poses numerous wireless transmission challenges. The body is composed

of various components that are not predictable and will change as the patient ages, gains or
losses weight, or even changes posture. Unlike the usual communication through co
nstant air, the
various tissues and organs within the body have their own unique conductivity, dielectric
c
onstant and characteristic impedance. As a result, signal level and propagation from an
implanted device to a remote receiver is unpredictable. Hence, with widely
variable
circumstances
, antenna design becomes crucial so that it can serve for multiple envi
ronments.


Interf
e
rence

Between all of the proposed applications for biomedical

wireless sensor networks
and the great number of people

a
ffected

by diseases that might be helped with the use of

these
networks, it is not unreasonable to expect hundreds

of t
housands of these networks in place in
the next decade.

This can lead to the probl
em of interference between wire
less networks in
people standing next to each other or even

the possibility of
colliding

signals within one person.
It

is clear that informatio
n from one person's network should

not manifest itself on someo
ne
else's display system. Inter
ference might also come f
rom other types of wireless com
munication,
such as micro
wave ovens or Bluetooth devices or in some cases some EM devices too.


Time
Synchronization

is a common requirement for wireless sensor networks since it allows
collective signal processing, sensor and source localization, data aggregation, and distributed
sampling. In wireless body area networks,
[6]

synchronized time stamps are
critical for proper
correlation of data coming from different sensors and for an efficient sharing of the
communication channel.


Energy Efficiency

Energy consumption is
one of the fundamental

design
constraints

in wireless
sensor networks since
sensor no
des have size restrictions and can be operated by either battery or
wireless power transfer
. To extend each node’s lifetime, it is necessary to reduce power
dissipation as

much as possible
.
I
f the node is implanted in the body, it is

not practical to replace
the
battery as often as would be re
quired.

[7]

Passive power sources, such as solar and vibration,

provide insufficien
t power for continuous operation. Wireless sensor networks require some
form of energy for various functions in e
ach

node, including running the
sensors, processing the
informa
tion, and data communication. Ideally, this power will be

evenly distributed and
consumed among the nodes in the

network. An even consumption of power would allow the

nodes to be recharged simu
ltaneously, thereby reducing the

use of bandwidth for recharging.

Biomedical wireless sensors add additional
power
constraints
due to

heat dissipated from using

the power. Depending on where in the body the sensor

is placed, the allowable amount of heat
di
ssipated varies.

[7]
Chronic implantation requires much lower dissipation, so as

to not damage
the tissue surrounding the sensor.
[6]

Various design trade
-
offs between communication and on
-
sensor

computation, collaborative protocols, and hierarchical networ
k organization

can yield
significant

energy savings. Once the sensor network is deployed, dynamic power management
techniques can

be employed in order to maximize battery life.


Regulatory
Requirements

Food

and Drug Administra
tion (FDA) regulates
the
testing and use

of
biomedical

sens
ors .
There must be

some evidence that these d
evices will not harm, and
potentially help
the test subject
s. Procedures for protecting pa
tients have been developed for
clinical trials.
[7]
While animal

testing is used for pre
li
minary data, it is often insuffi
cient for

determining the effect

of the sensor networks.
Hence,

even prototype devices will have to meet

the strict standards of patie
nt safety before any human test
ing can be done. The wireless
transmission of data must

n
ot harm the surrounding t
issues and the chronic function
ing and
power utilization o
f these devices must also be be
nign.

Besides, r
esearchers in biomed
ical
sensors must consider ethi
cal and moral issues that do not arise in most other sensor

applications.


Bandwidth

The frequency range
selected
for communications plays

an important role in the
design and performance of
WBAN as there is a direct relationship be
tween frequency and tissue
warming. The higher the

frequency of the EM signal, the higher is its
absorption

by the tissue
and more the tissue warming. Hence it is

desirable to use lower frequencies for communications.

However, the lower the frequency, the larger will the

antenna dimensions have to be.
T
herefore
there is a trade
-
off

between antenna dim
ensions and greater tissue warming
.Moreover, t
he
human body
being

composed mostly of water

that

absorbs a lot of
radiation, the percentage of
wa
ter in the tissue has a bearing on the signal strength

of the EM signal. However, the water
content is not

uni
form throughout

the body. Fat and bone are rel
atively dry and absorb less
energy when compared to

wetter tissues such as muscle [
9
]. This variation in

energy absorption,
coup
led with the possibility of tis
sue damage due to heating by radiation, complicates

the design
of biosensors and
WBANs. T
o avoid excessive radio interference as well as

having to obtain
FCC licenses, it may be advisable to

use the designated, unlicensed ISM frequency bands.

[7]
An
FCC regulation, however, makes it mandatory for

all commun
ication on the ISM frequency band
to use

spread spectrum techniq
ues. This further increases the com
plexity of the transceivers on
the biosensors
.


Low
C
ost

[8] T
oday's wireless

sensor systems cost around 100€ or higher. A major

reason for
this is the low
volume of the market so far,

but another more technical reason is that there are no

commercially available packaging technologies that

can efficiently integrate such heterogeneous
components

as batteries,
MEMS
,

processors, and radios in a single package
.


Use of
UWB
and other bandwidths in WBAN
by government

regulations


[3
]
A WBAN uses Wireless Medical Telemetry Services

(WMTS), unlicensed Industrial,
Scientific, and

Medical (ISM), Ultra
-
wideband (UWB) and Medical

Implant Communications
Service (MICS) bands

for

data transmission. WMTS is a licensed band used for

medical
telemetry system. The Federal Communication

Commission (FCC) urges the use of WMTS for

medical applications due to fewer interfering sources

.
Energy absorbed by the body when
exposed to RF wa
ves and is

measured in watts per kilogram

(
http://wireless.fcc.gov/services
).
However, only authorized

users such as physicians and trained technicians

are eligible to use this
band. Furthermore, the restricted

WMTS (14 MHz) bandwidth cannot support video
and

voice transmissions. The alternative spectrum for medical

applications is the 2.4 GHz ISM band
that includes

guard bands to protect adjacent channel interference.But this band is also used by
other technologies, such

as Bluetooth, Zigbee, andWiFi. A li
censedMICS band

(402

405 MHz)
is dedicated to implant communication



However, among all the bands, a special inclination is towards
UWB

that

comprises the
frequency range
from
[10]

3.1GHz to 10.6GHz in America constituted by
FCC.
UWB pulse
characterizes
medical

applications with instantaneous spectral occupancy in a

fractional
bandwidth
.

Such features rely on ultra
-
short


(nanosecond scale) waveforms that can be free of
sine
-
wave

carriers and do not require IF processing because they can

operate at baseba
nd.
Because the employing pulses with

ultra
-
short duration have UWB spectral occupancy, UWB

radios come with unique advantages that have long been

appreciated for medical engineering: 1)
enhanced capability

to penetrate through obstacles; 2) ultra high pre
cision ranging

at the
centimeter level; 3) very low spectral mask deciding

much little electromagnetic radiation for
environment and

human body and; 4) potentially small size and low processing

energy
consumed. Because the UWB impulse of every bit is so ma
ny that it

could get much higher gain
than conventional spread

spectrum systems. The feature makes it easy to image organs

of

human
body for medical application.There is another advantage based on the ultra
-
short pulse,

which is
strong multi
-
path resolving

ability. Because the radio

frequency signal of the conventional
wireless technique used

continuous wave, whose standing time is much longer than

multi
-
path
transmission time. The UWB pulse is so short that

it has very strong temporal and space
resolving a
bility ( mutilpath resolving power equals to 30cm), which is

suitable for the location
and detection in the medical
field
.

The third is low radio power of pulse, which is less than

-
41.3dB in room. The low radiation could influence the

environment around
much little, which is
suitable for hospital

room. T
he low radiation is safe for human body, even in

the short distance,
which make UWB as the clairvoyant

equipment possible.

However, use of UWB in WBAN is
still subject to government regulations and the fol
lowing outlines some of the FCC designated
bandwidth for medical purposes.


[11]
Wireless medical devices
must pass three challenging requirements

for regulatory
authorities:
(1)

they
must be
compatible with other us
es of the spectrum; (2)

be
safe and
effective for the
patient

(3)
be cost effective.



Generally, wireless medical devices fall into one of two informal FCC categories: short range
or long range. Short range technologies transmit
data from

the patient to a local

receiver/monitor.

Long r
ange technologies generally transmit patient data directly to a remote monitoring location.
As new communication techniques are developed for medical applications, the FCC often must
adjust its rules to accommodate these advancements.

B. Short Range Device
s for Patient Monitoring, Control and Diagnostics


Available technologies and the FCC services for short range patient monitoring include:

(usually cardiac implants)

operate in the bands below 200
kHz and communicate a
t distances of less than one foot from the patient’s body.

licensed communication between body implants and a nearby controller, the FCC added more
frequencies to this se
rvice in 2009 for use by body
-
worn monitoring devices. These devices
operate in the 401
-
406 MHz band at distances up to about 10 feet.

-
Fi, Bluetooth and Zigbee: These unlicensed technologies are commonly used with cell
phones, hand held devices and

personal computers, but can also be used for implanted or body
-
worn medical devices. These devices operate in the 902
-
928, 2400
-
2483.5 and 5725
-
5850 MHz
bands at distances up to a few hundred feet.

-
Wideband: New uses of unlicensed ultra
-
wideban
d technologies are starting to emerge
for

medical telemetry and imaging
applications. These devices operate at very low power in
almost any region of the spectrum at distances up to a few feet.

a
ccommodate operation of implanted microstimulator devices that might lead to the creation of
an artificial nervous system that could restore mobility to paralyzed limbs. The
se devices will
operate in the 413
-
457 MHz band at distances up to a few feet.

wireless personal area network (“PAN”) of multiple body sensors to monitor or control
patient
functions. These devices operate in the 2360
-
2400 MHz band at distances up to a few feet. 5

Long
-
Range Medical Telemetry:


data from body sensors to remote monitoring locations. These devices operate in various bands
between 600
-
1432 MHz band at distances up to several hundred feet.

wide Interoperability for Internet Access (WiMAX): Often referred to as a

“last mile”
broadband access technology, WiMAX provides wireless transmission using a variety of
transmission modes, from point
-
to
-
multipoint links to portable and

fully mobile Int
ernet access.
The technology provides up to 70 Mbps broadband at distances over several kilometers. The
technology is based on the IEEE 802.16 standard (also called Broadband Wireless Access) and
uses frequencies around 2.5 GHz in the US.

Data and sig
nal Processing

in WBAN

WBANs
typically
require real time signal processing

though with

stringent energy constraints,
hence
careful

consideration of computation and communication is crucial for

extended system
lifetime.
The difficulties of the design of the wireless control and

signal processing systems are
further enhanced through the introduction of noise

[11]
generated from loose physical contact of
the sensor node due to the highly
mobile and

pervasive environment
of the BAN
.

Several schemes for
optimal data analysis and noise
mitigation have been proposed
one
of which is

briefly discussed.

Figure 3: Block Diagram of Active Noise

In [12] an Active Noise Control system is designed to perform noise elimination alongside
signal proce
ssing .Figure (3) shows the
block diagram of the
proposed design
.

The main parts

of
signal processing
a
lgorithms are implemented on a DSP

evaluation board of type ADSP
-
21364
EZ
-
KIT LITE [
13
],

which includes an ADSP
-
21364 (SHARC) processor. These motes are
intelligent

sensors that
c
onsist of an ATmega128 eight bit

microcontroller with a clock frequency
of 7.3728 MHz, a

CC2420 2.4 GHz ZigBee compatible radio transceiver and

an MTS310 sensor
board. The data transfer rate of the

transceiver IC is 250 kilobit pe
r second (kbps) including a

preamble section, a header and a footer that are handled by

hardware. The sensor board includes
also a microphone

with variable gain amplifier, the output signal of which is

converted by a 10
bit analog to digital converter (ADC
) of

the
microcontroller
.

Data

from the wireless network are
forwarded to the DSP by a

gateway mote (mote0
in Fig. 3
). The DSP and the gateway

mote are
connected via asynchronous serial port. The

transmission rate of communication between the
two units is

115.2 kbps. In order to ensure the reliable and real time operation of

the system

Time
Division Multiplexing (TDM)
is deployed.
The PC basically serves as developing and debugging

tool for both platforms. Additionally, it is suitable for

logging and visual
ization of data sent by
gateway or DSP

over serial port.

.

Cardiac Sensor
Network

Cardiovascular diseases remain the biggest cause of deaths worldwide and hence special
attention is paid to enhance diagnosis and treatment for these diseases
.

Since the
invention of
ECG

(electrocardiography) at the 20
th

century, diagnostic and monitoring devices have obtained
the ECG signal with electrodes placed at the extremities of the torso and connected to a

fixed

medical instrument through a cable.
[1]
Microprocessor
s and digital signal processing techniques
have made possible automatic interpretation of
multiple channels of ECG. Moore’
s law

has
allowed engineers to improve the accuracy of ECG interpretation by applying ever increasing
computational

power.
Improvement
s in hardware have enabled portable ECG devices which are
still too bulky, inconvenient and expensive. Moreover, other vital signs like Blood Pressure,
Oxygen level and Pulse rate have to be taken into account for holistic assessment of heart
condition. He
nce, for efficient, continuous yet economic monitoring, miniaturized cardiac
sensors have been proposed
.

A specialized form of WBAN is cardiac sensor networks. Many
types of cardiac sensors have been proposed and some are in use. One such prototype
is
discussed which is a [1]
wearable

single channel wireless smart
ECG sensor
that
has been
developed for long
-
term monitoring of patients
at risk of life
-
threatening cardiac arrhythmias
.
The battery
-
operated sensor can be applied in virtually any orien
tation
on
the

upper left quadrant

of the chest
, and monitors patient
health continuously

for up to three days. An embedded
microcontroller calculates heart rate and monitors the ECG signal for life
-
threatening
arrhythmias, which are transmitted
wirelessly to a central server and relayed to a
respondent device
.

Networking and data analysis of such a cardiac sensor can be achieved through the

proposed
mechanism
[14
]

estimates

the data quality of a BAN of cardiac sensors while

particularly
considering the resource scarceness. First, the

method
performs
local data quality estimation

where it
filters out most of the normal events and

recognizes any abnormal
events (e.g., motion
artifact noise or

health
hazardous events) at
individual sensors. The
second step is

referred
Figure4: Local Data Estimator
1

to as
global estimation
that aggregates information

about the local data quality from all sensors
and fuses the

information in order to estim
ate the data quality of the overall

BAN.


The objective of the local estimation is to detect any

abnormal events in data generated from a
single cardiac sensor.

This mechanism is based on a well known fact that amplitude

of cardiac
signal and inter

pulse
interval (IPI) variability are

effective bedside measurements to detect any
abnormal events

The overview of the local estimation is illustrated

in Fig. 4
. First, the raw
cardiac sig
nal goes through a cardiac cycle
locating process to partition the signal i
nto cardiac

cycles. This process starts with a sequence of cascaded linear

digital filters that performs the pre
-
processing on the raw

cardiac signal before the peak detection process. The three

filters include

(
an integer coe
fficient band
-
pass filter,
a

d
erivative filter combined with
amplitude

squaring
process,

and
a

moving
-
window
integrator [14
]. The band
-
pass filter

rejects unnecessary noise
and the derivative filter provides

the slope information of the filtered signal. The amplitude

squaring process
makes all data points positive and emphasizes

the high amplitudes to make the
peak detection easier. The

moving
-
window integrator provides various waveform feature

information in addition to the slope information. Then, the

filtered signal goes through a p
eak
detection logic which locates cardiac cycles.

Using the information about the location of cardiac
cycles,

the time length of each cardiac cycle (i.e., IPI)

and the average amplitude of each cardiac
cycle

are

extract
ed
. The time

series of IPI is further

filtered out using a high
-
pass filter to

rem
ove
the trend as shown in Fig. 4
.
The data fusion process is performed at the aggregator side

where

the
information

collected from each

sensor
is used to analyze the quality of the received data
with respect to
what is transmitted
.

This investigates
different features of

medical signals such as
amplitude,

phase shift,

or temporal behavior to

detect any changes made between the data
transmitted from

the sensor and the d
ata received by the aggregator; such changes might occur
due to corruption by noise in the channel or even in the sensor node itself.
These

approaches
work for
not only for channel

noise or
interference but also for noise
generated from the

sensor
node
. Ho
wever, the most
useful application

of this method could be to detect anomalies in
cardiac cycle

(compared to a regular
cardiac cycle of a healthy person
)

.
Such anomalies could be
events like
[14
]
premature ventricular contraction, supraventricular
prematur
e, and

ectopic beat,
which are
usually
manually
recorded
by clinical

professionals
. Since this method allows the
detection of such fatal medical conditions remotely and seamlessly, life saving actions could be
taken more effectively
.


Conclusion


Wireless sensor network (WSN) technologies
have existed for quite long now and are evolving
every day. One such promising application is Wireless body area networks

(WBAN)
, a research
area that integrates wireless technologies with hea
lthcare application i
ndustries for improving the
quality of life.

It enables continuous physiological signal monitoring and supports health
consulting information anywhere and anytime.

The purpose of this paper is to provide a snapshot
of current developments and future direct
ion of research on
wireless

body area network systems
for continuous monitoring of patients.
Several technologies are needed for implementing a
wearable healthcare system. That is, a physiological signal measurement technology to measure
user’s physiologic
al signals continuously and wireless communication technology to construct a
wireless body area network
.
This paper
examines these different technological design
considerations of WBANs like network architecture,

energy efficiency, time synchronization,
sc
alability, bandwidth, signal processing, data analysis and the challenges in meeting all these.
One very crucial prospective use of WBAN is to incorporate existing cardiac sensors in large
scale networks for remote and ubiquitous monitoring of cardiac pati
ents. Hence this paper
described a prototype cardiac sensor and attempted to explain how data from such sensors could
be analyzed and integrated into a Cardiac Sensor Network

(CSN)
, which could be extended as
the future work of this paper
.






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