Development of Telemedicine and Telecare over Wireless Sensor Network

Shuo-Jen Hsu1, Hsin-Hsien Wu

1, Shih-Wei Chen

2, Tsang-Chi Liu

3, Wen-Tzeng Huang

3, Yuan-

Jen Chang4, Chin-Hsing Chen

4, You-Yin Chen

1,*

1Department of Electrical and Control Engineering, National Chiao Tung University, ROC 2Department of Electrical Engineering, National Taiwan University, ROC

3Department of Electrical Engineering, National Taipei University of Technology, ROC 4Department of Management Information Systems, Central Taiwan University of Science and

Technology *irradiance@so-net.net.tw

Abstract

Telemedicine & Telecare aided by wireless sensor network (WSN) has recently become a health-caring trend in the future. However, the low data rate of WSN limited the acceptable quality of service. Researches [1-6] adopted ZigBee-based WSN to implement a platform and/or a telecare system. However, the capacity problem due to low data rate characteristic has not been addressed. Our research proposed an arrhythmia-aware telecare system and a new DSP-based WSN platform, enabling good digital signal processing performance in ZigBee-based WSN. Proved by simulations and several real tests, combination of the proposed platform and system can provide a more bandwidth-conserving and reliable telecare system, with lower wireless traffic jam and higher stability. Keywords: Telemedicine, Wireless Sensor Network,

ZigBee, DSP, Discrete Wavelet Transform

1. Introduction

Telecare has recently become a health-caring trend

in the future. People can receive instant healthcare

services, such as according advice abnormal vital

signals were detected in a hospital, rest home, or at

home, where a telecare system is provided.

Aided with WSN technologies, the ubiquitous and

flexibility of telecare systems can be strengthened with

the network configuration. WSN facilitates the telecare

system with a center-control feature, which is suitable

to deliver the service to a group of people, and to

control the delivering of packets well-ordered. IEEE

802.15.4 and ZigBee, proposed to be the standard of

WSN by IEEE and ZigBee alliance, gained much

attention because of the low power consumption, low

cost, easy deployment, and various network topologies.

However, the low data rate has become the limitation

of the service’s acceptable quality. In [7], for example,

24 kbps was taken as the required data rate for ECG

monitoring application, but IEEE 802.15.4 could at

most provide 240 kbps. That indicated a maximum of

10 devices could be connected to telecare system to

provide ECG monitoring. Such estimation is far behind

the goal of WSN-based telecare system, which aimed

to deliver the service to an unknown amount of users.

Moreover, ECG monitoring service is the least

requirement of a well-designed telecare system since

ECG is the major vital signal which strongly reflects

human body status in an emergency.

In [8], evaluation of ECG monitoring traffic in

WSN showed that the traffic of a node with no data

processing was 4 times larger than one with source

coding to data. Furthermore, reduced traffic also

lowered the probability of packet collision. As ECG

generates more data than other vital signals, a low

delay, low computational complexity, and low

distortion ECG compression algorithm is required for a

telecare system using ZigBee-based WSN. However,

compression algorithms have to deal with many digital

signal processes, resulting in unacceptable delay that

affects the real-time application with a microcontroller-

based platform. Meanwhile, many powerful ECG

compression algorithms [9-12] were wavelet-based,

requiring longer computation time when performed by

a microcontroller. Moreover, no evaluations of these

algorithms have been conducted on any embedded

platform to prove the real-time characteristics.

Resent researches [1-6] have adopted a ZigBee-

based WSN to implement a telecare system. In these

researches, the development of a platform and/or a

telecare system was proposed and tested. Nevertheless,

none studied the bandwidth problem in WSN with

system design, which was related to low data rate. The

platforms designed/used in [1-6] were all

microcontroller-based.

2008 International Conference on Multimedia and Ubiquitous Engineering

978-0-7695-3134-2/08 $25.00 © 2008 IEEEDOI 10.1109/MUE.2008.105

597

This paper established a prototype of a DSP

platform which enables a good digital signal

processing performance in ZigBee-based WSN, and an

arrhythmia-aware telecare system. Discrete wavelet

transform (DWT) and enhanced set partitioning in

hierarchical tree (ESPIHT) [12] were adopted and

modified as the ECG compression algorithm while

transmitting ECG, since it showed excellent

performance and low computational complexity. Our

system reduced the possibility of traffic jam and packet

collision in the WSN. Compared with previous works

[1-6], simulation and testing with several indices, our

system provided a solution for a more bandwidth-

conservable, stable, and reliable WSN-based telecare

system.

2. System Architecture

Figure 1 showed the system architecture of our

proposed system. The hierarchical tree protocol was

adopted. ECG signal of a man which carried a ZigBee

end device would be continuously analyzed by the end

device. If abnormal ECG signals were detected, the

device would immediately locate a wireless channel,

and send the ECG signal to the ZigBee coordinator via

routers. Once the alarm was received, the operator

would respond by sending a telecare service agent for

rescue or check his status for further information. Note

that the ECG detection was designed to be self-

activated, while posture detection was an auxiliary

information to be activated passively.

2.1. Functionality

When an end device was turned on, it would

automatically search an existed WSN to find a suitable

router to join the WSN. If more than one router was

detected, the end device would choose the one with the

highest received signal strength index (RSSI) as its

parent, and send request to joining. Once the end

device receive a network address, indicating the

request was accepted, the end device would send an

authentication packet with the network address and a

pre-loaded authentication key to the coordinator of the

WSN. The end device would be kept in pending until

the coordinator authenticated the key, and a permission

command is sent back to the end device. Before the

new device has been approved, the parent router

would not respond to any request from the end device.

Upon receiving the permission packet, the end device

would sample the ECG sensor every second at 256 Hz.

The QRS/RR detection based on Pan-Tompkins

algorithm would then begin to calculate the QRS, RR,

and average RR width, which would be used to

indicate any occurrence of an arrhythmia. The

Bradycardia, Tachycardia, Asystole, Ventricular

Fibrillation, Skipped Beat, Premature Ventricular

Contraction (PVC), R-on-T Phenomenon, Bigeminy,

Trigeminy, and Interpolated PVC arrhythmias or

normal beat would be determined by the criterions

composed by the three indices [13]. After the QRS/RR

detection, if one of the arrhythmias were detected, a

buzzer on the end device would sound and the sampled

ECG would be transmitted. The ECG would be first

compressed at compression ratio (CR) 8 by our

modified ECG compression algorithm, and then

transmitted to the coordinator via routers. If the

posture query command was received by the end

device, the end device would locate one byte from

each axis and transmit it to the coordinator. Combining

the three values, the posture of the end device could be

Figure 1: System Architecture

598

analyzed (upside-down or normal). Figure 2 showed

the flowchart of the end device’s application.

Figure 2: Flowchart of the end device’s application

The router acted as a relay node between the end

devices and the coordinator or another router in WSN.

Due to the short range characteristics of IEEE802.15.4,

the end device, located far away from the coordinator,

would need a router to relay its data. Moreover, the

router could provide regional information of the end

device which was under the router and transmitted data.

The coordinator relayed the received ZigBee packet

from WSN or the received USB packet from gateway

to the gateway or to WSN. Working as a head of the

WSN, when a ZigBee device was added to a router,

the device would inform the coordinator with an

authentication packet. After receiving and checking the

packet, the coordinator would add the information to a

routing table, and upload information to establish a

patient table in the gateway.

The gateway, a MFC program ran on PC, was

responsible for controlling, decoding, and displaying

the USB packet from a coordinator. It acted as the

WSN control panel for the operator or health agent.

2.2. Hardware Design

The hardware of our platform is consisted of a

digital signal processor (TMS320VC5509A, TI, USA),

an IEEE 802.15.4 RF chip (UZ2400, UBEC, TW), a

tri-axis accelerometer (KXP94, Kionix, USA), and a

three leads ECG sensor (1ch, BioSenseTek, TW). The

processor worked at 144 MHz, with fast computation

and is suitable for digital signal processing, such as

data compression. It utilized Harvard architecture and

supported an internal bus structure composed of one

program bus, three data read buses, two data write

buses, and additional buses dedicated to peripheral and

Direct Memory Access (DMA) activities. For analog

data acquisition, the processor provided four Analog-

to-Digital Converters (ADC) in 10-bit resolution.

UZ2400 offered a IEEE 802.15.4 based RF control,

operating in 2.4 GHz Industrial, Science, Medical

(ISM) band, with power consumption of 22 mA in

transmission state, 18 mA in receive state, and 2 �A in

sleep state.

Until the date of submission, the PCB layout of the

smaller size version of the proposed hardware is still in

progress (it would be ready by 25, Jan). The hardware

used in this paper was prototypes built on DSP starter

kit (DSK 5509A Spectrum Digital, USA). The picture

of the proposed prototype platform was showed in

Figure 3.

(A)

(B)

Figure 3: (A) Platform (B) Working Scenario: A coordinator,

a router, and an end device.

2.3. Firmware Design

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Figure 4: Firmware Architecture on Each Device

ZigBee, an ultra-low power and low data-rate

wireless personal area network standard, was proposed

based on IEEE802.15.4 medium access control (MAC)

and physical (PHY) layers in 2003 and was revised in

2006. The MAC, network (NWK), and application

service (APS) layers were implemented on every

device, providing a wireless communication feature. A

real time operation system (RTOS) (OSEck 3.2.1,

ENEA, UK) was ported on the board. It controlled the

context-switch of each layer, and managed memory

and interruptions. Figure 4 showed the firmware

architecture on each device.

2.4. ECG Compression Algorithm

Low-band coefficients subtraction was proposed to

be an adjusting component in the ECG compression

algorithm composed by DWT and ESPIHT [12]. It was

suitable for embedded systems because of its low

computational complexity and excellent performance.

The block diagram of the proposed modification was

showed in Figure 5.

Figure 5: ECG Compression Algorithm Diagram

After ECG went through the fourth level DWT, we

observed the absolute value of the approximation

coefficients were always about 10 times bigger than

the first level’s detail coefficients, and about 100 times

bigger than other levels’ detail coefficients. These

large approximation coefficients would dominant the

output bitstream, since ESPIHT gave more bits for

large number. In this case, the minimum value of the

absolute value of the sixteen approximation

coefficients was subtracted from the approximation

coefficients, and was directly passed to the packetize

procedure. Thus, the dominant wavelet approximation

coefficients would be reduced before being sent into

ESPIHT,generating shorter output bitstreams. For

reconstruction, the value was simply added back to the

approximation coefficients after depacketizing and

invers ESPIHT procedures. In addition, , if the same

CR was fixed, ESPIHT would be more sensitive on the

smaller thresholds/coefficients and would encode

much smaller changes of the ECG waveform, which

resulting in less distortion after reconstruction.

Compared without the non-modified compression

algorithm, the bitstream sent between decomposition

and reconstruction side was shorter and conserved

more channel bandwidth. For details of the DWT and

ESPIHT, please refer to [12].

3. Mathematical Model and Simulation of Transmission Time Delay

In consideration of realistic situations, we tried to

build a mathematical model and simulate the average

transmission time delay of ECG data within one

second that would be suffered by an end device which

transmits the data either in compressed ECG

transmission or in uncompressed ECG transmission.

The relation between the expected time delay and the

number of end devices in a WSN was evaluated. The

following were the assumptions before mathematical

modeling:

(1). ACK frames were out of considering for simple.

(2).The probability of successful wireless channel

allocation for each device is the same, i.e., uniform

distribution model was used as the probability density

function of each device while transmitting a frame.

3.1. Math Model of Transmission Time Delay

The mathematical modeling began at CSMA/CA,

which is used in wireless communication system to

600

avoid packet collision. For details regrding CSMA/CA,

please refer to IEEE802.15.4 spec.

The expected time delay was estimated based a

successful wireless packet transmission.. The

probability of an end device successful packet

transmission was modeled as

� �

� �

backoff period

22

transmitted successfully at a slot

5 2 12

transmitted successfully

2 0

1

2

1 11 1

11

2

BE

n BE

BEBE

BE

BEBE n

P

P p pD D

P p p

�

��

��

� �

�

� � � � � � ��

� �� �where D is the number of end devices in the WSN,

BE is the backoff exponent in CSMA/CA, and

1P

D is the probability of wireless channel

allocation for each end device. With transmitted successfully

P ,

the expected of the time delay could be modeled in a

conditional probability function as

� � � �Time delay A BE P E E�

where AE is the event that time delay occurs and BEis the event of successful transmission.

The probability of time delay event caused by

CSMA/CA random backoff period could be estimated

as

2

2

time delay event

3 11

0 42 3

27 592 3

12 284 5

5 2 2 22

2 2 1

1 1(1 )

2 2

1 1 (1 ) (1 )

2 2

1(1 )

2

BE BE

BE

BE BEn nBE BE

BE BEn nBE BE

BEBE

BE n

P

n p n p p

n p p n p p

n p p�

�

� �� �

� �� �

� ��

� � �

� � � �

� � �

� �

� �

� �

� �Thus, the expectation of the time delay could be

obtained.

� � � �

� �

2

2

5 2 2 22

2 2 1

5 2 12

2 0

Time delay

1(1 )

2

11

2

BE BE

BE

BE

A B

BEBE

BE n

BE

BEBE n

E P E E

n p p

p p

�

�

� ��

� � ��

�

� �

�

��

�

� �

� �

3.2. Simulation of Compressed and Uncompressed ECG Transmission

In Figure 6, the expected time delay,

� � � �Time delay A BE P E E� , versus the number of

end devices in a WSN, D , was evaluated. The

number D was from 1 to 30.

(A)

(B)

Figure 6: (A) Expected time delay versus the number of

devices. (B) Probability of successful transmission.

The result shown in Figure 6(A) and (B) indicated

that if an end device was to transmit a packet into a

WSN with 5 activated end devices which were

transmitting, the expected time delay due to the

CSMA/CA would be 14 time slots with a 60 %

successful transmission rate. The time slot unit is a

period of time preset in CSMA/CA. Since the

uncompressed transmission of ECG data within one

second would be separated into 3 packets, the end

device time delay would be doubled to get a complete

ECG data. Hence, the time delay in the uncompressed

transmission was twice worse than in the compressed

situation.

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4. Results and Discussions

The following results were examined using our three

prototype platforms, which took each role of end

device, router, and coordinator. The extensive, on-the-

spot deployment experiment will be conducted upon

the arrival of the new version platform (25, Jan).

4.1. Timing Performance of ECG Compression Algorithm, QRS/RR Detection Algorithm, and Posture Detection

Table I: Time Performance of ECG Compression

Input

Bytes

Output

Bytes CR

Compression

Time (ms)

Decompression

Time (�s)

51 5.02:1 8.28 240

42 6.09:1 8.12 179

36 7.11:1 8.01 133

32 8.00:1 7.92 126

28 9.14:1 7.87 114

25 10.24:1 7.8 110

21 12.19:1 7.75 99

18 14.22:1 7.7 96

16 16.00:1 7.59 94

14 18.29:1 7.61 91

256

13 19.69:1 7.60 89

Table II: Time Performance of QRS/RR Detection Algorithm

and Posture Detection

Average Time Consumption (�s)

QRS/RR Detection 972

Posture Detection 1

In Table I, the timing performance of ECG

compression, which began when arrhythmia occurs,

and decompression algorithm, were examined on

TMS320VC5509A at 144 MHz and on PC, AMD

Sempron 2800+ at 1.61 GHz respectively. Table I

showed that the time consumed in compression was

approximately 7-8 ms. Since the sampling rate of ECG

of an end device was 256 Hz, a 7-8 ms delay between

two sample periods due to compression algorithm

would result in losing 2 sample points of ECG.

However, it would not affect ECG analysis on the

gateway. Table II showed the time spent on QRS/RR

detection and posture detection.

4.2. CR versus PRD of ECG Compression

Figure 6: Comparison between the compression algorithms which

utilized lowest subband subtraction and non-utilized one

Figure 6 showed the comparison between the

compression algorithms which utilized lowest subband

subtraction and non-utilized one. CR and PRD is

formulated as

sidencompressioofbytesOutput

sidencompressioofbytesInputCR �

� � � �� �

� �%100

ˆ

ˆ

PRD1

0

2

1

0

2

��

�

�

��

�

�

�L

i

L

i

ix

ixix

where )(ix is the output bytes of the decoder in the

gateway, )(ˆ ix is the original sampled data in an end

device, and L is the length of a sample .

The comparison was done offline and evaluated

from one hundred seconds of ECG data. Fixed at a CR,

lower PRD was reached. Fixed at a PRD, higher CR

was gained. About 20-30 bits of output bits can be

omitted. According to [9], CR in 8:1 and 16:1 while

PRD at about 4-5% and 10-15% were suggested to be

clinically useful ECG information by cardiologists. In

Figure 6, CR at 8, chosen in our system, resulted in

PRD less than 2.5 %, which satisfied the criteria

mentioned in [9]. Furthermore, CR at 8 showed good

performance. The time delay was of less than 8, The

length of encoded bytes at 32 bytes was only about 1/4

maximum ZigBee packet length, while the

uncompressed ECG needed 2 and a half maximum

ZigBee packet length.

4.3. Packet Loss Tests

Packet loss tests for compressed ECG transmission

(CR set to 8) and uncompressed ECG transmission

were performed. The goal of the tests was to compare

the reliability of the two transmission types under the

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busy wireless channel, simulating the situation with

large number of nodes in WSN.

In order to create the busy wireless channel status

with the three nodes that we had, only two nodes was

used and the transmitter node transmitted a packet set

repeatedly whenever a wireless channel was freed. The

packet set in the compressed ECG transmission was a

packet of 58 bytes. In the uncompressed transmission,

2 packets of 127 bytes and a packet of 68 bytes were

transmitted. The compositions of the data sets were the

number of a second of ECG at 256Hz under two

transmission types. The data set in both scenarios were

repeated ten thousand times, i.e., 10000 packets in

compressed scenario and 30000 packets in

uncompressed scenario. The correct receiving rate and

the number of failed packets (lost packets and incorrect

content packets) were calculated and shown in Table

III. The results showed that the compressed

transmission offered more reliable quality of service

than the uncompressed transmission. when arrhythmia

occurred and the ECG was transmitted.

Table III: Result of Packet Lost Test

Scenario of Compressed ECG Transmission (CR set to 8)

Distance (meter) 1 5 9

Failure Packets

(10000 – Success Packet) 77 258 623

Success Receiving Rate

(100 – 100 x Failure Packets / 10000) 99.23 97.42

93.7

7

Scenario of Uncompressed ECG Transmission

Distance (meter) 1 5 9

Failure Packets

(10000 x 3 - Success Packet) 470 2885 4457

Success Receiving Rate

(100 – 100 x Failure Packets/(3 x10000)) 98.43 90.38 85.15

4.4. Gateway

Figure 7 showed the screen-shot of the gateway.

When one of our men pretended to suffer from

arrhythmia (narrow the threshold used in arrhythmia

criterion), the condition was detected and his ECG

signal was transmitted to the gateway. The left-bottom

window showed the ECG signal and right bottom

window showed the same operating message from a

coordinator. The top-center window showed the

structure of the WSN where ‘C-0x0000’ stands for the

coordinator whose address was 0x0000, ‘R-0x0001’

stands for a router under the coordinator whose

address was 0x0001, and ‘D-0x1234’ stands for an end

device under the router whose address was 0x1234.

The top-right window showed some characteristics of

the received ECG and its diagnosis. The top-left

window showed the man’s posture.

5. Conclusions

This paper established a prototype of a DSP

platform which enables good digital signal processing

performance in ZigBee-based WSN, and an

arrhythmia-aware telecare system.

Low-band coefficients subtraction is proposed to be

an adjusting component in the ECG compression

algorithm composed by DWT and ESPIHT [12],

gaining better performance. Fixed at a CR, lower PRD

was reached. Fixed at a PRD, higher CR was gained.

Moreover, according to [9], the PRD of our modified

ECG compression algorithm was half of the PRD

requirement, and completely satisfied the clinically

useful ECG information.

Timing performances of ECG compression,

decompression, QRS/RR detection, and posture

detection were examined. The results showed that the

time delay introduced by each algorithm could be

ignored and would not affect real-time application.

Packet lost tests were performed and compared

between the compressed transmission scenario and

uncompressed transmission scenario, while the

wireless channel was in tight status. The results

showed that the compressed transmission in WSN

could effectively increase the receiving rate.

Compared with previous works [1-6], simulated, and

tested in several indices, our system suffered less from

traffic jam its consequences. Hence, we provided a

solution to build a more bandwidth conservable, stable,

and reliable WSN-based telecare system.

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Figure 7: Gateway Screen-Shot

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