video pertekom

2.
-Komunikasi virtual, dipahami sebagai virtual reality pada ruang lingkup (alam maya) dengan menggunakan internet. Komunikasi virtual sebenarnya dilakukan dengan cara representasi informasi digital yang bersifat diskrit.
A Network-based System Architecture for
Remote Medical Applications

- Remote Medical Network (http://web.it.kth.se/~axel/papers/2007/APAN-HuiminShe.pdf)
Huimin She1, 2
1Dept. of Electronic, Computer and Software
Systems, Royal Institute of Technology, Sweden
2ASIC & System State Key Lab., Dept. of
Microelectronics, Fudan Univ., Shanghai, China
Tel: +46-8-790-4247
Zhonghai Lu
Dept. of Electronic, Computer and Software
Systems, Royal Institute of Technology, Sweden
Tel: +46-8-790-4110
zhonghai@kth.se
huimin@kth.se
Axel Jantsch, Li-Rong Zheng
Dept. of Electronic, Computer and Software
Systems, Royal Institute of Technology, Sweden
Tel: +46-8-790-4124, +46-8-790-4104
{axel, lrzheng}@kth.se
Dian Zhou
ASIC & System State Key Lab., Dept. of
Microelectronics, Fudan Univ., Shanghai, China
Tel: +86-21-5135-5286
zhoud@fudan.edu.cn

ABSTRACT
Nowadays, the evolution of wireless communication and network
technologies enables remote medical services to be available
everywhere in the world. In this paper, a network-based system
architecture adopting wireless personal area network (WPAN)
protocol IEEE 802.15.4/Zigbee standard and 3G communication
networks for remote medical applications is proposed. In the
proposed system, the number and type of medical sensors are
scalable depending on individual needs. This feature allows the
system to be flexibly applied in several medical applications.
Furthermore, a differentiated service using priority scheduling and
data compression is introduced. This scheme can not only reduce
transmission delay for critical physiological signals and enhance
bandwidth utilization at the same time, but also decrease power
consumption of the hand-held personal server which uses battery
as the energy source.

Categories and Subject Descriptors
J.3 [Computer applications]: Life and Medical Sciences –
medical information systems

General Terms
Design, Performance

Keywords
System architecture, Sensor network, Remote medical
applications

1. INTRODUCTION
In medical applications, collecting patient physiological
information timely is crucial for clinicians to make treatment
advices in time, which is of large importance for saving lives and
ensuring patient’s safety. The development of wireless
communication and network technologies has made a significant
impact on remote medical applications during last few years [9]. It
makes remote health care at home or in the hospital practically
feasible and comfortable. Although face-to-face communication
between a patient and a clinician can not be replaced, there are
efficient and flexible ways to provide remote medical care by
adopting wireless telemedicine which has many advantages.
Firstly, clinicians can read patients’ physiological parameters in
time and then give real-time diagnosis advices which are
important to patients’ recovery. Secondly, patients can measure
their physiological signals and then send them to the hospital
remotely without the necessity to go to the hospital. Thirdly,
patients can move around freely while carrying wireless hand-held
medical devices. And finally, with the help of this system, a
clinician can take care of a few patients simultaneously, and thus
the personnel expense will be reduced.
In traditional approaches, remote medical services are
implemented over wired communication technologies like the
Integrated Services Digital Network (ISDN) [5] [7]. Most current
telemedicine applications are limited to communications between
fixed locations with conventional handsets. These heavy medical
devices will prevent the patients from moving around freely. In
[3], some ongoing and emerging applications of wireless
information technology in health care are investigated. With the
development of mobile communication technologies, such as
GSM, GPRS, especially 3G networks, wireless medical service
can be delivered to any locations flexibly. In recent years, there
are many new applications in health provision using mobile
technology [1] [2] [10]. 3G communication network provides a
broadband, packet-based transmission of text, digitized voice,
video, and multimedia at data rates up to 2 Mbps. It offers a
consistent set of services to mobile computer and phone users no
Copyright is held by the author/owner(s)
Asia Pacific Advanced Network 2007, 27-31 August 2007, Xi’an,
People’s Republic of China.
Network Research Workshop, 27 August 2007, Xi’an, People’s Republic
of China.
matter where they are located in the world. In [14], a portable
teletrauma system using commercially available 3G wireless
cellular data services is introduced. However, they did not
mention the communication between medical sensors and the
trauma-patient unit.
In this paper, a network-based system architecture for remote
medical applications using low power IEEE 802.15.4/Zigbee
standard and commercially available 3G networks is proposed. In
our proposed system, the number and type of medical sensors are
scalable depending on individual requirements. This feature
allows this system to be flexibly applied to a wide range of
medical applications such as continuous home monitoring and inhospital
health care. Moreover, a differentiated service using data
compression and priority scheduling is introduced. This scheme
can reduce transmission latency for critical physiological signals
and decrease power consumption of the hand-held personal server
which uses battery as the power source.
The rest of this paper is organized as follows: In section II, the
system architectures including medical sensors, the personal
server and the differentiated service are presented. In section III,
an example use case is discussed. Finally, conclusions are made in
section IV.


2. SYSTEM ARCHITECTURE

2.1 Overview of system architecture
The whole system architecture is shown in figure 1. It is
composed of medical sensor nodes, a hand-held personal server, a
hospital server and related services. In this system, medical sensor
nodes are used to collect physiological signals including biosignals,
medical images, and voice signals. These obtained signals
are fed into the personal server through wireless personal area
network (WPAN). The wireless communication between the
sensor nodes and the hand-held personal server uses IEEE
820.15.4/Zigbee standard. Then the hand-held personal server
processes the data and displays the results on its LCD screen. And
the data can be stored in a local memory for self recording. If
necessary or required, the data can be transmitted to the hospital
server via 3G communication networks. With the availability of
3G networks, digitalized data and voice can be transmitted
simultaneously. After arriving at the hospital server, the data are
either stored in the clinical data base, or available to a clinician
through a hospital’s local area network (LAN). Then clinicians
can analyze the physiological data and give diagnosis advices
accordingly. Alternatively, when a clinician is away from the
hospital, he/she still can get the data via a PDA and give diagnosis
advices to the patient remotely.

Figure 1. The system architecture
In this system, the number and type of medical sensor nodes to
build the local personal network are variable depending on
individual’s needs. This feature makes the system flexible with a
lot of medical applications such as remote health care, home
monitoring, disaster and emergency monitoring. Furthermore, this
system provides convenience for patients as well as for clinicians.
For patients, they can get medical service at home or any other
places they prefer. And they can move around freely while
carrying light hand-held medical device. For clinicians, they can
give diagnosis suggestions to patients remotely without the
necessity to go to the hospital if nothing emergency happens. In
the following three sub-sections, more detailed descriptions about
medical sensors, the hand-held personal server, and the
differentiated service will be presented.
2.2 Medical sensors and wireless personal
area network
The main tasks of the medical sensors are to collect physiological
signals and send them to the personal server. Typical medical
sensors and characteristics of the signals are shown in table 1 [13].
In this system, the type and number of medical sensors are
scalable depending on applications. Several commonly used
medical sensors are briefly introduced as follows:
1) Electrocardiography (ECG) is the most widely used
technique for cardiac disease diagnosing. The researchers in
Harvard University have developed sensor boards for both
the Mica2/MicaZ and Telos mote platforms that provide
continuous ECG monitoring by measuring the differential
across a single pair of electrodes [12].
2) Electroencephalograph (EEG) is the neurophysiologic
measurement of the electrical activity of the brain by
recording from electrodes placed on the scalp. It is capable of
detecting changes in electrical activity in the brain on a
millisecond-level.
3) Electrooculography (EOG) is a technique for measuring the
resting potential of the retina. The resulting signal is called
the electrooculogram. The main applications are in
ophthalmological diagnosis and in recording eye movements.
4) Electromyogram (EMG) is a medical technique for
evaluating and recording physiologic properties of muscles at
rest and while contracting.

Table 1. Characteristics of biomedical signals

Signal Frequency

Range Signal Range
Electrocardiograph
(ECG) 0.05~100 Hz 0.01~5 mV
Electroencephalograph
(EEG) 0.5~60 Hz 15~100 mV
Electrooculogram
(EOG) 0.5~50 Hz N/A
Electromyogram
(EMG) 0.5~60 Hz N/A
Heart Rate 45~200 beats/min N/A
Breathing Rate 12~40 breaths/min N/A
Blood pressure dc-60 Hz 40~300mmHg
Depending on the characteristic of digitized physiological signals,
a low data rate, short range and low power protocol is appropriate
for the data transmission between medical sensors and the
personal server. The IEEE 802.15.4/Zigbee standard is adopted in
this system. The IEEE Standard 802.15.4 describes a very low rate
wireless technology that is designed for communication among
wireless devices within a short range, using very low power and
with low data rate requirements [11]. In [6], IEEE 802.15.4
standard is utilized for medical sensor body area networking. And
the performance of this protocol is analyzed. The simulation
results show that IEEE Std. 802.15.4 can be used for medical
sensor networking with low data rate asymmetric traffic when
properly configured.
In the proposed system, various sampling rates and quantization
levels are used when the biomedical signals are digitized before
sent to the hospital server. Taking ECG as an example, a relatively
low sampling frequency of 128 Hz is appropriate for a good
representation of ECG signals, while a sampling rate of 250Hz
with 16-bit resolution has been used in ECG characterization
processing. From table 1, we can see that ECG generates the
highest data rate among the patient’s vital signals, which is about
10 kB/s. Then the low data rate wireless technology IEEE
802.15.4/Zigbee standard, which supports data rate of 250 kbit/s
at 2.4GHz frequency band, can be adopted for communication
between medical sensors and the personal server.

2.3 The personal server
Previous descriptions show that the personal server plays an
important role in overall telemedicine system. It is designed as a
hand-held unit which can be used to communicate parallelly with
a series of scalable medical sensor nodes as well as a remote
hospital server. It maintains a communication bridge between
patients and the hospital. Medical sensors start to collect data
(such as ECG) after getting the command from the personal server
and then send it to the personal server via wireless personal area
network (WPAN). Results (e.g. body temperature or blood
pressure) can be displayed on LCD screen of the personal server.
And data may be sent to the remote hospital server for further
processing if necessary. In general, the personal server performs
the following tasks: 1) Initialization and configuration of medical
sensor nodes. 2) Collecting data from medical sensors. 3)
Processing physiological data and displaying results. 4) Keeping
reliable communication with remote hospital server. 5) Providing
a graphic user interface. 6) Providing voice communication
between patients and physicians.
The diagram of the personal server is shown in Figure 2. The main
components of the personal server are listed as follows:
1) Processor & Memory module: The processor manages the
connections and data flow among all modules. It also takes
charge of initialization and configuration of connected
medical sensor nodes.
2) User Interface: The LCD screen is used for showing
measurement results (e.g. body temperature) and the
keyboard is used to input request from patient. For example,
for heart disease patients, an ECG measurement or blood
pressure testing can be taken if required.
3) Communication module: This module consists of two submodules—
a data transceiver and a Zigbee module, which
respectively manage communicating with the hospital server
and medical sensor nodes. The data transceiver sub-module
is used to transmit data to the hospital server as well as get
command from it. The Zigbee sub-module is used to
communicate with medical sensor nodes which require a low
data rate and short range communication link. To reduce
power consumption, in this design, IEEE 802.15.4/Zigbee
standard is adopted for the communication between medical
sensor nodes and the hand-held personal server.

Figure 2. Diagram of the personal server
4) Bio-signal Analyzer: The main tasks of the personal server
are to collect and process physiological data from medical
sensor nodes. Bio-signal analyzer module is used to analyze
bio-signals and performs parameter extraction under the
remote clinician’s request. For example, among the patient’s
vital signals, ECG generates the highest data rate, which is
about 10 kB/s. R-interval analysis can be performed to
determine the peaks through setting the threshold and first
derivative for a standard peak function. By transmitting
certain R-intervals instead of the whole ECG waveform, the
data rate can be lowered and power consumption can be
reduced subsequently.
5) Speech Recognition: This module is used to record voice
signals and sounds from the patient especially during
sleeping-time. When there are abnormal snoring sounds,
alarms will be made to inform the care giver or wake up the
patient himself/herself.
6) Alarm Maker: If one of the physiological signals exceeds the
threshold that is pre-set, this module will make alarms to
inform the clinician or a care giver. Then the patient will get
corresponding treatment in time.
7) Voice Module: This module is used to provide voice
communication between the hand-held personal server and
the hospital. Conversations can be started by either side.
With the help of this module, the patient can communicate
with the physician more directly and effectively.
8) Power Supply: This module is used to provide energy for
other modules.

2.4 Differentiated services
Among patients who had heart attacks, about 30% of them died
even before reaching the hospital [8]. Although heart attack can
happen suddenly without apparent indications, if correct
instructions can be made immediately, then mortality can be
reduced. So providing timely access to patient information is
crucial for saving lives and ensuring patients’ safety. Therefore,
providing guaranteed service and reducing transmission latency
for critical physiological signals is of great importance for lifethreatening
medical applications. On the other hand, since the
personal server is powered by battery, power consumption has
great impact on the efficiency of wireless personal area network
(WPAN) and prolonging the working time of the personal server.
As all know, reducing the transmission period will improve
overall bandwidth utilization as well as decrease power
consumption. In order to reduce transmission delay for critical
physiological signals, improve overall bandwidth utilization and
reduce power consumption, a differentiated service based on two
schemes --- priority scheduling and data compression --- is
proposed.
A. Priority scheduling & data compression
Depending on the characteristics of different physiological signals,
the traffic from medical sensors is divided into four types
according to their data rates and latency requirements. The four
types of traffic are: 1) high data-rate and low latency traffic; 2)
low data-rate and low latency traffic; 3) low data-rate and high
latency traffic; 4) high-data rate and high latency traffic. Low
latency means that the signal is critical, and its transmission delay
should be as short as possible. Each type of traffic is assigned a
priority weight which implies its transmission order when there
are several types of physiological signals to be sent. In table 2, an
example of priorities for different traffic types is shown. However,
the ‘high’ and ‘low’ defined here are relative. And the priority
weight can be assigned dynamically during the initialization
process of the personal server according to a specific application.
For example, when monitoring heart disease patients, ECG has the
highest priority; while monitoring head disease patients, EEG has
the highest priority and so on. For high data-rate and high latency
signal (such as medical image), it will be compressed according to
a given ratio and stored in local memory until its deadline expired.
And for other signals, they will be sent out immediately according
to their priority orders.

Table 2. Priorities for different traffic types
Data Type Data rate Latency Priority
ECG High Low 1
EEG, EOG, EMG Low Low 2
Heart rate &
Blood pressure &
Body temperature
Low High 3
Medical Image High High 4
B. The differentiated service
A flowchart of the differentiated service is shown in figure 3. The
personal server has two working modes, which are inactive mode
and active mode. When there is no workload, the personal server
will turn into inactive mode to save energy. And if there is
workload, the personal server wakes up from inactive mode and is
ready for transmission. If the physiological signals are critical,
they will be sent to the hospital server according to their priority
orders. From previous definitions, we know that physiological
signals with low latency requirement are critical signals and others
are non-critical signals. For non-critical physiological signals,
they will be compressed according to a given compression ratio
and then stored in local memory. If there is no other data to send,
non-critical physiological signals will be sent to the hospital
immediately. Otherwise, they will not be sent to the hospital
server until their deadlines expired.

Figure 3. Differentiated service flow
For life-threatening medical applications, timely access to the
patient’s physiological information is crucial for providing correct
treatment in time and improving the overall safety of the patient’s
care. By providing distinguishing services for different
physiological signals, the priority scheduling scheme not only
reduces the transmission delay for critical physiological signals,
but also decreases the probability of traffic congestion. Thus the
overall quality of service (QoS) is improved. The number of sent
packets is reduced by adopting the data compression scheme.
Therefore the bandwidth utilization is improved and the total
transmission time is reduced. Since the communication module of
the personal server consumes a big proportion of the whole energy,
thus the energy can be reduced when the total transmission time is
shortened. In a word, by using the differentiated service, the
transmission delay of critical physiological signals is reduced and
bandwidth utilization is enhanced at the same time. Moreover, the
power consumption is reduced.

3. AN EXAMPLE USE CASE
With the development of wireless technologies, telemedicine has
become practically feasible and increasingly popular. Health
telematics applications enable the availability of prompt and
professional medical care at understaffed areas like rural health
centers, ambulance vehicles, trains, ships and patient home
monitoring. With the help of wireless personal area sensor
network, complete home patient monitoring becomes
technologically feasible and comfortable (figure 4[4]). Moreover,
with this telemedicine system, in-hospital health caring will
become more convenient. Physicians and nurses do not need to
always stay with patients. They can read and analyze patients’
physiological data via telemedicine system and then give
diagnosing advice remotely. And staff expense will be reduced
subsequently. In this section, an example of patient home
monitoring is discussed.
The picture of a telemedicine system for patient home monitoring
is shown in figure 4, it is an example taken from [4]. It consists of
several medical sensors put on the patient’s body, a hand-held
personal server, a remote hospital server and related services. The
medical sensors which can measure ECG, SpO2, body temperature,
and blood pressure independently. The sensors and the hand-held
personal server form a local personal area network which uses
short range, low power protocol IEEE 802.15.4/Zigbee standard.
This local personal area network is scalable depending on the
medical applications and the number of physiological sensors
involved. And the communication between local personal server
and remote hospital server uses commercially available 3G
communication networks.

Figure 4. A telemedicine system[4]
The whole system works as follows. At first, a physician or nursecontrolled
remote hospital server determines when a new
measurement is needed, and then it gives commands to the local
hand-held personal server via 3G networks. After receiving the
commands, the personal server starts to initialize and configure
the medical sensors. And then a wireless personal area network
(WPAN) is formed automatically. According to the commands
from the hospital server, each type of physiological signal is
assigned a priority weight which indicates its critical level. And
the priority weight can be assigned dynamically depending on the
application. (For example, a larger priority weight will be
assigned to ECG signals than body temperature for heart disease
patient. Then ECG signals will be processed and sent earlier than
body temperature if both of them arrived at the personal at the
same time). For seriously-sick patients, a threshold of
corresponding physiological signal can be pre-set. If the signal
exceeds the threshold, the local personal server will generate
alarm to inform a care giver or the patient himself. This
mechanism improves the safety of patients and reduces staff
expense at the same time. The local personal server works in two
modes --- active mode and inactive mode. When there is workload,
it will wake up from the inactive mode to the active mode. The
physiological sensors either automatically or manually triggered
to collect required data. The measured physiological signals are
transmitted to the personal server via a wireless personal area
network (WPAN). The personal server will process and store the
data in local storage for self recording. If required, the signals will
be transmitted to the remote hospital server at different orders
according to their priorities. After arriving at the hospital server,
these data will be analyzed by the physician. And then treatment
advices will be given or corresponding measures can be taken.

4. CONCLUSION
A network-based system architecture for remote medical
applications is introduced in this paper. By using IEEE
802.15.4/Zigbee standard and commercially available 3G
networks, this system can be used either at home for continuous
monitoring or in hospital for health care with strong scalability
and flexibility. According to different emergency levels of
physiological signal, a differentiated service based on priority
scheduling and data compression is presented. The proposed
scheme not only greatly reduces transmission delay for critical
physiological signals and enhances bandwidth utilization at the
same time, but also reduces power consumption of the hand-held
personal server. This mechanism improves quality of service (QoS)
of the overall system which is very important for life-critical
medical applications. The future work is to build experiment
environment based on the proposed system architecture.

5. ACKNOWLEDGEMENT
We would like to thank Liping Wang at National Institute of
Informatics (NII), Tokyo, Japan, for the fruitful discussions. We
also thank the anonymous reviewers for their valuable comments.

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3.
Robart III adalah demonstrasi untuk perlindungan untuk ukuran respon.
ROBART III is an advanced demonstration platform for non-lethal security response measures, incorporating reflexive teleoperated control concepts developed on the earlier ROBART II system. The addition of threat-response capability to the detection and assessment features developed on previous systems (ROBART I and ROBART II) has been motivated by increased military interest in Law Enforcement and Operations Other Than War.
Like the MDARS robotic security system being developed at NCCOSC RDTE DIV (the Navy's Command Control and Communications center in San Diego, called NRaD for short), ROBART III will be capable of autonomously navigating in semi-structured environments such as office buildings and warehouses. Reflexive teleoperation mode employs the vehicle's extensive onboard sensor suite to prevent collisions with obstacles when the human operator assumes control and remotely drives the vehicle to investigate a situation of interest.
The non-lethal-response weapon incorporated in the ROBART III system is a pneumatically-powered dart gun capable of firing a variety of 3/16-inch-diameter projectiles, including tranquilizer darts. A Gatling-gun style rotating barrel arrangement allows six shots with minimal mechanical complexity. All six darts can be fired individually or in rapid succession, and a visible-red laser sight is provided to facilitate manual operation under joystick control using video relayed to the operator from the robot's head-mounted camera.
This paper presents a general description of the overall ROBART III system, with focus on sensor-assisted reflexive teleoperation of both navigation and weapon firing, and various issues related to non-lethal response capabilities.