Robot Control - ICMCC

Optimisation Issues of High Throughput
Medical Data and Video Streaming Traffic
in 3G Wireless Environments
R. S. H. ISTAPANIAN* and N. PHILIP*
* Mobile Information and Network Technologies Research Centre,
Kingston University, London
Kingston upon Thames, KT1 2EE, UK
r.istepanian@kingston.ac.uk.

M- Health : Emerging Mobile Communications
and Network Technologies for healthcare
Istepanian (etal.) , IEEE Trans. Information Technologies in Biomedicine,
Vol. 8, 4, DEc. 2004.

Introduction:
• OTELO system Medical and non medical data.
• still Images
• Robot Control
• Medical video streaming
• video conference
• 3G QoS for Ultrasound requirements.
• Soft Computing and Optimization issues in 3G – medical QoS
(m-QoS) will be discussed here and especially Multiobjective
optimization methodology based on Adaptive Simulated
Annealing (ASA).

OTELO System – Introduction
EU-IST Funded Project (2001-2004)

Expert station - Input Device
To Control the Patient station
robot:
• One DoF hand free Input device
• Easy to use, comparable to
standard probe
• 6DOF localisation sensor - Flock of
Bird TM

Patient Station
Robot Controller Unit
Videoconference
System
Robotic Head &
US Probe Holder
Ultrasound
Machine

EXPERT & PATIENT Stations

Ultrasound Holder (Robotic Arm)

OTELO Medical & Non-Medical Data Requirements
Expert Station
• Video-conference
3G Uplink
• Robot Control
• Video-conference
• Robot Control
• Force feedback
• Dynamic US images
• Still US Images
3G Downlink
• Fictive Probe force
Patient station

Table 1. OTELO medical data requirements and corresponding data rates.
Ultrasound video
stream
Ultrasound still
images
Ambient video
stream
Voice
Robot control
data
Flow direction
Simplex:
Patient to Expert
Simplex:
Patient to Expert
Duplex Duplex duplex
Transport
Protocol
Speed
Requirement
RTP/UDP/IP TCP/IP RTP/ UDP/IP RTP/UDP/IP UDP/IP
Real-time Non Real-time Real-time Real-time Real-time
Payload data
rate
requirement
over the airinterface
(without protocol
headers)
15 frames/s
@ 210 kbit/s
Uplink
1 frame/ 10s
Uplink
15 to 1 frame/s
symmetrically
16 kbit/s
symmetrically
0.3 kbit/s
symmetrically
The detailed medical and non-medical OTELO data traffic are shown in Table 1.
From this table it can be seen that the most traffic and bandwidth demanding
OTELO traffic type is the medical video streaming traffic of the system.

OTELO Functional Modalities and Data Follow
Medical Data
Still US Images
Stream US
Images
Stream US
Images
Robotic Arm control
Frequency
Medium Qlty.
Videoconf
High Qlty.
Videoconf.
Data
Description
Gray Scale,
512x512 Pixel
Gray Scale,
CIF (Regon Of Interest),
200x200 Pixel
Gray Scale,
CIF, 352x288 Pixel
Data Rates
&
Resolution
14-97
Kbyte
Data Flow
Direction
Up-link
P-to-E
10 fps Up-link
P-to-E
7 fps Up-link
P-to-E
100-200 Hz 3-4 Kbps UP or Down
Link, E-to-P, P-to-E
QCIF
176x144
QCIF
176x144
CIF
352x288
5 fps Up & Down
P-to-E, E-to-P
7.5 fps Up & Down
P-to-E, E-to-P
5 fps Up & Down
P-to-E, E-to-P

Transmitted image
Received image
2 cm hyperechoic focal lesion in
deeper part of the right liver

Therefore we should implement ‘some control mechanism’ that will
adhere within these bounds:
1- OTELO’s user (QoS) requirement
2- the Network QoS requirements
From the Clinical Trials of the system and the experts
evaluation of the diagnostic quality of the received US
image and stream, that the communication bounds
for efficient diagnosis using the OTELO system are:
1. Frame rate of the received images >= 5fps ( kbps)
2. Peak Signal to Noise Ratio (PSNR) >= 36dB
3. The average delay of Probe movement-to-Received
image at the Patient station is < 350 ms
These are called Medical ‘user QoS’ requirements

3G OTELO Performance analysis studies :
1. Evaluate the performance of the OTELO Robotic system
over UMTS network.
2. To study the effect of multiplexed data transmission on
the Real-time performance.
3. Subjective and objective evaluation of the received data
Garawi, (etal.) , ‘ Performance Analysis of a Compact Robotic Tele-Echography
E-Health System over Terrestrial and Mobile CommunicationLinks’, Proc.
5th. IEE International Conference on 3G Mobile Communication
Technologies- 3G 2004, London, 18-20, October, pp.118-122, 2004.

OTELO System on UMTS network
GGSN
SGSN
UTRAN
3G
Core Network
GMSC
PSTN
ISDN or ADSL
RNC
Node B
Node B
- Ambient Video
- Voice
- Robot Control
Downlink:
3G mobile handset
(or PCMCIA card)
(Patient) Mobile
OTELO Station
GateAway
Robot Control
Unit
Modem
Uplink:
- US video stream
or Ambient Video
- US still images
- Voice
- Robot Control
Probe Holder
Robot
Robotic
Control Input
Device
Audio-Video
Device
Expert Station
Audio-Video
Device
Echograph
Device

Ultrasound data stream throughput
Intantaneous recieved throuhput Vs time
Average total
throughput:
19.7 Kbps
Average US video
stream throughput:
12.7 Kbps
Throughput (Kbps)
100
90
80
70
60
50
40
30
20
10
0
Robotic
Data
Robotic Data +
US stream
Average throughput
0 20 40 60 80 100 120 140
Time (s)
Total throughput for the control & ultrasound data received
at the Expert station, UMTS Uplink Data rate = 64Kbps.

Supporting robust video communications over 3G wireless
networks is a significant problem, primarily because of two
factors:
• Low bandwidth.
• Time varying error characteristics of the transmission channel.
This will cause congestion and then degradation in the network
performance:
• utilization
• packet losses
• higher delay
• delay jitter
These are called generally 3G Network QoS.

Adaptive simulated Annealing in the medical 3G network optimization
Initialization (algorithm parameters)
Generate a new
frame rate
Accept or reject
no
accept
yes
Adapt the next
walk to the topography
of the required 3G m-QoS
Reduce temperature
No
3G m-QoS
condition
satisfied
Yes
End

The proposed Optimization Technique:
In this work we introduce Adaptive Simulated Annealing (ASA)
Optimization technique and apply it for this multiobjective
optimization problem. In particular we consider the packet
loss and delay Jitter objectives function (Фi), therefore the
algebraic inequality here are:
Packet loss
Delay Jitter
φ
φ
r ) 1
( ≤ ε
r)
2
( ≤ ε
1
2
ε 1 ε 2
, are the limits of these inequalities.

The proposed Optimization Technique:
These can be combined as one equation that the optimization
techniques will work on to find the optimum rate.
⎧ ⎧φi(
r)
−εi
⎫ ⎫
E( r)
= max⎨max⎨
,0⎬
: i = 1, 2⎬
⎩ ⎩ ωi
⎭ ⎭
Ф is the objective function,
i is the number of QoS metrics elements,
r is the rate control value,
ω is the weightings.

End-to-End System Architecture

Conclusions and future work:
(This is ongoing research)
• The OTELO ultrasound stream is the most traffic demanding .
• New QoS metrics for medical applications
• Multiobjective optimization technique base (ASA) is considered
to find the optimal frame rate that satisfy the QoS of the
received images.
• Network monitoring is used here to feedback the packet loss
and delay jitter using the RTCP receiver report.
• Packet Loss and delay jitter is considered here as our objective
functions. PSNR will be added later to the optimisation problem

Acknowledgments:
EU-IST for funding the OTELO project
Vodafone R&D, UK

THANK YOU
Robert S. H. Istepanian
r.istepanian@kingston.ac.uk
www.kingston.ac.uk/MINT