Grid-based Medical Devices for Everyday Health
Mobile Medical Monitoring
Presented by David De Roure
Overview of talk
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Partners
Scenario
Grid software
Demonstration
Current activity
Closing thoughts
Technical innovation in
physical and digital life
Henk Muller (Bristol), Matthew Chalmers (Glasgow),
Adrian Friday, Hans Gellerson (Lancaster),
Steve Benford, Tom Rodden (Nottingham),
Bill Gaver (RCA), David De Roure (Southampton),
Geraldine Fitzpatrick (Sussex), Anthony Steed (UCL)
University of Nottingham
Tom Rodden
Chris Greenhalgh
Alastair Hampshire
Jan Humble
John Crowe
Barry Hayes-Gill
Carl Barratt
Ben Palethorpe
Mark Sumner
University of Southampton
David De Roure
Don Cruickshank
University of Oxford
Lionel Tarassenko
William R. Cobern
Oliver J. Gibson
University of Lancaster
Adrian Friday
Oliver Storz
Nigel Davies
University of Glasgow
Matthew Chalmers
University of Bristol
Henk Muller
Chris Setchell
Scenario
• Patients are remotely monitored using a series of
small mobile and wearable devices constructed
from an arrangement of existing sensors
• Information collected from these remote devices is
made available using Grid technology
• Medical professionals have tools to analyse online medical information and are able to access
these through remote interfaces.
Grid Research Agenda
• Making remote data available to the Grid in order
that a wider scientific community can access
scientific data as quickly as possible, often across
variable bandwidth communication services
• Making Grid facilities available to remote users
when these need to be delivered across lower
bandwidth communication using devices with
significant display and processor limitations
The Maturing eScience “Grid”
1998
Broadening Computation
Research
Focus
Additional
Challenges
New
Uses
New
Scientists
Resources
Security
Management
Modelling
Simulation
2001
2003
Knowledge
Information
Sensors
Mobility
Semantic
Modelling
Access
Structure
Metadata
Knowledge
Discovery and
Recording
Physics
Pharmacy
Medical
Chemistry
Astronomy
BioInformatics
Engineering
2005
Devices
Activity
Ubiquitous
Autonomic
Behaviour
Remote
Sensing
Remote
Access
Architectures
(e.g P2P,
Ad-hoc networs)
Environmental
Monitoring
Capturing
Activity and
Process
Activity and
Lab
Monitoring
Field Scientists
Environmental
Scientists
“Wet” Lab Scientists
MIAS - Devices
• Exploring the development of mobile medical
technologies that can be remotely connected onto
a distributed grid infrastructure
– Continuous monitoring of multiple signals via
wearable devices
– Periodic monitoring using Java phones and blood
glucose measures
• All signals available to a broad community and
can be processed using standard Grid Services
Java Phone
+
Blood Monitor
Standard
Grid
Service for
feature detection
Proxy
Buffers Material
for sending on
Grid
based
Storage
Services
Patients
Visualisation Services
Proxy
Wearable
Devices
Converts Signals
to database record
Asynchronous
Mobile World
Display
Clinicians
Grid Services
Wearable Device
Sensor bus
•
•
•
•
Easy Plug and Play of Sensors
Wireless connection using 802.11
Positioning information from GPS
Nine wire sensor bus running
through wearable to allow new
sensors
GPS aerial
Range of different sensors
• ECG
• Oxygen saturation
• Body movement
– Accelerometers
– GPS
• All plug and play to
standard bus
• Changes reported to the
underlying infrastructure
Blood Glucose Monitoring
• Exploring medical
devices that rely on
self-reporting
• Extends web based
system developed by
Oxford University and
e-San Ltd
• Off-the-shelf GPRS
(General Packet Radio
Service) mobile phone
• Blood Glucose meter
Self Reporting
• Patient takes measurement
• Measurement sent via
mobile phone to remote
infrastructure
• Series of lifestyle questions
asked as part of the clinical
trial
• Users promoted for
compliance.
• Current trial involves 100+
patients
Deploying on the Grid
Register new
device
Generic device proxy
factory(s)
Blood sugar
meter
PAR sensor
JavaPhone proxy
JavaPhone
Data logger
Iridium
Other sensors
ECG sensor
accelerometer
802.11
S/w module
S
Data logger
proxy
D
PAR sensor
proxy
S
Multicast
beacon
9-wire bus
(pluggable)
Device Proxy
Management Client
Device
S
Sensor
Trial
manager
Add sensor to
trial database
Other sensor
proxy
S
Cyberjacket
proxy
D
ECG sensor
proxy
S
S
Other sensor
proxy
New GRID Port Types:
D
D
Blood sugar
meter proxy
GPS receiver
DF
DeviceProx yFactory
GPRS
PAR sensor
Cyberjacket
(Bitsy)
DF
New device
configuration
S
Sensor
data-pump
Sensor
data-pump
Sensor
data-pump
Sensor
Database
Service
RDBMS
Live monitoring display
Sensor and
device status
display
GPS live map
Scrolling
sensor charts
Ōelipse of
normalityÕ
visualisation
Dataflow user interface
Data
chooser/
fetcher
Table views
Graph views
Putting devices on the Grid
• Make devices and sensors available as if they were
first class Grid Services
• Two new application-independent port types:
– a generic sensor,
– a generic device (assumed to host a number of sensors)
• Currently our devices require a proxy to match
between these definitions and the sensor
• Project was an early GT3 adopter for prototype
– Grid Service model worked
– concerns about security
Sensor port type: self-description
Name
#
Modify
?False
Description
1
Mutabilit
yConstant
IdentifiedAs
Description
1
Mutable
False
Expanded description, e.g. placement, accuracy, etc.
MeasurementTemplate
1
Constant
False
The format in which measurements are reported
MeasurementDiscardPolicyExtensibility
MeasurementPublicationPolicyExtensibility
ConfigurationExtensibility
1..
*
1..
*
1..
*
1
Constant
False
Constant
False
Constant
False
Acceptable XML schema types for the measurementDiscardPolicy
SDE
Acceptable XML Schema types for the measurementPublishingPolicy
SDE
Acceptable XML Schema types for sensor configuration SDE
Mutable
False
Current status, e.g. in contact with proxy or disconnected
ProxyStatus
Sensor ID, names and type
Sensor port type: Externally modifiable configuration
Name
#
Mutabili
ty
Modify
?
Description
MeasurementDiscardPolicy
1
Mutable
True
The conditions under which the sensor should discard
historical measurements
MeasurementPublishin
gPolicy
configuration
1
Mutable
True
The conditions under which the sensor (proxy) should
make a new measurement public
0..
*
Mutable
True
Sensor-specific configuration information, e.g. sample
rate
Sensor port type: measurement
Name
#
Mutability
Modify
?
Description
Measurement
1
Mutable
False
The most recent measurement made by the sensor
MeasurementCounter
1
Mutable
False
A running counter of measurements made
MeasurementHistory
1
Mutable
False
The complete known history of measurements
Demo at All Hands Meeting in Nottingham, 2003
Related activities
Advanced Grid Interfaces for Environmental e-Science
in the Lab and in the Field
• The Antarctic Lake Carbon
Cycling project
• The Urban Pollution
Monitoring Project
See demonstrations
or www.equator.ac.uk
Live clinical record
• Readings appear as a live database
• Standard queries and interfaces can be used to
manipulate the data
• On-line services used to process the data
• Exploits existing grid standards for reliability
• Presents a range of different interfaces for
clinicians
• Provides range of feedback to patients.
Portal for Information Access
• Interactive access to live and stored information
(e.g. visualised, excel) collected from wearable
devices
– For use by clinicians
– Could be used by patients
– Also needed by “pervasive support desk”
• Accessible via pervasive devices, e.g. phone
• Based on spatial model
Patient
Proxy of
Mobile
clinician
Location ontology
is-adjacent-to
is-part-of
AbstractSpace
is-owned-by
akt:Organisation
has-usual-occupant
subClassOf
permits-access-to
akt:Person
is-part-of
PartitionedSpace
EnclosedSpace
permits-access-to
akt:PostalAddress
subClassOf
subClassOf
has-postal-address
Building
is-part-of
FloorTraversingSpace
subClassOf
is-part-of
subClassOf
subClassOf
Floor
is-part-of
subClassOf
subClassOf
Corridor
is-part-of
Stairs
Lift
Room
Ian Millard
Semantic
Pervasive
Grid
Fundamentally
about
Interoperability
and inference
Grid and Pervasive share issues in
large scale distributed systems.
e.g. service description, discovery,
composition; autonomic computing.
These can be aided with semantics.
Pervasive applications
need the Grid,
e.g. Sensor Networks
Grid applications need
Pervasive Computing
e.g. Smart Laboratory
http://ubigrid.lancs.ac.uk/
Conclusion
• We have demonstrated the collection of medical
and contextual data from wearable devices using
Grid infrastructure
• We have demonstrated a means of access to that
data by a variety of users including use of
pervasive devices
• We have provided an illustration of the important
relationship between Grid and Pervasive
computing
www.equator.ac.uk