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Grid-based Medical Devices for Everyday Health. Mobile Medical Monitoring Presented by David De Roure. Overview of talk. Partners Scenario Grid software Demonstration Current activity Closing thoughts. Technical innovation in physical and digital life.

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Mobile medical monitoring presented by david de roure

Grid-based Medical Devices for Everyday Health

Mobile Medical MonitoringPresented by David De Roure


Overview of talk
Overview of talk

  • Partners

  • Scenario

  • Grid software

  • Demonstration

  • Current activity

  • Closing thoughts


Technical innovation in physical and digital life

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)


Mobile medical monitoring presented by david de roure

University of SouthamptonDavid De RoureDon Cruickshank

University of GlasgowMatthew Chalmers

University of BristolHenk MullerChris Setchell

University of LancasterAdrian FridayOliver StorzNigel Davies

University of NottinghamTom RoddenChris GreenhalghAlastair HampshireJan HumbleJohn CroweBarry Hayes-GillCarl BarrattBen PalethorpeMark Sumner

University of OxfordLionel TarassenkoWilliam R. CobernOliver J. Gibson


Scenario
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 on-line medical information and are able to access these through remote interfaces.


Grid research agenda
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


Mobile medical monitoring presented by david de roure

Broadening

ResearchFocus

Activity

Computation

Knowledge

Sensors

Devices

Mobility

Information

Semantic Modelling

AutonomicBehaviour

Ubiquitous

ResourcesSecurityManagement

Capturing

Activity and

Process

Access Structure

Metadata

Additional

Challenges

Remote Sensing

Architectures

(e.g P2P,

Ad-hoc networs)

Modelling

Simulation

Knowledge Discovery and

Recording

Remote Access

Activity andLab

Monitoring

NewUses

Environmental

Monitoring

Physics

Pharmacy

Medical

Field Scientists

NewScientists

Chemistry

Astronomy

BioInformatics

“Wet” Lab Scientists

Engineering

EnvironmentalScientists

The Maturing eScience “Grid”

1998

2001

2003

2005


Mias devices
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


Mobile medical monitoring presented by david de roure

Asynchronous

Mobile World

Grid Services

Grid protocol

Java Phone

+Blood Monitor

StandardGrid Service for feature detection

Proxy

Buffers Material for sending on

Grid based

Storage Services

Grid protocol

Grid protocol

Patients

Visualisation Services

Proxy

Converts Signals

to database record

Wearable

Devices

Grid protocol

Display

Clinicians


Wearable device
Wearable Device

  • 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

Sensor bus

GPS aerial


Range of different sensors
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
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
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



Putting devices on the grid
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


Mobile medical monitoring presented by david de roure

Sensor port type: self-description

Name

#

Mutability

Modify?

Description

IdentifiedAs

1

Constant

False

Sensor ID, names and type

Description

1

Mutable

False

Expanded description, e.g. placement, accuracy, etc.

MeasurementTemplate

1

Constant

False

The format in which measurements are reported

MeasurementDiscard-PolicyExtensibility

1..*

Constant

False

Acceptable XML schema types for the measurementDiscardPolicy SDE

MeasurementPublication-PolicyExtensibility

1..*

Constant

False

Acceptable XML Schema types for the measurementPublishingPolicy SDE

ConfigurationExtensibility

1..*

Constant

False

Acceptable XML Schema types for sensor configuration SDE

ProxyStatus

1

Mutable

False

Current status, e.g. in contact with proxy or disconnected

Name

#

Mutability

Modify?

Description

MeasurementDiscard-Policy

1

Mutable

True

The conditions under which the sensor should discard historical measurements

MeasurementPublishing-Policy

1

Mutable

True

The conditions under which the sensor (proxy) should make a new measurement public

configuration

0..*

Mutable

True

Sensor-specific configuration information, e.g. sample rate

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

Sensor port type: Externally modifiable configuration

Sensor port type: measurement



Related activities
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 demonstrationsorwww.equator.ac.uk


Live clinical record
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
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


Mobile medical monitoring presented by david de roure

Patient

Proxy of

Mobile

clinician


Location ontology
Location ontology

Ian Millard


Mobile medical monitoring presented by david de roure

Semantic

Pervasive

Grid


Mobile medical monitoring presented by david de roure

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



Conclusion
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