Mobile Medical MonitoringPresented by David De Roure

Grid-based Medical Devices for Everyday Health

Overview of talk• Partners• Scenario• Grid software• Demonstration• Current activity• Closing thoughts

Technical innovation in physical and digital lifeHenk 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 NottinghamTom RoddenChris GreenhalghAlastair HampshireJan HumbleJohn CroweBarry Hayes-GillCarl BarrattBen PalethorpeMark Sumner

University of OxfordLionel TarassenkoWilliam R. CobernOliver J. Gibson

University of SouthamptonDavid De RoureDon Cruickshank

University of GlasgowMatthew Chalmers

University of BristolHenk MullerChris Setchell

University of LancasterAdrian FridayOliver StorzNigel Davies

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• 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

1998 2001 2003 2005

Access StructureMetadata

CapturingActivity and

Process

AdditionalChallenges

ResourcesSecurity

ManagementArchitectures

(e.g P2P,Ad-hoc networs)

AutonomicBehaviour

Semantic Modelling

Remote Sensing

ComputationBroadeningResearch

FocusInformation

KnowledgeMobility

Sensors Devices Ubiquitous

Activity

ModellingSimulation

NewUses

Knowledge Discovery andRecording

Remote Access

EnvironmentalMonitoring

Activity andLab

Monitoring

EnvironmentalScientists

NewScientists

Physics

AstronomyChemistry

Engineering

Pharmacy

BioInformatics

Medical Field Scientists

“Wet” Lab Scientists

The Maturing eScience “Grid”

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

AsynchronousMobile World

Grid Services

Java Phone+

Blood MonitorProxy

Buffers Material for sending on

Grid based

Storage Services

StandardGrid

Service for feature detection

Wearable Devices

ProxyConverts Signalsto database record

Visualisation Services

DisplayGrid

proto

col

Grid protocol

Grid protocol

Grid protocol

Patients

Clinicians

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• 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

JavaPhone Blood sugar meter

Data logger PAR sensor

PAR sensor

Other sensors

Cyberjacket (Bitsy)

ECG sensor

accelerometer

GPS receiver

9-wire bus (pluggable)

JavaPhone proxy

Blood sugar meter proxy

Data logger proxy

PAR sensor proxy

Other sensor proxy

Cyberjacket proxy

ECG sensor proxy

Other sensor proxy

Generic device proxy factory(s)

GPRS

Iridium

802.11

Multicast beacon

DF

D

D

D

S

S

S

S

S

Device Proxy Management Client

Register new device New device

configuration

Sensor data-pump

RDBMS

Sensor Database Service

Sensor data-pump

Sensor data-pump

Trial manager

Add sensor to trial database

S/w module

Live monitoring display

Sensor and device status

display

GPS live map

Ōelipse of normalityÕ

visualisation

Scrolling sensor charts

S

Data chooser/ fetcher

Table views

Graph views

Dataflow user interface

DF

D

S

New GRID Port Types: DeviceProxyFactory Device Sensor

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-descriptionName # Mutabilit

yModify?

DescriptionIdentifiedAs 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

Sensor port type: Externally modifiable configurationName # Mutabili

tyModify?

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

Sensor port type: measurementName # 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

• The Antarctic Lake Carbon Cycling project

• The Urban Pollution Monitoring Project

See demonstrationsor www.equator.ac.uk

Advanced Grid Interfaces for Environmental e-Science in the Lab and in the Field

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 ofMobileclinician

Location ontology

subClassOf

akt:Postal-Address Building

Room

Partitioned-Space

akt:Person

Corridor

Floor-Traversing-

Space

akt:Organisation

Enclosed-Space

Abstract-Space

Stairs Lift

subClassOf

subClassOf

subClassOfFloor

subClassOf

subClassOf

has-postal-address

is-part-of

subClassOf

is-part-of

is-part-of

has-usual-occupant

is-part-of

is-part-of is-part-of

is-adjacent-to

subClassOf

permits-access-to

is-owned-by

permits-access-to

Ian Millard

Pervasive

Semantic

Grid

Pervasive applicationsneed the Grid, e.g. Sensor Networks

Grid applications needPervasive Computinge.g. Smart Laboratory

Grid and Pervasive share issues in large scale distributed systems. e.g. service description, discovery, composition; autonomic computing. These can be aided with semantics.

FundamentallyaboutInteroperability and inference

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