Mobile Medical Monitoring Presented by David De Roure

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Mobile Medical MonitoringPresented by David De
Roure
Grid-based Medical Devices for Everyday Health
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Overview of talk

• Partners
• Scenario
• Grid software
• Demonstration
• Current activity
• Closing thoughts

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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)

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• 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
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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.

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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

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The Maturing eScience Grid
1998
2001
2003
2005
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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

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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
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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
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Range of different sensors

• ECG
• Oxygen saturation
• Body movement
• Accelerometers
• GPS
• All plug and play to standard bus
• Changes reported to the underlying infrastructure

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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

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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

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Deploying on the Grid
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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

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Sensor port type self-description
Sensor port type Externally modifiable
configuration
Sensor port type measurement
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Demo at All Hands Meeting in Nottingham, 2003
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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 demonstrationsor www.equator.ac.uk

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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.

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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

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Location ontology
Ian Millard
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Semantic
Pervasive
Grid
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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
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http//ubigrid.lancs.ac.uk/
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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

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• www.equator.ac.uk