Presentation on theme: "Mobile Medical Monitoring Presented by David De Roure Grid-based Medical Devices for Everyday Health."— Presentation transcript:
Mobile Medical Monitoring Presented 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 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 Oxford Lionel Tarassenko William R. Cobern Oliver J. Gibson University of Southampton David De Roure Don Cruickshank University of Glasgow Matthew Chalmers University of Bristol Henk Muller Chris Setchell University of Lancaster Adrian Friday Oliver Storz Nigel 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
1998200120032005 Access Structure Metadata Capturing Activity and Process Additional Challenges Resources Security Management Architectures (e.g P2P, Ad-hoc networs) Autonomic Behaviour Semantic Modelling Remote Sensing Computation Broadening Research Focus Information Knowledge Mobility Sensors Devices Ubiquitous Activity Modelling Simulation New Uses Knowledge Discovery and Recording Remote Access Environmental Monitoring Activity and Lab Monitoring Environmental Scientists New Scientists Physics Astronomy Chemistry Engineering Pharmacy BioInformatics MedicalField 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
Asynchronous Mobile World Grid Services Java Phone + Blood Monitor Proxy Buffers Material for sending on Grid based Storage Services Standard Grid Service for feature detection Wearable Devices Proxy Converts Signals to database record Visualisation Services Display 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
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#Mutabilit y Modify ? Description IdentifiedAs1ConstantFalseSensor ID, names and type Description1MutableFalseExpanded description, e.g. placement, accuracy, etc. MeasurementTemplate1ConstantFalseThe format in which measurements are reported MeasurementDiscard- PolicyExtensibility 1.. * ConstantFalseAcceptable XML schema types for the measurementDiscardPolicy SDE MeasurementPublication- PolicyExtensibility 1.. * ConstantFalseAcceptable XML Schema types for the measurementPublishingPolicy SDE ConfigurationExtensibility1.. * ConstantFalseAcceptable XML Schema types for sensor configuration SDE ProxyStatus1MutableFalseCurrent status, e.g. in contact with proxy or disconnected Sensor port type: Externally modifiable configuration Name#Mutabili ty Modify ? Description MeasurementDiscard- Policy 1MutableTrueThe conditions under which the sensor should discard historical measurements MeasurementPublishin g- Policy 1MutableTrueThe conditions under which the sensor (proxy) should make a new measurement public configuration0.. * MutableTrueSensor-specific configuration information, e.g. sample rate Sensor port type: measurement Name#MutabilityModify ? Description Measurement1MutableFalseThe most recent measurement made by the sensor MeasurementCounter1MutableFalseA running counter of measurements made MeasurementHistory1MutableFalseThe complete known history of measurements
Related activities The Antarctic Lake Carbon Cycling project The Urban Pollution Monitoring Project See demonstrations or 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
Pervasive applications need the Grid, e.g. Sensor Networks Grid applications need Pervasive Computing e.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. Fundamentally about Interoperability and inference
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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