An Open And Reconfigurable Wireless Sensor Network For Pervasive Health Monitoring
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This document presents a framework for an open and reconfigurable wireless sensor network aimed at pervasive health monitoring. It discusses the challenges and solutions in interoperability, data representation, and medical rules implementation of biosensor networks, along with a prototype that utilizes Sensorml for device communication. Future work includes enhancing event management and improving security measures in the system architecture.
![An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring A. Triantafyllidis, V. Koutkias, I. Chouvarda and N. Maglaveras Lab of Medical Informatics Faculty of Medicine, Aristotle University of Thessaloniki Thessaloniki, Greece [email_address]](https://image.slidesharecdn.com/anopenandreconfigurablewirelesssensornetworkforpervasivehealthmonitoring-091203052904-phpapp02/85/An-Open-And-Reconfigurable-Wireless-Sensor-Network-For-Pervasive-Health-Monitoring-1-320.jpg)
![<!-- Rule process parameters of Average function --> <parameters><ParameterList><parameter name=" heartRateAverage "><Datagroup> <component name="comparisonCriteria"> greaterThan </component><component name="maxThreshold">100</component> <component name="timeFrame">300</component></Datagroup></parameter><parameter name="relationship"><Datagroup> <component name="logicCriteria">[OR]</component></Datagroup></parameter><parameter name="glucoseAverage"> <Datagroup><component name="comparisonCriteria">greaterThan</component> <component name="maxThreshold">200</component><component name="timeFrameRule">1800</component> </Datagroup></parameter></ParameterList></parameters></process> SensorML-based Medical Rule Example Definition (2/2)](https://image.slidesharecdn.com/anopenandreconfigurablewirelesssensornetworkforpervasivehealthmonitoring-091203052904-phpapp02/85/An-Open-And-Reconfigurable-Wireless-Sensor-Network-For-Pervasive-Health-Monitoring-21-320.jpg)
![An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring A. Triantafyllidis, V. Koutkias, I. Chouvarda and N. Maglaveras Lab of Medical Informatics Faculty of Medicine, Aristotle University of Thessaloniki Thessaloniki, Greece [email_address] Thank you!!!](https://image.slidesharecdn.com/anopenandreconfigurablewirelesssensornetworkforpervasivehealthmonitoring-091203052904-phpapp02/85/An-Open-And-Reconfigurable-Wireless-Sensor-Network-For-Pervasive-Health-Monitoring-25-320.jpg)
An Open And Reconfigurable Wireless Sensor Network For Pervasive Health Monitoring
- 1. An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring A. Triantafyllidis, V. Koutkias, I. Chouvarda and N. Maglaveras Lab of Medical Informatics Faculty of Medicine, Aristotle University of Thessaloniki Thessaloniki, Greece [email_address]
- 2. Presentation Outline Sensor Networks today: Pros and Cons Our Proposal: A Framework for Interoperable and Open Biosensor Networks System Architecture Functional Attributes Data and System Representation Prototype Implementation Description Discussion and Future Work
- 3. Sensor Networks: Trends and Recent Advances Miniaturization CPU Speed and Memory increase according to Moore’s Law OS Advances: Multi-threading OS, Java Platform New Protocols: Wireless Communication: ZigBee, WiBree Data Representation: IEEE 1451,SensorML
- 4. Sensor Networks Today: Challenges Interoperability : applications “tight” to hardware difficulties in achieving interoperability for different sensor nodes and different services custom solutions with maintenance problems Intelligence : nodes usually not smart enough to do processing tasks accumulation of processing burden in a single central node Energy : high energy costs through continuous data transfer in wireless links
- 5. System Architecture Objectives Formal Sensor Descriptions : Sufficient description of the entire sensor network, including nodes and processes/services Openness : Plug ‘n play support in adding/removing nodes Reconfigurability : Sensors adaptation to application/user needs in run-time Requirements : A data representation standard for communication requirements: OGC® SensorML ( http:// www.opengeospatial.org/standards/sensorml / ) Intelligence: Medical Rules
- 6. SensorML: A New Standard for Sensor Description Sensor Node Identification (meta-data info) Sensor Capabilities (e.g. resolution, range, accuracy) Sensor Inputs (e.g. sensor data detected from environment, sensor data from another node) Sensor Outputs (e.g. sensor data, alerts according to specific criteria) Sensor Processes (e.g. calculation of a MIN derivative in a sensor data array) Sensor Connections (Connections of nodes inputs to outputs and vise versa)
- 7. SensorML Example Document and Schema Physical and Functional Entities are expressed as processes <sml: ProcessModel id=”PROCESS_ID”> <sml:metadata … /> <sml:inputs … /> <sml:outputs … /> <sml:parameters … /> <sml:method xlink:href=”urn:{authority}:def:process:{processName}:{version}”/> </sml:ProcessModel> Schematron language can be used for strict definition of XML schema <sch:rule context=”xpath expression”> <sch:assert test=”xpath expression”> Message if incorrect </sch:assert> <sch:assert … /> <sch:assert … /> </sch:rule> <sch:rule … /> <sch:rule … /> </sch:pattern> <sch:pattern … /> <sch:pattern … /> </sch:schema>
- 9. Sensor Capabilities/Modules Sensing : Acquirement of sensing phenomenon data Monitoring : Data processing according to pre-defined Medical Rules. Corresponding algorithms take place Communication : Communication with MBU via SensorML messages when an event is recognized Data Handling : Serialization and de-serialization of data
- 10. MBU (Mobile Base Unit) Capabilities Additional capabilities with respect to Sensors: Data Aggregation Compound rules containing information about multiple biosignals (currently, support of AND/OR logical operators) Actions Respond to sensor Generate alert for user Forward information to Medical Center
- 11. Medical Rules: Event and Action Oriented Events : Spontaneous Events Monitor whether a raw signal value or signal derivative, average value, maximum or minimum value etc, is above or below predefined thresholds within a time window Persistent Events Event onset, Event end (currently , under development) Actions : Triggered in the form of messages to the MBU, describing the event and optionally carrying data that triggered the event
- 12. Communication Flow (1/4) MBU receives Sensor Node’s self-description encapsulated in a SensorML message Dynamic GUI construction in the MBU device Interoperability between MBU and Sensor Node is achieved Ability of creating personalized Medical Rules Send Sensor Description Sensor MBU
- 13. Communication Flow (2/4) 2. MBU sends Medical Rules info to the Sensors involved Sensor Logic adaptation takes place Sensor Data processing according to Medical Rule Medical Rule example: Get the average value of heart rate in a time window of X sec when average value above Y pulses/min Send Medical Rules Sensor MBU
- 14. Communication Flow (3/4) 3. Sensor sends alert to the MBU when the rule criteria are met and the corresponding event is triggered, containing: Sensor Data Event Start time Event Description Send Alert Sensor MBU
- 15. Communication Flow (4/4) 4. MBU forwards information related to the triggered event to the Medical Center Currently, asynchronous ways of communication are studied: SMS, MMS Forward Information (SMS/MMS) MBU Medical Center
- 16. Prototype Features Sensors emulation as mobile terminals, e.g.: Heart Rate Sensor Blood Glucose Sensor MBU is a PocketPC - SmartPhone device (Currently, a Qtek MDA II device) Bluetooth is chosen as the wireless communication link: Low-cost Low-power Used widely SensorML adoption for data exchange
- 17. Prototype Implementation J2ME-CLDC (Java 2 Micro Edition- Connected Limited Device Configuration) Open platform for mobile applications Currently supported by Sun SPOT sensor platform Supported by the majority of mobile devices JSR-82 Libraries (Java Specifications Request) Interface that links Bluetooth hardware to Java Ad-hoc device connection Automatic service discovery KXML Lightweight, non-validating pull-parser for serialization and de-serialization of SensorML data ( http:// kobjects.org/kxml / )
- 18. SensorML-based Sensor Self-description Example (1/2) <!– Sensor Identification --> <Sensor id="HR_id"> <identification><identifierList><identifier name="shortName"/><identifier name="longName"/><identifier name="modelNumber"/><identifier name="manufacturer"/></identifierList></identification> <!-- Sensor general and functional capabilities --> <capabilities><PropertyList><property name="generalCapabilities"><DataGroup><component name=" resolution ">1.0</component> <component name=" range ">0 200</component><component name=" accuracy ">-1 1</component></DataGroup></property> <!-- Sensor functional capabilities for samplingRate and sizeOfWindow configuration --> <property name="functionalCapabilities"><DataGroup><component name=" samplingRateRangeConfigurationCapability "> 0.001 1</component><component name=" sizeOfWindowRangeConfigurationCapability "> 1 100</component></DataGroup></property></PropertyList></capabilities>
- 19. <!--Sensor inputs--> <inputs><inputList><input name=" heartRate "></input></inputList></inputs> <!--Sensor outputs--> <outputs><outputList><output name=" minAlert "><DataGroup><component name=" eventTime "></component><component name =" measuredValue "> </component></DataGroup></output><output name=" maxAlert " .../><output name=" averageAlert " .../></outputList></outputs> <!--Definition of the actuating procedure of an alert with MIN criteria, as a process model--> <processes><processList><process name =" minAlertRuleTrigger "> <inputs><inputList><input name="heartRate"></input></inputList></inputs> <outputs><outputList><output name="eventTime"></output><output name="measuredValue"> </output></outputList></outputs><parameters><ParameterList><parameter name="heartRateMin"><Datagroup> <component name="comparisonCriteria"/><component name="maxThreshold"/> <component name="timeFrame"/></Datagroup></parameter></ParameterList> </parameters></process>...</processList></processes> <!-- System inputs to process inputs, process outputs to system outputs --> <connections><ConnectionList><connection name=" outputOfMinAlertRuleTriggerToSensor "><Link> <source ref="minAlertRuleTrigger/outputs/measuredValue"/><destination ref="this/outputs/minAlert/measuredValue"/></Link> </connection>...</ConnectionList></connections></Sensor> SensorML-based Sensor Self-description Example (2/2)
- 20. SensorML-based Medical Rule Example Definition (1/2) <process name =" AVG_HRGLU"> <!-- Rule process requests of sampling rate --> <capabilities><PropertyList><property name="samplingRateHeart">1</property><property name="samplingRateGlucose">0.1</property> </PropertyList></capabilities> <!-- Rule process inputs --> <inputs><InputList><input name="physiologicalPhenomena"><DataGroup><component name="heartRate"></component><component name="bloodGlucose"></component></DataGroup></input></InputList></inputs> <!-- Rule process outputs --> <outputs><OutputList><output name="AVG_HRGLU" description="AVG_HRGLU DESCRIPTION"><DataGroup> <component name="outputGivenAs">SMS</component><component name="smsText">PATIENT IN ALERT CONDITION</component> <component name="eventTime" /><component name="measuredValue" /><component name="outputTimeFrame"/> <component name="dt"/></DataGroup></output></OutputList></outputs>
- 21. <!-- Rule process parameters of Average function --> <parameters><ParameterList><parameter name=" heartRateAverage "><Datagroup> <component name="comparisonCriteria"> greaterThan </component><component name="maxThreshold">100</component> <component name="timeFrame">300</component></Datagroup></parameter><parameter name="relationship"><Datagroup> <component name="logicCriteria">[OR]</component></Datagroup></parameter><parameter name="glucoseAverage"> <Datagroup><component name="comparisonCriteria">greaterThan</component> <component name="maxThreshold">200</component><component name="timeFrameRule">1800</component> </Datagroup></parameter></ParameterList></parameters></process> SensorML-based Medical Rule Example Definition (2/2)
- 22. MBU Demo Snapshots Adaptive User Interface for medical rules definition through a step-by-step procedure: Parameter selection and parameter definition
- 23. MBU Snapshots in PDA Adaptive UI as seen in a Qtek MDA II device
- 24. Discussion: Future Work Support for management of both spontaneous and persistent events: Recognition of repetitive events Alert severity and prioritization Interconnection with Medical Center SOA Architecture: Web Services Security issues: Currently employed the built-in authentication mechanism of Bluetooth Elaborating on data encryption techniques on top of the Bluetooth protocol stack as a more advanced security approach
- 25. An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring A. Triantafyllidis, V. Koutkias, I. Chouvarda and N. Maglaveras Lab of Medical Informatics Faculty of Medicine, Aristotle University of Thessaloniki Thessaloniki, Greece [email_address] Thank you!!!