Mobile Body Sensor Networks for Health Applications
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Mobile Body Sensor Networks for Health Applications

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    Mobile Body Sensor Networks for Health Applications Mobile Body Sensor Networks for Health Applications Presentation Transcript

    • Mobile Body Sensor Networks for Health Applications Yuan Xue, Vanderbilt Posu Yan, UC Berkeley A collaborative work of Vanderbilt (Sztipanovits, Xue, Werner, Mathe, Jiang) Berkeley (Bajcsy, Sastry’s group) Cornell (Wicker group)
    • Topics
      • Introduction
      • Monitoring congestive heart failure (CHF) patients
        • System overview
        • Security support
        • Experiments
      • WAVE and Berkeley Fit
    • Introduction
      • The cost of health care has become a national concern.
        • Medicare was 35 million for 2003 and 35.4 million for 2004
        • Health care expenditures in the United States will project to rise to 15.9% of the GDP ($2.6 trillion) by 2010 .
      • Impact of Information Technology
        • Electronic Patient Records
        • Remote Patient Monitoring
          • Integration of wireless communication, networking and information technology
          • large amount of medical information can be collected to help determine the most effective strategies for treating chronic illness, reducing disability and secondary conditions
          • improving health outcomes, and reducing the healthcare expenses by more efficient use of clinical resources.
    • Remote Patient Monitoring
      • Needs to be part of the overall chronic disease management process.
      • Requires fully integration of
        • IT Technologies
          • wireless communication, sensor platform, networking, and database
        • Clinical enterprise practice
      • Explicitly incorporates security and privacy policies to protect the end-to-end communication and access of sensitive medical information.
    • System Overview End-to-end Security models Execution Engines BPEL Engine EMR EMR Services Monitor Services Monitor Services Service Oriented Architecture Protocol models Workflow models Monitor models Sensor network Patient management Decision Support Remote Patient Management Computing and Network Infrastructure Clinical Information System Homecare System Execution Engines Clinical Foundation Technology Foundation
    • Monitoring CHF Patients
      • Provide unobtrusive and persistent monitoring
        • Weight
        • Blood pressure
        • Heart rate
        • Energy expenditure
      • Data analysis and feedback
        • Automated - based on thresholds (i.e. cannot allow rapid weight fluctuation, etc.)
        • Doctor intervention
    • System Architecture Medical Database Automated Evaluation Doctor Evaluation feedback 802.11/internet 802.15.4 Bluetooth
    • System Components
      • Hardware
        • Nokia N810 Internet Tablet
          • External 802.15.4 basestation
        • Motion sensor (802.15.4)
        • Weight scale (Bluetooth)
        • Blood pressure monitor (Bluetooth)
      • Software
        • SPINE (Signal Processing In Node Environment)
        • Bluetooth daemon
        • Apache Axis2 WSDL client
      Nokia N810 Motion sensor Weight scale Blood pressure monitor
    • Remote Monitoring Software Architecture SPINE Data sampling Data analysis Sensor control Data analysis Sensor control Data aggregation Web service Buffer Management Secure Comm. Sensor Auth. Secure Communication Sensor Authentication Service Layer TinyOS Telos Mote TinyOS Telos Mote Comm Layer Media Access Control Media Access Ctr Maemo Linux Nokia N10 USB Data analysis Data aggregation Web service TinyOS Workstation OS/hardware platform Sensor Healthcare Gateway Clinical System
    • Integration With Clinical Information System
    • SPINE
      • Open-source framework for managing wireless sensor networks
        • Discovery
          • 1 motion sensor node
        • Configuration
          • Energy expenditure feature @ 1 Hz
        • Data processing
          • Calculate kilocalories per minute
      • SPINEController
        • Main application which runs a SPINE server, communicates with Bluetooth daemon, runs networking thread (WSDL Client)
    • Bluetooth Daemon
      • Communicates with weight scale and blood pressure monitor
        • SDP (Service Discovery Protocol) and SPP (Serial Port Profile) protocols
        • Hardware configured to send last measurement automatically after measurement is taken
      • Communicates with SPINEController through text files
    • Apache Axis2 WSDL Client
      • Runs in thread in SPINEController
      • Queues data
        • Sends data in queue to medical database
        • Automatically retries to send data if unsuccessful (no wireless connectivity)
      • Data log files
        • All data
        • Queued data
    • Security and Privacy Overview
      • Security Requirements
        • Data confidentiality
        • Data integrity
        • Device authentication
        • User authentication and access control
        • Service availability
    • Vertical View Across Different Network Layers
      • Network security
        • involves the security issues from link to transport layer security.
        • provides communication platform security service, including data confidentiality, integrity, source authentication, service availability (e.g., resilience to DoS/jamming attacks)
        • independent of application semantics
      • Application security
        • Web security/ Web service security.(e.g., resilience to SQL injection, cross-site scripting)
        • User authentication and access control
        • Data access policy
        • Ensures the consistency between the privacy policy and workflow
    • Security Mechanisms
      • Existing security mechanisms and solutions to leverage
        • Web security solutions
        • SSL
        • TinySec
      • New security service to implement
        • Device authentication
        • Sensor-to-gateway secure communication
        • Resilience to jamming attack -- channel reallocation
        • Privacy policy enforcement
      • All above security mechanisms need to be integrated in the system
        • Challenge: How to ensure the end-to-end system security
    • Network Security Architecture Data sampling Data analysis Sensor control Data analysis Sensor control Data aggregation Web service Secure Comm. Sensor Auth. Secure Communication Sensor Authentication Service Layer TinyOS Telos Mote TinyOS Telos Mote Comm Layer Channel reallocation Channel reallocation Maemo Linux Nokia N10 USB Data analysis Data aggregation Web service TinyOS Workstation OS/hardware platform Sensor Healthcare Gateway Clinical System SSL
    • Horizontal -- along the message communication path
      • Stage 1: between sensors and mobile gateway
        • IEEE 802.15.4 communication standard
          • Pre-key distribution
          • Sensor device authentication
          • Encryption and MAC generation based on SkipJack in TinySec
            • Computation: 5.3 ms
            • Verification 1.3~1.4ms
        • Bluetooth
      • Stage 2: between sensor fusion center and the Vanderbilt web server.
        • SSL
          • Client device (or user) authentication
          • Data encryption and integration protection
      • Stage 3: Within Vanderbilt Clinical Information System
        • Integration of user authentication and access control policy with workflow model
    • Application-Layer Security Architecture Web Service Layer Alert Processing Workflow Data archive workflow Detail Alert Sensor collection Policy Layer Policy Enforcement Policy Enforcement Monitoring Screen Alert Validating Screen
    • Experiment on CHF Patient
      • 5 hour experiment
        • Nokia N810 battery life approximately 4 hours – required battery change
      • Energy expenditure every minute
      • Weight, blood pressure, heart rate measurement at beginning and end of experiment
      • Hardware malfunction at end of experiment
        • Failed CRC checks on incoming serial packets
    • Experimental Results Time (min) Energy Expenditure (kCal / min) raw data moving avg.
    • Experimental Results Time (min) raw data moving avg. car Energy Expenditure (kCal / min)
    • WAVE and Berkeley Fit
      • Social networking in mobile BSNs for health applications
      • WAVE – API for Android OS
        • Sensor setup through SPINE framework
        • Data processing
          • Action recognition
          • Energy expenditure estimation
          • GPS functions
      • Berkeley Fit
        • Showcase application for WAVE
        • Encourages exercise through social interaction
    • Social Interaction
      • Compete to see who expends the most energy each day
        • Users will see leaderboard with rankings
      • Exercise teams
        • Users exposed to both encouragement and competition
      • Other features
        • 1 mile, 5 mile, etc. competition runs for time
    • Planned Experiments
      • Study of 30 college students
      • Monitor energy expenditure
        • Phase 1
          • Control group with no social feedback
        • Phase 2
          • Add social feedback
        • Change in energy expenditure with social feedback enabled?
    • Summary and Future Work
      • Our system is consistent with the existing clinical enterprise practice, and thus have the capability to scale and become part of the overall patient management process.
      • Future Work
        • Full migration to Android
          • Current Android release has no support for Bluetooth – no external sensors
            • Android 2.0 will have Bluetooth API
        • Distributed action recognition
        • Experiments on obese children
        • Extension of security models to sensor networking system and integration with application-level security models