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Mobile Body Sensor Networks for Health Applications
Yuan Xue, Vanderbilt
Posu Yan, UC Berkeley
A collaborative work ofVanderbilt (Sztipanovits, Xue, Werner, Mathe, Jiang) Berkeley (Bajcsy, Sastry’s group) Cornell (Wicker group)
2
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.
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4
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
5
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
End-to-end Security models
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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
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System Architecture
802.15.4Blue
toot
h
Medical Database
Automated Evaluation
Doctor Evaluation
feed
back 802.11/internet
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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
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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 N10USB
Data analysis
Data aggregation
Web service
TinyOS
Workstation
OS/hardware
platform
Sensor Healthcare Gateway Clinical System
SPINE
Integration With Clinical Information System
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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)
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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
13
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
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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
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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
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Challenge: How to ensure the end-to-end system security
Network Security Architecture
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Data sampling
Data analysis
Sensor control
Data analysis
Sensor control Data aggregation
Web serviceSecure Comm.
Sensor Auth.
Secure Communication
Sensor Authentication
Service Layer
TinyOS
Telos Mote
TinyOS
Telos Mote
Comm Layer Channel reallocation Channel reallocation
Maemo Linux
Nokia N10USB
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
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Application-Layer Security Architecture
Monitoring Screen
Web Service LayerWeb Service Layer
Alert Processing Workflow
Alert Processing Workflow
Data archive workflow
Data archive workflow
Alert Validating Screen
DetailAlert
Sensorcollection
Policy LayerPolicy LayerPolicy Enforcement
Policy Enforcement
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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
21
Experimental Results
Time (min)
Ene
rgy
Exp
endi
ture
(kC
al /
min
)raw data
moving avg.
22
Experimental Results
Time (min)
raw data
moving avg.
car
Ene
rgy
Exp
endi
ture
(kC
al /
min
)
23
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
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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
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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?
26
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
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