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)

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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

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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

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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

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Experimental Results

Time (min)

Ene

rgy

Exp

endi

ture

(kC

al /

min

)raw data

moving avg.

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Experimental Results

Time (min)

raw data

moving avg.

car

Ene

rgy

Exp

endi

ture

(kC

al /

min

)

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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?

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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