IBM Research © 2006 IBM Corporation HARMONI: Client Middleware for Long-Term, Continuous, Remote Health Monitoring Iqbal Mohomed, Maria Ebling, William

IBM Research

© 2006 IBM Corporation

HARMONI: Client Middleware for Long-Term, Continuous, Remote Health Monitoring

Iqbal Mohomed, Maria Ebling, William Jerome, Archan Misra

IBM Research


Remote Health Monitoring: The Business Motivation

The United States spends $1.9 trillion on healthcare, or more than 16% of its GDP

More than 90 million Americans live with chronic illnesses.

– Chronic diseases account for 70% of all deaths in the United States.

– The medical care costs of people with chronic diseases account for more than 75% of the nation’s $1.4 trillion medical care costs.

– Chronic diseases require long-term management

By 2010, the US will experience the most citizens in history, age 65 or over

– 200,000 Doctor Deficit by the year 2010

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Remote Health Monitoring: The Opportunity

Long-term monitoring offers benefits:– Early disease detection and trend analysis for healthy and at-risk individuals – Treatment and progress monitoring for patients– Participants in drug trials or experimental treatments to gauge efficacy, and side

effects– Reduced workload on doctors, nurses and other healthcare providers

Enabled by rapid improvements in two key technologies:– Improvements in wireless communications (WiFi, 3G, Bluetooth)– Continuing miniaturization of wireless sensors.

Server

Patient Diary

BT

Data

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Challenges of Long-term Monitoring

Technical

– Cheap, unobtrusive, relatively accurate sensor technology

– Specialized backend data storage, processing, analysis and visualization techniques and infrastructure

– Techniques to deal with the limitations of mobile devices

– End-to-end security

Non-Technical

– Privacy, Bioethics, Healthcare Access, etc.

IBM Research


Challenges of Long-term Monitoring

Technical

– Cheap, unobtrusive, relatively accurate sensor technology

– Specialized backend data storage, processing, analysis and visualization techniques and infrastructure

– Techniques to deal with the limitations of mobile devices

– End-to-end security

Non-Technical

– Privacy, Bioethics, Healthcare Access, etc.

IBM Research


Remote Health Monitoring using Personal Mobile Hub

Three-tier architecture, using a personal pervasive device (cell phone or PDA) as a relay (sensorpervasive device server)

– Cellphones are becoming the ubiquitous computing device.

Sensors collect variety of physiological and context data, and transmit via Bluetooth– Examples: Heart Rate, Weight, Blood Pressure, GPS

Examples: PCC (IBM) , CodeBlue (Harvard), Medical Jacket (Berkeley)

PAN (Bluetooth)WAN

(CDMA)

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The Evolution of Long-term Monitoring

3-Tier Hub Architecture

Client device functions as a pure relay

– All data is relayed to backend server

– Provides only “store-and-forward” during disconnection

Optimizing client resources (e.g., bandwidth, energy) not a primary objective

– Sensor stream rates relatively modest in practice

HARMONI: Healthcare Adaptive Remote Monitoring

Data stream processing distributed across both client and server

– Appropriately filtered data relayed to backend server

– Local triggering of actions while in disconnected state

Optimized usage of device resources and network bandwidth

– Context-aware, adaptive data filtering

– Using connectivity predictions for scheduling transmissions

– Stream-based data compression

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HARMONI: Opportunities Addressed

Efficient utilization of bandwidth and energy

– If you are sitting at your desk, and have a heart rate within a normal range, does the system need to transmit every single value?

Customize behavior for individual users

– Is the normal range of your heart rate the same as the person sitting beside you?

Adjust system behavior to the user’s context

– If you leave your desk and go to the gym, does the range of your heart rate change?

Cope with disconnections

– What happens if an “interesting” pattern in sensor readings occurs when there is no connectivity to the remote server?

IBM Research


Key Innovations in HARMONI

Context-Aware Stream

Correlation and

Data Filtering

Predictive Anticipation and

Transmission Scheduling

Smart Disconnected

Operation

Compressed,

Energy-Efficient

Sensor Data Relaying

IBM Research


HARMONI Implementation Platform

Nokia 770 Internet tablet– ARM processor, Linux-based– High-resolution display(800x480), touch screen with

up to 65,536 colors – 64-128 MB RAM, 64 MB FLASH storage

(expandable up to 1GB … can be used for virtual memory)

– Built-in Bluetooth (BlueZ stack) and 802.11 interfaces

– Relatively cheap: $350– http:///www.maemo.org provides open-source

software and development environment.– Code compiled on an Intel/Debian Linux 3.1 box

using cross-compiler (http://www.scratchbox.org)

Nonin Model 4100 Sp02/heart rate monitor– Provides Heart rate and Oxygen saturation– Supports Bluetooth Serial Port Profile (SPP)– 120 hours of continuous operation with 2 AA

batteries– Three packets transmitted per second, where each

packet is 375 bytes

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Variation in User Data

Variation in Heart Rate Readings for Different Individuals

0

50

100

150

200

Time

Hea

rt R

ate

(bea

ts p

er m

inu

te)

IBM Research


Summary: Next Steps

(Ongoing) Complete implementation and testing of the HARMONI Middleware

– Perform real user studies to validate the impact of data compression and event filtering

Develop algorithms and techniques for efficient connectivity prediction and anticipation.

Connect with backend component to develop personalized filters and rules.

IBM Research


(Backup) Data rates for different sensors

Data Rates for Different Sensors

8147.456

294.912

1440

0

200

400

600

800

1000

1200

1400

1600

Sp02 EKG EEG EMG

Kb

its

per

sec

on

d

Documents

IBM Research © 2006 IBM Corporation HARMONI: Client Middleware for Long-Term, Continuous, Remote Health Monitoring Iqbal Mohomed, Maria Ebling, William