codeblue: wireless sensor networks for emergency medical care

CodeBlue: Wireless Sensor Networks for Emergency Medical Care David Malan, Thaddeus R.F. Fulford-Jones, Victor Shnayder, Breanne Duncan, and Matt Welsh, Harvard University Mark Gaynor, Boston University, Steve Moulton, Boston Medical Center Wireless Sensor Networks Family of UC Berkeley “mote” designs WeC (1999) René (2000) DOT (2001) Exciting emerging domain of deeply networked systems • Typical 4 MHz microcontroller, 4 KB RAM, 128 KB ROM • FSK radio up to 19.2 Kbps, range > 100m • 15-20mA active (5-6 days), 15μA sleeping (21 years, but limited by battery) Drive towards miniaturization and low power • Eventual goal - complete systems in 1 mm 3 , MEMS sensors MICA (2002) Speck (2003) CodeBlue for Emergency Medical Triage triage decisions, relays to EMTs Ambulance system makes Correlate with patient records at hospital PDAs carried by EMTs receive vital signs and enter into field report collect vital signs (pulse ox, heart rate, etc.) Motes attached to patients • Patient motes form ad-hoc wireless network with EMT PDAs • Enables rapid, continuous survey of patients in field • Requires secure, reliable communications Research Challenges Flexible Network Communications Infrastructure • Nodes adapt to changes in location, connectivity, and link quality • High-risk patients receive higher network service level Distributed Data Collection and Integration • In-network data aggregation and analysis • Patient data integrated into hospital information systems Fault-tolerance and Data Integrity • Resilience to node failures and assurance of accurate data VitalDust: Wireless Pulse Oximeter Pulse oximetry interface board Mote with processor and radio Antenna Finger sensor • Mica2 motes transmit blood oxygen and pulse statistics • PDA visualization tools allow mobile patient monitoring iRevive: Mobile Patient Care Record • PDA-based Patient Care Record software • Collect incident information, observations, procedures • Automatic transfer of PCR to hospital on arrival The Hourglass Data Collection Network Storage Compression Transcoding Aggregation Hospital information systems EMS dispatch 911 dispatch EMTs Hospital staff Vital Dust sensors AEP AEP AEP Filesystem SQL Queries Application Application Application Application Discovery Service Event Notification VitalEKG: Wireless Two-lead EKG Wireless Ad-Hoc Routing for Critical Care Path to nearest EMT Prioritized path Critical patient • Dynamic route discovery • Prioritize path for critical patients • Security • Adaptive energy conservation Mass Casualty Events and Disasters Biochemical Attack Scenario • EMTs easily identify and treat high-risk patients Coordinated Response • Sensors assess situation and invoke emergency response services Improved Organization • Automatic, optimized allocation of hospital and triage resources Future Work Multi-Hop Routing Protocol • Design prioritization and power conservation strategies Hourglass Data Collection Network • Peer-to-peer publish/subscribe overlay network • Push aggregation and filtering services into overlay nodes • Forthcoming project to link metropolitan 911/EMS dispatch services (Telecom City) Demonstrations and Deployment • Demo VitalDust prototypes for Boston area hospitals and EMT teams • Interested parties conduct case studies using VitalDust technology http://www.eecs.harvard.edu/syrah [email protected]

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CodeBlue: Wireless Sensor Networksfor Emergency Medical Care

David Malan, Thaddeus R.F. Fulford-Jones, Victor Shnayder, Breanne Duncan, and Matt Welsh , Harvard UniversityMark Gaynor , Boston University, Steve Moulton , Boston Medical Center

Wireless Sensor NetworksFamily of UC Berkeley “mote” designs

WeC (1999) René (2000) DOT (2001)

Exciting emerging domain of deeply networked systems• Typical 4 MHz microcontroller, 4 KB RAM, 128 KB ROM• FSK radio up to 19.2 Kbps, range > 100m• 15-20mA active (5-6 days), 15µA sleeping (21 years, but limited by

battery)

Drive towards miniaturization and low power• Eventual goal - complete systems in 1 mm3, MEMS sensors

MICA (2002) Speck (2003)

CodeBlue for Emergency Medical Triage

triage decisions, relays to EMTsAmbulance system makes

Correlate with patient recordsat hospital

PDAs carried by EMTsreceive vital signs and enterinto field report

collect vital signs (pulse ox, heart rate, etc.)Motes attached to patients

• Patient motes form ad-hoc wireless network with EMT PDAs• Enables rapid, continuous survey of patients in field• Requires secure, reliable communications

Research ChallengesFlexible Network Communications Infrastructure

• Nodes adapt to changes in location, connectivity, and link quality• High-risk patients receive higher network service level

Distributed Data Collection and Integration• In-network data aggregation and analysis• Patient data integrated into hospital information systems

Fault-tolerance and Data Integrity• Resilience to node failures and assurance of accurate data

VitalDust: Wireless Pulse Oximeter

Pulse oximetryinterface board

Mote withprocessor and radio

Antenna

Finger sensor

• Mica2 motes transmit blood oxygen and pulse statistics• PDA visualization tools allow mobile patient monitoring

iRevive: Mobile Patient Care Record

• PDA-based Patient CareRecord software

• Collect incident information,observations, procedures

• Automatic transfer of PCR tohospital on arrival

The Hourglass Data Collection Network

Storage

CompressionTranscoding

Aggregation

Hospital informationsystems

EMS dispatch

911 dispatch

EMTs

Hospital staff

Vital Dust sensors

AEP AEP AEP

Filesystem SQL Queries

Application Application Application Application

DiscoveryService

Event Notification

VitalEKG: Wireless Two-lead EKG

Wireless Ad-Hoc Routing for Critical CarePath to nearest EMTPrioritized path

Critical patient

• Dynamic route discovery

• Prioritize path for criticalpatients

• Security

• Adaptive energy conservation

Mass Casualty Events and DisastersBiochemical Attack Scenario

• EMTs easily identify and treat high-risk patients

Coordinated Response• Sensors assess situation and invoke emergency response services

Improved Organization• Automatic, optimized allocation of hospital and triage resources

Future WorkMulti-Hop Routing Protocol

• Design prioritization and power conservation strategies

Hourglass Data Collection Network• Peer-to-peer publish/subscribe overlay network• Push aggregation and filtering services into overlay nodes• Forthcoming project to link metropolitan 911/EMS dispatch services

(Telecom City)

Demonstrations and Deployment• Demo VitalDust prototypes for Boston area hospitals and EMT teams• Interested parties conduct case studies using VitalDust technology

http://www.eecs.harvard.edu/syrah [email protected]

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