second generation, fully networked smart medical vest ... · second generation, fully networked...

Second Generation, Fully Networked Smart Medical Vest

UtopiaCompression

UtopiaCompression CorporationDr. Jacob Yadegar (UC) Dr. Anurag Ganguli (UC) Mr. Zvi Topol (UC)

Dr. Hieu Nguyen (UC) Dr. Shalon Zeferjahn (UC)

Professor William Kaiser (UCLA) Dr. Massoud Agahi (Cedars-Sinai Medical Center )

Acknowledgment

Department of Homeland Security


UtopiaCompression Slide 2

Develop a Fully Networked Smart Medical Vest for Health Status Monitoring of First Responders

Project Objective


Agenda


1 – System Overview2 – Sensor Suite3 – Energy Management4 – Intelligent Hierarchical Decision Support System5 – Personalization and Activity Awareness6 – Conclusions

Two Tier Research and Development StrategyTier 1 – The Simulator

Physiological Data Warehouse Hierarchical Decision Support System

Tier 2 – The Global Integrated End-to-End System


PDA attached to the belt

Wearable Wireless Physiological and Anatomic Sensors


http://images.google.com/imgres?imgurl=http://www.finanzen.net/pda/grafik/pda_palmv_2.gif&imgrefurl=http://www.finanzen.net/pda/pda_uebersicht.asp&h=283&w=200&sz=19&tbnid=AKj9ZM5nrwyfpM:&tbnh=114&tbnw=81&prev=/images%3Fq%3Dimage%2Bof%2Ba%2Bpda%26um%3D1&start=3&sa=X&oi=images&ct=image&cd=3


The Simulator

Data Warehouse

Communication Module

Data Storage Module

HDSS

Communication Module

Decision Tree ModulePhysiological Parsing

Algorithm

Health Status



The Global System – the vision

Sensors

ECG

Body Motion

PulseOximeter

Personal Server

Peer PDA

Blue Tooth Personal Server

PDA

Personal Server

Peer PDA

Personal Server

Peer PDA Personal Server

Peer PDA

Command CenterLaptop


BodyTemperature

CerebralOximeter

Etc.

Energy Mgmt


The Sensor Suite


Blood Pressure/Heart Rate/Heart Rhythm Irregularity (A&D MEDICAL) [wrist]Cerebral Oximetry (SOMANETICS) [forehead], BiS Index/EEG (ASPECT MEDICAL SYSTEMS) [corner of forehead],End-Tidal CO2/Respiratory Rate (PHASEIN/SMITHS MEDICAL/NIHON KOHDEN) [nose/mouth], Temperature/Heat Flux/Galvanic Skin Response (BODY MEDIA) [arm], Voice/Respiratory Rate/Cough [throat], Pulse Oximetry/Carbon Monoxide (MASIMO) [finger/toe/ear lobe]


Energy Management

ECG Interface

MicroLEAP

Bluetooth Wireless Interface

Inertial Motion Sensors (3‐Axis Accelerometers

and Gyroscopes)

Standard Sensor

Interfaces

Personal Server

Bluetooth and IEEE 802.11

Wireless Interfaces

Personal Server to Database Server Transport

MicroLEAP 3-AxisMotion Sensors

MicroLEAP 3-AxisMotion Sensors

MicroLEAPECG Sensor

MicroLEAPPulse Oximeter

Personal Server




Global Hierarchical Decision Support System Architecture

A firefighter in operation

Physiologic Sensors Readings

Instantaneous PhysiologicalBehavior Assessment

Temporal/ContextualPhysiological Behavior Assessment

Prediction of Upcoming Physiological Behavior –

Trending

PersonalizedHealth Status Assessment

Personalization Module

Processing Sensors Signals

http://www.firstduetraining.com/ffsurvival.html

Second Generation, Fully Networked Smart Medical Vests

The Ontological Physiologic Knowledgebase System – Top Level Vital Signs


EmergencyCare

Respiratory

SkinTemperature

Cardio-Vascular

Current research focus


Cardio-Vascular Sub-Tree of Emergency Care Ontology

Fragment of Emergency Care Hierarchy

* Alert Level I**Alert Level II*** Alert Level III**** Alert Level IV

Action 1: Do NothingAction 2: Verbal Status CheckAction 3: Buddy Status CheckAction 4: Leave the FieldAction 5: Send Rescue

Cardio-Vascular

HeartRate

BloodPressure

Junctional

Ventricular****

HeartRhythm

Sinus

Type I

Type IIBlock***

No Block

Regular

Irregular***

Normal

Tachy**

{HR>100}

Brady*

{HR< 50}

Abnormal****

Normal**

Block

40 ≤ HR≤ 50***

HR ≤ 40****

Type II or IIIor Other

Action 4/5

EmergencyCare*

Action 2/3

Action 4/5

Action 1

Action 2

Action 3/4

Action 3

No Block Action 3

Type I Action 3

Type (II or III)**** Action 4

Other Action 3/4

Irregular***

No Block Action 3

Block

Type I Action 3/4

Action 4/5

RegularBlock***

No Block

Type I Action 3

Type (II or III)****

or Other

Action 4/5

Action 2/3

Action 4


Type II**** Action 4

Other Action 3/4


Hierarchical Decision Support System – Cardio-Vascular Sub-Tree

HighBP>160

100 < BP ≤ 160 80 < BP ≤ 90***

BP ≤ 80****BP ≤ 100

Cardio-Vascular

EmergencyCare*



HeartRhythm

BloodPressure

High**

{>100}Go to HrhHeart

Rate

Normal{50 ≤ HR ≤ 100}

Action 1

{<50}Low*

*Go to Hrh

Action 1

Action 3/4

160 < BP ≤ 180**

180 < BP ≤ 200***

200 < BP ≤ 220****

BP > 220****

90 < BP ≤ 100**

Action 2

Action 2

Action 3/4

Action 2/3

Action 4/5

Action 4/5

Normal

Low*




Hierarchical Decision Support System – Respiratory Sub-Tree

Respiratory

Cough**

Respiratory Rate



EmergencyCare*

8 < BP ≤ 205< RR ≤ 8***

RR ≤ 5****

RR>20High

Normal

BP ≤ 8Low*

*

Action 1

20 < RR ≤ 30** Action 2

Action 4/5

RR >30*** Action 3/4

Action 2/3

Able to talk Action 2

Unable to talk**** Action 4/5

Fragmented Sentences*** Action 3





1 – Each physiological parameter yields a certain action and alert2 – Challenge is to combine the different alerts and actions to yield a single recommendation?3 – No fixed solution 4 – Machine Learning through use of physiological data labeled by medical experts5 – Modular, Computational Efficiency

Hierarchical Decision Support System – the Parsing Algorithm



• For Radical, Moderate and Conservative strategies, we have compared our iterativelearning method with Naïve Bayes, SVM (with linear kernel), and with NeuralNetworks (Multilayer Perceptron), which are the popular classification methods.

• The following accuracy results were obtained

Learning the Acuity Matrix (continued)

Classifier

Strategy

Our method Naïve Bayes SVM Neural Networks1 hidden layer

Neural Networks Adaptive # of hidden layers

Conservative 98.56% 68.82% 75.86% 79.45% 93.30%

Moderate 88.79% 66.95% 46.2% 61.92% 83.40%

Radical 95.69% 77.15% 89.65% 65.08% 89.09%


Health Status Recommendations – Current, Contextual and Prediction


Conservative versus Moderate recommendationsInstantaneous versus Receding Horizon recommendations



Hierarchical Decision Support System – Personalizing the System

• Normal values of the physiological parameters differ fromindividual to individual

• The Hierarchical Decision Support needs to be “personalized”in order to accurately issue alerts and alarms

• Personalization is done using data collected during trainingexercises


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Variation of heart-rate for three different individuals while performing the same exercise

Necessity for Personalization

Individual A Individual B

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Heart Rate

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Hierarchical Decision Support System – Activity Aware

• Normal values of the physiological parameters also depend on the physical activity being performed

• The Hierarchical Decision Support needs to be “Activity-Aware” in order to accurately issue alerts and alarms

• Activity type can be inferred from motion sensors which are also part of the sensor suite


Activity Recognition


Foot pressure readings Vertical acceleration readings

Sedentary

Walk

Jog

Walk +Weight


Sensor Suite and DSS – Current prototypes


Sensor suite and DSS



ConclusionsDevelopment of a fully networked smart medical vest

Energy Aware

Intelligent Hierarchical Decision Support System

Personalized

Activity Aware



Working for UtopiaCutting edge research environment

Variety of disciplines – Applied mathematics, Artificial Intelligence, Computer Vision, Wireless Communication Networks, Robotics

Contact:[email protected]@utopiacompression.comSee also Jacob or Anurag at the workshop

mailto:[email protected]

mailto:[email protected]

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