mobilizing mhealth: interdisciplinary computer science and engineering

eric c. larson | eclarson.com

interdisciplinary computer science and engineering mobilizing mhealth

Assistant Professor Computer Science and EngineeringWednesday, September 11, 13

databases

algorithms

CompSci

Wednesday, September 11, 13

mobile

databases

algorithms

CompSci

mobile

databases

algorithms

CompSci

mhealth

mobile

databases

algorithms

CompSci mhealth

what is mhealth?

stress check

glucose buddyfitness trainer

heart rate

zombie run

consider physician’s need

compliance?

doctor patient?

compliance?

doctor patient?

compliance

what can the mobile phone already sense ubiquitously?

accelerometergyroscopemagnetometer /compassdual camera / flash1+ microphonesproximity sensorcapacitive sensorgpsmotorized actuator

compliance++;cost--;dr_pat *= 10;

what can the mobile phone sense with clinical utility?

futureresearch

lungfunction jaundice

futureresearch

spirometer lung function??Wednesday, September 11, 13

spirometer

device that measures amount of air inhaled and

exhaled.

lung function

asthmaCOPDcystic fibrosis

evaluates pulmonary impairments

using a spirometer

volume

timeWednesday, September 11, 13

using a spirometer

volume

using a spirometer

volume

volume-time graphvo

FEV1: Forced Expiratory Volume in 1 secondFVC: Forced Vital Capacity

volume-time graphvo

time1 sec.

volume-time graphvo

time1 sec.

FVCFEV1% = FEV1/FVC

FEV1% = FEV1/FVC

> 80% healthy60 - 79% mild40 - 59% moderate

< 40% severe

flow-volume graphflo

volume

flow-volume graphflo

volume

volumeFEV1 FVC

1 sec.

PEF: Peak Expiratory FlowFEV1: Forced Expiratory Volume in 1 secondFVC: Forced Vital Capacity

flow-volume graph

volume

normal

flow-volume graph

volume

normalobstructive

flow-volume graph

obstructive diseases

resistance in air path leads to reduced air flow

obstructive diseases

resistance in air path leads to reduced air flow

restrictive diseases

lungs are unable to pump enough air and pressure

restrictive diseases

lungs are unable to pump enough air and pressure

flow-volume graphFlo

Volume

normalobstructive

flow-volume graphFlo

Volume

normal

restrictiveobstructive

clinical spirometry

home spirometry

faster detectionrapid recovery

trendingWednesday, September 11, 13

home spirometry

high cost barrierpatient compliance

less coachinglimited integration

challenges with

flow ratevolume

lung functionairflowsensor

flow ratevolume

soundpressure microphone

flow ratevolume

soundpressure microphone processing

estimated

SpiroSmart

availabilitycostportabilitymore effective coaching interfaceintegrated uploading

Using SpiroSmart

]Wednesday, September 11, 13

Using SpiroSmart

study design

x 3Wednesday, September 11, 13

study enrollment

participants 5218-75 years old, mostly healthy18-75 years old, mostly healthy

study a

study enrollment

study a

participants 1012-17 years old, mixed healthy/abnormal12-17 years old, mixed healthy/abnormal

study b

study enrollment

study a

participants 1012-17 years old, mixed healthy/abnormal12-17 years old, mixed healthy/abnormal

study b

participants 5610-69 years old, mostly abnormal10-69 years old, mostly abnormal

study c

enrolled bypulm

onologists

flow features

measuresregression

FEV1FVCPEF

0 1 2 3 40

Volume(L)

0 2 4 6 8 100

time(s)

0 1 2 3 40

)Volume(L)

0 2 4 6 8 100

time(s)

flow features

measuresregression

curveregression

FEV1FVCPEF

0 1 2 3 40

Volume(L)

0 2 4 6 8 100

time(s)

0 1 2 3 40

)Volume(L)

0 2 4 6 8 100

time(s)

flow features

measuresregression

curveregression

lung functionFEV1FVCPEF

0 1 2 3 4 5 6 7−1

−0.5

time(s)

amplitude

flow features

0 1 2 3 4 5 6 7−1

−0.5

time(s)

amplitude

flow features

vocal tractsource output

time(s)

frequency(Hz)

1 2 3 4 5 60

0 1 2 3 4 5 6 7−1

−0.5

time(s)

amplitude

flow features

0 1 2 3 4 5 6 7−1

−0.5

time(s)

amplitude

envelope detection

0 1 2 3 4 5 6 7−1

−0.5

time(s)

amplitude

resonance tracking0 1 2 3 4 5 6 7

−0.5

1lpc8raw

time(s)

amplitude

flow estimation features

vocal tractsource output 0 1 2 3 4 5 6 7−1

−0.5

1lpc8raw

time(s)

amplitude

auto-regressive estimate

time(s)

frequency(Hz)

1 2 3 4 5 60

curveregression

−1 0 1 2 3 4 50

time(s)

0 1 2 3 4 50

time(s)

feature 1

feature N

window

ed machine

learning regression

−1 0 1 2 3 4 50

time(s)

flow(L/s)

curveregression

−1 0 1 2 3 4 50

time(s)

0 1 2 3 4 50

time(s)

feature 1

feature N

curveoutput

window

ed machine

learning regression

resultsmeasuresregression

curveregression

−2 0 2 4 60

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

curveregression

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

curveregression

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

curveregression

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−2 0 2 4 60

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

−1 0 1 2 3 40

volume(L)

flow(L/s)

can SpiroSmart curves be used for diagnosis?

survey

• 10 subjects curves

survey

• 5 pulmonologists

survey

• 5 pulmonologists

• unaware if from SpiroSmart / spirometer

survey

results

normalminimal obstructivemild obstructivemoderate obstructivesevere obstructive

restrictive

inadequate

results

restrictive

inadequate

identical64%

results

restrictive

inadequate

one off10%

identical64%

results

restrictive

inadequate

one off10%

identical64%

results

restrictive

inadequate false positive14%

one off10%

identical64%

results

restrictive

inadequate

false negative4%

false positive14%

one off10%

identical64%

results

restrictive

inadequate

error8%false negative

false positive14%

one off10%

identical64%

results

restrictive

inadequate

error8%false negative

false positive14%

one off10%

identical64%

abnormal vs normal96%

appropriate for screening and early diagnosis

in the home? in the developing world?

in 10 years, COPD will surpass AIDS/HIV as cause of death in developing nations

futureresearch

neonatal jaundice in the US

kernicterus: 21

hazardous jaundice: 1158

extreme jaundice: 2,317

severe jaundice: 35,000

phototherapy: 290,000

visible jaundice: 3.5 million

births/year: 4.1 million

Method Accuracy Disadvantages

TSB Gold standard (r=1.0)

Painful, costly, inconvenient, delayed

TcBAccurate

(r= 0.75 -0.93)Meter = $7000

tips $5 unavailable in most

physician offices

Visual assessment (provider or parent)

not accurate (r= 0.36 - 0.7), underestimates

severity

No standardization lighting, pigmentation

bilicam

participants 400-4 days old, collected in nursery0-4 days old, collected in nursery

study a

participants 400-4 days old, collected in nursery0-4 days old, collected in nursery

study a

Color Linearization

• Camera Settings Adjustment

• Light Source Estimation

Image Segmentation

• Quality Control for Distance, Lighting, and Shadow

• Sternum, Forehead, Card Segmented

Color Calibration

• Dynamic Least Squares Regression

• Automatic Feature Selection

Neonatal Skin Response to Bilirubin

• Skin Independent Color Transformations Applied

• Multivariate Machine Learning Regression

bilicam

0 5 10 15bilicam values

Fitted values bilicam

biliru

bilicam estimation0

155 10

mg/dlr=0.91

bilicam initial results

• near term: screening

• near term: screening• medium term: TSB study

• near term: screening• medium term: TSB study• long term: developing world

“in many resource poor nations, hyperbilirubinemia is the second or

third leading cause of infant mortality and disability”

futureresearch

oxygen volume, VO2

blood pressureWednesday, September 11, 13

intra ocular pressure

activity detectionelder care

daily activity logscongestive heart failure

health markers via phoneblood pressurepulse oximetrystress

opportunistic sensingpain managementdetecting circulation

activity detectionelder care

daily activity logscongestive heart failure

health markers via phoneblood pressurepulse oximetrystress

opportunistic sensingpain managementdetecting circulation

developing worldWednesday, September 11, 13

take away

• CSE has a large role in the coming evolution of

mHealth, equal to doctors

• sensing and clinical utility

• deploy in clinic, deploy in world, then release

• ... look for a new mobile application sensing

course ...

eric c. larson | eclarson.com

interdisciplinary computer science and engineering mobilizing mhealth

Assistant Professor Computer Science and Engineering

eclarson.com eclarson@lyle.smu.edu@ec_larson Thank You!

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