Mobilizing mHealth: interdisciplinary computer science and engineering

eric c. larson | eclarson.com
interdisciplinary computer science and engineering
mobilizing mhealth
Assistant Professor Computer Science and Engineering
Wednesday, September 11, 13

databases
algorithms
arch
AI
OS
Comp
Sci

mobile
databases
algorithms
arch
AI
OS
Comp
Sci

mobile
databases
algorithms
arch
AI
OS
Comp
Sci
mhealth

what is mhealth?

stress check
glucose buddy
ﬁtness trainer
heart rate
zombie run

consider physician’s need

compliance?
cost?
doctor patient?

cost

compliance

what can the mobile phone
already sense ubiquitously?

accelerometer
gyroscope
magnetometer /compass
dual camera / ﬂash
1+ microphones
proximity sensor
capacitive sensor
gps
motorized actuator

accelerometer
gyroscope
magnetometer /compass
dual camera / ﬂash
1+ microphones
proximity sensor
capacitive sensor
gps
motorized actuator
compliance++;
cost--;
dr_pat *= 10;

what can the mobile phone
sense with clinical utility?

future
research
lung
function
jaundice

spirometer lung function??

spirometer
device that measures
amount of air inhaled and
exhaled.

lung function
asthma
COPD
cystic ﬁbrosis
evaluates pulmonary
impairments

using a spirometer
ﬂow
volume
volume
time

volume-time graph
volume
time

volume-time graph
volume
time
FEV1
FVC
FEV1: Forced Expiratory Volume in 1 second
FVC: Forced Vital Capacity

volume-time graph
volume
time1 sec.
FEV1
FVC

volume-time graph
volume
time1 sec.
FEV1
FVC
FEV1% = FEV1/FVC

FEV1% = FEV1/FVC

FEV1% = FEV1/FVC
> 80% healthy
60 - 79% mild
40 - 59% moderate
< 40% severe

ﬂow-volume graph
ﬂow
volume

ﬂow
volume
FEV1 FVC
1 sec.
PEF
PEF: Peak Expiratory Flow
ﬂow-volume graph

ﬂow
volume
normal
ﬂow-volume graph

ﬂow
volume
normal
obstructive
ﬂow-volume graph

obstructive diseases
resistance in air path leads to reduced air ﬂow

restrictive diseases
lungs are unable to pump enough air and pressure

ﬂow-volume graph
Flow
Volume
normal
obstructive

ﬂow-volume graph
Flow
Volume
normal
restrictive
obstructive

clinical spirometry

home spirometry

home spirometry
faster detection
rapid recovery
trending

home spirometry
high cost barrier
patient compliance
less coaching
limited integration
challenges with

ﬂow rate
volume
lung function
airﬂow
sensor

ﬂow rate
volume
lung function
airﬂow
sensor
sound
pressure
microphone

ﬂow rate
volume
lung function
airﬂow
sensor
sound
pressure
microphone processing
estimated

SpiroSmart
availability
cost
portability
more effective coaching interface
integrated uploading

Using SpiroSmart

Using SpiroSmart
]

study design
x 3
x 3

study enrollment

study enrollment
participants 52
18-75 years old, mostly healthy18-75 years old, mostly healthy
study a

study enrollment
participants 52
study a
participants 10
12-17 years old, mixed healthy/abnormal12-17 years old, mixed healthy/abnormal
study b

study enrollment
participants 52
study a
participants 10
12-17 years old, mixed healthy/abnormal12-17 years old, mixed healthy/abnormal
study b
participants 56
10-69 years old, mostly abnormal10-69 years old, mostly abnormal
study c
enrolledby
pulmonologists

audio

audio
ﬂow features

audio
ﬂow features
measures
regression
FEV1
FVC
PEF

0 1 2 3 4
0
5
10
15
Flow(L/s)
Volume(L)
1
2
3
4
Volume(L)
0 1 2 3 4
0
5
10
15
Flow(L/s) Volume(L)
0 2 4 6 8 10
0
1
2
3
4
time(s)
Volume(L)
audio
ﬂow features
measures
regression
curve
regression
FEV1
FVC
PEF

0 1 2 3 4
0
5
10
15
Flow(L/s)
Volume(L)
1
2
3
4
Volume(L)
0 1 2 3 4
0
5
10
15
Flow(L/s) Volume(L)
0 2 4 6 8 10
0
1
2
3
4
time(s)
Volume(L)
audio
ﬂow features
measures
regression
curve
regression
lung function
FEV1
FVC
PEF

0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
ﬂow features

0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
ﬂow features
vocal tractsource output
frequency(Hz)
1 2 3 4 5 6
0
500
1000
1500
2000
2500

0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
ﬂow features
0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
envelope detection
0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
resonance tracking
0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
lpc8raw
time(s)
amplitude
ﬂow estimation features
vocal tractsource output 0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
lpc8raw
time(s)
amplitude
auto-regressive estimate
frequency(Hz)
1 2 3 4 5 6
0
500
1000
1500
2000
2500

curve
regression
−1 0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
time(s)
featurevalue
0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
time(s)
featurevalue
feature 1
feature N
windowedmachine
learningregression
...

−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
curve
regression
−1 0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
time(s)
featurevalue
0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
time(s)
featurevalue
feature 1
feature N
curve
output
windowedmachine
learningregression
...

results
measures
regression

curve
regression
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)

curve
regression
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)

curve
regression
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)

curve
regression
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−2 0 2 4 6
0
5
10
15
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
volume(L)
flow(L/s)
−1 0 1 2 3 4
0
2
4
6
8
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
normal
minimal obstructive
mild obstructive
moderate obstructive
severe obstructive
restrictive
inadequate

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate
identical
64%

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate
one off
10%
identical
64%

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate false positive
14%
one off
10%
identical
64%

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate
false negative
4%
false positive
14%
one off
10%
identical
64%

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate
error
8%
false negative
4%
false positive
14%
one off
10%
identical
64%

results
normal
minimal obstructive
mild obstructive
severe obstructive
restrictive
inadequate
error
8%
false negative
4%
false positive
14%
one off
10%
identical
64%
abnormal vs normal
96%

appropriate for
screening and early diagnosis

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

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
TcB
Accurate
(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 40
0-4 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

051015
TcBvalues
0 5 10 15
bilicam values
Fitted values bilicam
bilirubinlevel
bilicam estimation
0
15
155 10
5
10
mg/dl
r=0.91
bilicam initial results

• near term: screening

• medium term: TSB study

• long term: developing world

• long term: developing world
“in many resource poor nations,
hyperbilirubinemia is the second or
third leading cause of infant
mortality and disability”

oxygen volume, VO2

blood pressure

intra ocular pressure

activity detection
elder care
daily activity logs
congestive heart failure
health markers via phone
blood pressure
pulse oximetry
stress
opportunistic sensing
pain management
detecting circulation

activity detection
elder care
daily activity logs
congestive heart failure
health markers via phone
blood pressure
pulse oximetry
stress
opportunistic sensing
pain management
detecting circulation
developing world

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!

Mobilizing mHealth: interdisciplinary computer science and engineering

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