Ubicomp2012 spiro smartpresentation

Using a Microphone to Measure Lung Function
on a Mobile Phone
SpiroSmart
University of Washington
Eric Larson, Mayank Goel,
Gaetano Borriello, Sonya Heltshe, Margaret Rosenfeld, Shwetak Patel
UbiComp Lab
Friday, September 7, 12

What is SpiroSmart?
A smartphone based spirometer
that leverages on-device
microphone to help you keep track
of your lung function.

Performance
As good as a home spirometer
Error within 5.1% in a study with 52 participants

Performance
As good as a home spirometer
Error within 5.1% in a study with 52 participants
> $2000

What is SpiroSmart?
that helps you keep track of your
lung function.

What is SpiroSmart?
that helps you keep track of your
lung function.lung function
spirometer

Spirometer?
Device that measures
amount of air inhaled and
exhaled.

Lung Function?
For example:
Asthma
COPD
Cystic Fibrosis
Chronic Bronchitis
Evaluates severity of
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

Volume-Time Graph
FEV1% = FEV1/FVC

Volume-Time Graph
FEV1% = FEV1/FVC
> 80% Healthy
60 - 79% Mild
40 - 59% Moderate
< 40% Severe

Flow-Volume Graph
Flow
Volume

Flow-Volume Graph
Flow
Volume
FEV1 FVC
PEF
PEF: Peak Expiratory Flow

Flow-Volume Graph
Flow
Volume
FEV1 FVC
1 sec.
PEF
PEF: Peak Expiratory Flow

Flow-Volume Graph
Flow
Volume
Normal

Flow-Volume Graph
Flow
Volume
Normal
Obstructive

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
Restrictive
Obstructive

Clinical Spirometery

Home Spirometery

Home Spirometery
Faster detection
Rapid recovery

Home Spirometery
High cost barrier
Patient compliance
Less coaching
Limited feedback
Challenges with

SpiroSmart
Availability
Cost
Portability
More effective coaching interface
Integrated uploading

Using SpiroSmart

Using SpiroSmart
]

Initial Study Design
x 3
x 3

Need of attachments

Mouthpiece

Study Design
x 3
x 3

+
Study Design
x 3 x 3 x 3
+ +
x 3
x 3

dataset
+ + +

subjects 52
duration 45 minutes
ﬁrst session 29
abnormals 12
revisits 10
dataset
+ + +

dataset

dataset
ﬂow features

dataset
ﬂow features
measures
regression

dataset
ﬂow features
measures
regression
FEV1
FVC
PEF

dataset
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)
dataset
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)
dataset
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
0 1 2 3 4 5 6 7
−1
−0.5
0
0.5
1
time(s)
amplitude
envelope detection

time(s)
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

time(s)
frequency(Hz)
1 2 3 4 5 6
0
500
1000
1500
2000
2500
time(s)
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

time(s)
frequency(Hz)
1 2 3 4 5 6
0
500
1000
1500
2000
2500
time(s)
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
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
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
ﬁltersource output

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
estimated

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
auto-regressive estimate
estimated

ﬂow features
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)

ﬂow features
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
ground truth
spirometer

ﬂow features
0 1 2 3 4 5
0
0.1
0.2
0.3
0.4
time(s)
featurevalue
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
ground truth
spirometer
feature 1

ﬂow features
−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
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
ground truth
spirometer
feature 1
feature 2

ﬂow features
−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
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
ground truth
spirometer
−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 2

lung function
dataset
ﬂow features
measures
regression
curve
regression

−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)
−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
measures
regressionground truth
feature 1
feature 2

−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s) measures
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
ground truth
feature 1
feature 2

−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s) measures
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
7.1
0.33
0.35
PEF featuresground truth
feature 1
feature 2

−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s) measures
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
7.1
0.33
0.35
3.2
0.12
0.17
FEV1 features
PEF featuresground truth
feature 1
feature 2

measures
regression
FEV1 features PEF features

measures
regression
FEV1 features
bagged
decision tree
PEF features
bagged
decision tree

measures
regression
FEV1 features
bagged
decision tree
output
PEF features
bagged
decision tree
output

resultslung function
measures
regression
Mean%Error
Lower is
better

measures
regression
FEV1 FVC FEV1% PEF
0
1
2
3
4
5
6
7
Mean%Error
Lower is
better

measures
regression
FEV1 FVC FEV1% PEF
0
1
2
3
4
5
6
7
No Personalization
Personalization
Mean%Error
Lower is
better

measures
regression
Average Error = 5.1%
ATS Criteria = 5-7%

measures
regression
Average Error = 5.1%
ATS Criteria = 5-7%
normal
Attachments have no effect on Error

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 2

−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 2
curve
output

curve
regression
bagged
decision tree
−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

curve
regression
bagged
decision tree
−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
CRF

curve
regression
bagged
decision tree
−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
CRF
−1 0 1 2 3 4 5
0
2
4
6
8
time(s)
flow(L/s)

example curvescurve
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)

example curvescurve
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)

example curvescurve
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)

example curvescurve
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)

evaluationcurve
regression
lung function

evaluationcurve
regression
10 subjects curves
lung function

evaluationcurve
regression
5 pulmonologists
10 subjects curves
lung function

evaluationcurve
regression
5 pulmonologists
10 subjects curves
unaware if from SpiroSmart / Spirometer
lung function

surveycurve
regression
lung function

results
curve
regression
normal
minimal obstructive
mild obstructive
moderate obstructive
severe obstructive
restrictive
inadequate
lung function
identical
32 / 50

results
curve
regression
normal
minimal obstructive
mild obstructive
moderate obstructive
severe obstructive
restrictive
inadequate
lung function
identical
one off
32 / 50
5 / 18

limitations

limitations
no inhalation
quiet surroundings

limitations
no inhalation
quiet surroundings
coaching

limitations
no inhalation
quiet surroundings
coaching
smartphone

limitations
no inhalation
quiet surroundings
coaching
smartphone
trends over time

current work
no inhalation
quiet surroundings
coaching
smartphone
trends over time

current work
asthma study underway
no inhalation
quiet surroundings
coaching
smartphone
trends over time

current work
incentive graphicsno inhalation
quiet surroundings
coaching
smartphone
trends over time

current work
incentive graphics
air quality, non-chronic disease
no inhalation
quiet surroundings
coaching
smartphone
trends over time

current work
incentive graphics
air quality, non-chronic disease
call in service
no inhalation
quiet surroundings
coaching
smartphone
trends over time

call in service

Using a Microphone to Measure Lung Function
on a Mobile Phone
SpiroSmart
eclarson.com
eclarson@uw.edu
mayankgoel.com
mayankg@uw.edu
University of Washington
Eric Larson, Mayank Goel,
Gaetano Borriello, Sonya Heltshe, Margaret Rosenfeld, Shwetak Patel
UbiComp Lab

backup - curves
quantitative

backup - bland altman
igure 6. Cumulative error plot of the percentage of the results (y-axis) within the percent error of the x-axis, shown for “normal,”
abnormal” and “personalized abnormal” groups. Also shown are the accuracies for each measure, defined as the percentage of
measures within accepted clinical limits (i.e., the variation one would expect on a traditional spirometer).
ature is taken. For FEV1, the integration of the features
uring the first second is used (Figure 5).
egression is implemented using bagged decision trees and
ean square error; 100 trees are used in each forest. Each
aining subset is used to predict lung function for a given
st instance, resulting in an ensemble of predictions. The
nal decision is made by clustering the ensemble using k-
eans (k=2). The centroid of the cluster with the most in-
ances is the final prediction of PEF, FEV1, or FVC.
urve Shape Regression: The shape of the curve is a more
fficult and involved regression. Instead of a single meas-
roSmart and a clinical spirometer. Based on our evaluation
we conclude that SpiroSmart will meet the needs of home
lung function monitoring.
Estimate of Lung Function Measures
We breakdown the comparison of measurements from Spi
roSmart and a clinical spirometer by how the percent erro
is distributed and how well this conforms to accepted clini
cal variances in each measure.
Distribution of Percent Error in Lung Function Measures
The curves in Figure 6 present the cumulative percentage
error plots for FVC, FEV1, PEF, and FEV1/FVC. Explana
e 7. Bland Altman plots of percent error between SpiroSmart and a clinical spirometer versus the value obtained from th
al spirometer. th th
percentiles (short dashes) are also shown. Outliers in each plot are tFriday, September 7, 12

backup - mouthpiece
sling
Figure 8. Average percent error for each configuration.
level of agreement and reproducibility between spirometry
measures is highly correlated with how experienced the
subject is at performing the tests [22]. The only other varia-
ble suggestive of being associated with bias magnitude
(p<0.1) was mouthpiece use.

backup - survey table
Curve
Range of Diagnoses Within Rater
Spirometer SpiroSmart Agree One-off
1 Norm-Obs. (Mild) Norm-Obs. (Min.) 3 1
2 Norm-Obs. (Mild) Norm-Obs. (Mild) 3 1
3 Normal Norm-Obs. (Mild) 3 1
4 Normal Normal 5 0
5 Normal Normal 5 0
6 Obs. (Min.-Mod.) Restrictive 0 0
7 Obs. (Mild-Mod.) Norm-Obs. (Mild) 0 2
8 Insuff.-Norm Insuff.-Norm 3 0
9 Normal Normal 5 0
Normal Normal 5 0
Total N/A N/A 32/50 5/18
Table 2. Summary of survey responses from 5 pulmonologists.
Also shown are the number of times a pulmonologist’s diagno-
Fig-
ction
ions.
sig-
nifi-
piece
For
error
This
re of
on—
Im-
ixed
udiesFriday, September 7, 12

backup - features after
gone through phone

backup -
• what else?
•

Ubicomp2012 spiro smartpresentation

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