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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
Physiological Data Modeling Contest
Introductory Remarks
An ICML-2004 Workshop, July 8, 2004
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Notes/outline
Welcome (overview of techniques tried, # entrants, etc, maybe 1 slide)Schedule (including order of teams)
Zodiac, DCTRI, NLM, INF, LRI, Gama, Amin, SS [ask Max about who wanted to be near edge]BodyMedia (maybe 15/20 slides) – when we started, what we do, Why the contest?Details of contest
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Schedule
8:30 Introduction8:50 Brown-Zodiac presentation9:15 DCRTI presentation9:35 National Library of Medicine10:00 Break10:30 UTexas - Amin10:50 CMU-Informedia11:15 Univ. of Porto - Gama11:35 Laboratoire de Recherche en Informatique12:00 Lunch14:00 Smart Signal14:25 Revelation of channel and annotation names14:40 Revelation of scores15:00 Discussion of results15:30 Break16:00 Awards presentation16:10 Brainstorming16:30 Break
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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
BodyMedia is a body monitoring solutions provider.
We make tools for continuous body monitoring.
5 Years Old
$20M in Venture Funding
Who We Are
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Our Mission
To be the leader in integrated products and information services that track and promote health and wellness through continuous, free-living, body monitoring.
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What we make.Hardware and software that…
Collects Stores Processes Represents
…continuous physiologic and lifestyle information
about people.
101010101010101010101010101010101010101010101 101010101010101010101010101010101010101010101 101010101010101010101010101010
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SenseWearTM Armband
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2-axis Accelerometer (inside)
Heart Beat Receiver Board (inside)
Timestamp Button
Heat Flux Sensor
Near-Body Ambient Temperature Sensor
GSR Sensors
Skin Temperature
What it monitors:
Acceleration (Motion)
Galvanic Skin Response (GSR)
Skin Temperature
Heat Flux
Heart Beats
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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
2-axis Accelerometer (inside)
Heart Beat Receiver Board (inside)
Timestamp Button
Heat Flux Sensor
Near-Body Ambient Temperature Sensor
GSR Sensors
Skin Temperature
What it monitors:
Acceleration (Motion)
Galvanic Skin Response (GSR)
Skin Temperature
Heat Flux
Heart Beats
Machine Learning
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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
2-axis Accelerometer (inside)
Heart Beat Receiver Board (inside)
Timestamp Button
Heat Flux Sensor
Near-Body Ambient Temperature Sensor
GSR Sensors
Skin Temperature
What it monitors:
Acceleration (Motion)
Galvanic Skin Response (GSR)
Skin Temperature
Heat Flux
Heart Beats
What we derive (today):
Total Energy Expenditure
Physical Activity Duration
Type of Physical Activity
(e.g. Resistance, Cardiovascular)
Number of Steps
Sleep Efficiency
Contexts: Sleep, lying down, sedentary, driving, ambulation, biking, other exercise
Machine Learning
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Low Motion High Motion
Aerobic Activity?
In a vehicle?Resistance Activity?
Sedentary?
Multi-Sensors for Disambiguation
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High Heat Flux
Low Heat Flux
Low Motion High Motion
Aerobic Activity
In a vehicle
Resistance Activity
Sedentary
Multi-Sensors for Disambiguation
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High Heat Flux
Low Heat Flux
Low Motion High Motion
Aerobic Activity
In a vehicle
Resistance Activity
Sedentary
FeverLow HR
High HR
Multi-Sensors for Disambiguation
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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
0
0.05
0.1
0.15
0.2
0.25
Ac
ce
lera
tio
n S
AD
(g
)
Accelerometer SAD
ClimbingStairs
Walk around block Climbing
Stairs
Multi-Sensors for Disambiguation
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0
5
10
15
20
25
30
35
Hea
t fl
ux
(W/m
^2)
0
0.05
0.1
0.15
0.2
0.25
Acc
eler
atio
n (
g)
Heat Flux
Accelerometer SAD
ClimbingStairs
Walk around block Climbing
Stairs
Multi-Sensors for Disambiguation
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Biking Driving
Office Running
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Accuracy VO2 ICC Model ICC
Treadmill 90.3% 0.96 0.88
Biking 93.7% 0.90 0.73
Arm Ergometer 95.4% 0.76 0.88
Stepping 91.5% 0.97 0.91
University of Pittsburgh Study
All results within 95% confidence interval
Example: Energy Expenditure – Accuracy
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Collect gold standarddata and wide array of high-rate sensor data
Create relevantcompressed datastreams
Build algorithm
EvaluateValidate externally
TrainingTesting
Algorithm Development Process:
Identify gaps, generalize to broader population
Develop context detectors
Sum()
Variance()
Peaks()
Pedometer()
Frequency()
By subject/lab CV
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Challenges/opportunities for us
• Our data is sequential– Can’t train for my afternoon data with my morning data– Can take advantage of dynamic information
• Many Gigabytes of data– More than a hundred thousand hours of labeled data. – Slow to churn through even with simple algorithms
• Despite all our data – there are many chances to overfit– We had only one person who did “spinning” in our dataset– Curse of dimensionality…
• Only one left-handed female with COPD who rides a bike only holding on with her left arm.
• Silver, Bronze, and Tin instead of Gold standards– Noise, poor annotation are problems– Can also be very difficult to obtain (e.g. heart attack data)
• Sensor noise
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Our Markets
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Armband
Glucose Meter
Pulse Oximeter
Blood Pressure Monitor
Weight Scale
Wireless Communication Gateway
Cellular/Two-way pager/Telephone Communication
BodyMedia’s Platform for Remote Healthcare
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Weight Management
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Diabetes Management
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Diabetes Management
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Scientific Research
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Scientific Research
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The Physiological Data Modeling Contest
• large amounts of data • sequential data • issues of sensor fusion• rich domain
– noise – hidden variables– context
• 3 domains– Gender– Very common activity– Common but not as common activity
• Only one left-handed female with COPD who rides a bike only holding on with her left arm.
• Silver, Bronze, and Tin instead of Gold standards– Noise, poor annotation are problems– Can also be very difficult to obtain (e.g. heart attack data)
• Sensor noise
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Why did we do the contest?
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The PDMC Contest
Details of contest and dataset, including size, tasks, number of entries, etc.
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Predictions of performance
We’re going to try to predict performance.
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