copyright bodymedia, inc. © 2004, confidential and proprietary physiological data modeling contest...

Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary

Physiological Data Modeling Contest

Introductory Remarks

An ICML-2004 Workshop, July 8, 2004


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


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


BodyMedia is a body monitoring solutions provider.

We make tools for continuous body monitoring.

5 Years Old

$20M in Venture Funding

Who We Are


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.


What we make.Hardware and software that…

Collects Stores Processes Represents

…continuous physiologic and lifestyle information

about people.

101010101010101010101010101010101010101010101 101010101010101010101010101010101010101010101 101010101010101010101010101010


SenseWearTM Armband


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




Timestamp Button

Heat Flux Sensor


GSR Sensors

Skin Temperature

What it monitors:



Skin Temperature

Heat Flux

Heart Beats

Machine Learning




Timestamp Button

Heat Flux Sensor


GSR Sensors

Skin Temperature

What it monitors:



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


Low Motion High Motion

Aerobic Activity?

In a vehicle?Resistance Activity?

Sedentary?

Multi-Sensors for Disambiguation


High Heat Flux

Low Heat Flux


Aerobic Activity

In a vehicle

Resistance Activity

Sedentary



High Heat Flux

Low Heat Flux


Aerobic Activity

In a vehicle

Resistance Activity

Sedentary

FeverLow HR

High HR



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



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



Biking Driving

Office Running


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


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


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


Our Markets


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



Weight Management


Diabetes Management


Scientific Research


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


Why did we do the contest?


The PDMC Contest

Details of contest and dataset, including size, tasks, number of entries, etc.


Predictions of performance

We’re going to try to predict performance.

copyright bodymedia, inc. © 2004, confidential and proprietary physiological data modeling contest...

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