Physiological Data Modeling Contest

Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary

Physiological Data Modeling Contest

An ICML-2004 Workshop, July 8, 2004


Revelation of Contexts

• Context 1 – Annotation 3004

• Context 2 – Annotation 5102

Watching TV

Sleep


Revelation of Channels

characteristic1 agecharacteristic2 handednesssensor1 gsr_averagesensor2 heat_flux_averagesensor3 near_body_temp_averagesensor4 pedometersensor5 skin_temp_averagesensor6 longitudinal_accelerometer_SADsensor7 longitudinal_accelerometer_averagesensor8 transverse_accelerometer_SADsensor9 transverse_accelerometer_average


Explanation of Channels

• gsr_average– Measures sweat (conductivity across the skin)– Unit: micro Siemens

• heat_flux_average– Measures heat lost to the environment– Unit: Watts/meter^2

• near_body_temp_average– Measures temperature near armband– Unit: degrees Centigrade

• pedometer– Measures number of steps– Unit: steps

• skin_temp_average– Measures temperature of skin in contact with armband– Unit: degrees Centigrade


Explanation of Channels

• longitudinal_accelerometer_SAD– Measures Sum of Absolute Differences of vertical acceleration– Unit: g

• longitudinal_accelerometer_average– Measures average of vertical acceleration– Unit: g

• transverse_accelerometer_SAD– Measures Sum of Absolute Differences of horizontal acceleration– Unit: g

• transverse_accelerometer_average– Measures average of horizontal acceleration– Unit: g


Revelation of Set Distributions

Training Set Distribution

Test Set Distribution

Number of Men 12 21Number of Women 7 9Number of Men Sessions 1380 1635Number of Women Sessions 38 78Number of Sessions with TV 67 72Number of Sessions without TV 976 994Number of TV Minutes 4483 5813Number of non-TV Minutes 168037 191802Number of Sessions with Sleep 236 244Number of Sessions without Sleep 902 942Number of Minutes with Sleep 98532 103666Number of Minutes withouth Sleep 76717 95225


Brown – Zodiac

• Summary of Approaches

– Approach 1: No Chars

– Approach 2: 1 Char

– Approach 3: 2 Chars


Brown – Zodiac – Predictions

S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .7 0 .9C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .666666667 0 .766666667

• Approach 1: No Chars


Brown – Zodiac – Results

S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .7 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .6 0 .7 0 .3430C ontext 2 (s leep) 0 .7 0 .7 0 .8007O verall 0 .666666667 0 .766666667 0 .6045

• Approach 1: No Chars




• Approach 2: 1 Char




• Approach 3: 2 Chars


Zo d ia c

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

c ontex t1 (TV ) c ontex t2 (s leep) gender A vg

Sco

re

Zodiac 1 : D ec is ion Tree

Zodiac 2 : Log is t ic R egres s ion

Zodiac 3 : S V M

Brown – Zodiac – Summary


Daimler Chrysler Research And Technology India


– Approach 1: Raw Data

– Approach 2: PCA


Daimler Chrysler Research And Technology India – Predictions


• Approach 1: Raw Data


Daimler Chrysler Research And Technology India – Results


• Approach 1: Raw Data


Daimler Chrysler Research And Technology India – Predictions


• Approach 2: PCA


Daimler Chrysler Research And Technology India – Results


• Approach 2: PCA


D a im le rC h ry s le r R e s e a rc h A n d T e c h n o lo g y In d ia , B a n g a lo re

0.6763

0.8761

0.4645

0.6723

0.6253

0.7859

0.4645

0.6252

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000


Sco

re D C R TI 1 : R aw D ata

D C R TI 2 : P C A

Daimler Chrysler Research And Technology India – Summary


O v e ra ll S c o re

0.6723

0.6252

0.6045

0.5891 0.5885

0.5400

0.5600

0.5800

0.6000

0.6200

0.6400

0.6600

0.6800

D C R TI 1 : R aw D ata D C R TI 2 : P C A Zodiac 1 : D ec is ion Tree Zodiac 2 : Log is t icR egres s ion

Zodiac 3 : S V M

Sco

reLeader Board


National Library of Medicine


– Approach 1: mDSB 1

– Approach 2: mDSB 2

– Approach 3: Simple Bayesian


National Library of Medicine – Predictions


• Approach 1: mDSB 1


National Library of Medicine – Results






• Approach 3: Simple Bayesian


N a tio n a l L ib ra ry o f M e d ic in e

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000


Sco

re

N LM 1: m D S B 1

N LM 2: m D S B 2

N LM 3: S im ple B ay es ian

National Library of Medicine – Summary



0.7614

0.6959 0.69150.6723

0.62520.6045

0.5891 0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

N LM 3: S im pleB ay es ian

N LM 1: m D S B 1 N LM 2: m D S B 2 D C R TI 1 : R awD ata

D C R TI 2 : P C A Zodiac 1 :D ec is ion Tree

Zodiac 2 :Log is t ic

R egres s ion

Zodiac 3 : S V M

Sco

reLeader Board


University of Texas Austin – Saurabh Amin

• Summary of Approach


University of Texas Austin – Saurabh Amin – Predictions



University of Texas Austin – Saurabh Amin – Results



S a u ra b h A m in

0.7000

0.3065

0.7591

0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000


Sco

re A m in

University of Texas Austin – Saurabh Amin – Summary



0.7614

0.6959 0.69150.6723

0.62520.6045

0.5891 0.5885 0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

N LM 3:S im ple

B ay es ian

N LM 1: m D S B1

N LM 2: m D S B2

D C R TI 1 : R awD ata

D C R TI 2 : P C A Zodiac 1 :D ec is ion Tree

Zodiac 2 :Log is t ic

R egres s ion

A m in Zodiac 3 :S V M

Sco

reLeader Board


CMU – Informedia


– Approach 1: 50/50

– Approach 2: All Negative

– Approach 3: Semi-Supervised


CMU – Informedia – Predictions


• Approach 1: 50/50


CMU – Informedia – Results


• Approach 1: 50/50




• Approach 2: All Negative




• Approach 3: Semi-Supervised


In fo rm e d ia

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000


Sco

re

In fo rm edia 1 : S V M B as eline

In form edia 2 : A ll N eg.

In form edia 3 : F ilte r

CMU – Informedia – Summary



0.7620 0.7614 0.7584 0.7541

0.6959 0.69150.6723

0.62520.6045 0.5891 0.5885 0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Sco

reLeader Board


University of Porto – João Gama


• Approach: Ultra Fast Forrest of Trees


University of Porto – João Gama – Predictions

S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 0 .85C ontext 1 (w atc h ing T V ) 0 0 .825C ontext 2 (s leep) 0 0 .85O verall #D IV /0 ! 0 .841666667



University of Porto – João Gama – Results

S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 0 .85 0 .6248C ontext 1 (w atc h ing T V ) 0 0 .825 0 .6922C ontext 2 (s leep) 0 0 .85 0 .8357O verall #D IV /0 ! 0 .841666667 0 .7176



G a m a

0.6922

0.8357

0.6248

0.7176

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000


Sco

re G am a: U F F T

University of Porto – João Gama – Summary



0.7620 0.7614 0.7584 0.75410.7176

0.6959 0.69150.6723

0.62520.6045 0.5891 0.5885 0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Sco

reLeader Board


Laboratoire de Recherche en Informatique


– Approach 1: Voting Procedure

– Approach 2: Weighted Voting Procedure


Laboratoire de Recherche en Informatique – Predictions


• Approach 1: Voting Procedure


Laboratoire de Recherche en Informatique – Results


• Approach 1: Voting Procedure


Laboratoire de Recherche en Informatique – Predictions


• Approach 2: Weighted Voting Procedure


Laboratoire de Recherche en Informatique – Results


• Approach 2: Weighted Voting Procedure


L a b o ra to ire d e R e c h e rc h e e n In fo rm a tiq u e , U n iv e rs ité P a ris -S u d

0.6875

0.8187

0.6069

0.70440.6854

0.8275

0.5825

0.6985

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000


Sco

re LR I 1 : V ot ing

LR I 2 : W eighted V ot ing

Laboratoire de Recherche en Informatique – Summary



0.7620 0.7614 0.7584 0.75410.7176 0.7044 0.6985 0.6959 0.6915

0.67230.6252

0.6045 0.5891 0.5885 0.5885

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Sco

reLeader Board


Smart Signal



Smart Signal – Predictions



Smart Signal – Results



S m a rt S ig n a l

0.7498

0.8684

0.6614

0.7598

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000


Sco

re S m art S igna l

Smart Signal – Summary


Leader Board

?


Summary: Gender Prediction

G e n d e r

0.7591

0.6697 0.6697 0.6697 0.6697 0.66140.6361 0.6342 0.6324 0.6248 0.6236 0.6217 0.6069

0.5825

0.4645 0.4645

0.0000

0.1000

0.2000

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0.4000

0.5000

0.6000

0.7000

0.8000

Sco

re


Summary: Context1 (3004 – Watching TV)

C o n te x t 1 (T V )

0.7498 0.7375 0.7314 0.7310 0.7208 0.7049 0.7011 0.7000 0.6922 0.6875 0.6854 0.6763

0.6253

0.3430 0.3430 0.3430

0.0000

0.1000

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Sco

re


Summary: Context2 (5102 – Sleeping)

C o n te x t 2 (s le e p )

0.9125 0.9096 0.8988 0.8938 0.8761 0.86840.8357 0.8275 0.8187 0.8007 0.8007 0.8007 0.7859

0.7130 0.7036

0.3065

0.0000

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0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

Sco

re



0.7620 0.7614 0.7598 0.7584 0.75410.7176 0.7044 0.6985 0.6959 0.6915

0.67230.6252

0.6045 0.5891 0.5885 0.5885

0.0000

0.1000

0.2000

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0.9000

Sco

reSummary: Overall ResultsSummary: Overall Results


Awards

• Honorable Mention: Gender– Saurabh Amin (0.7591)

• Honorable Mention: Context 1 (watching TV)– Smart Signal (0.7498)

• Honorable Mention: Context 2 (sleep)– Informedia 3: Semi-Supervised (0.9125)

• Overall Winner:– Informedia 3: Semi-Supervised (0.7620)

Physiological Data Modeling Contest

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