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Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
Physiological Data Modeling Contest
An ICML-2004 Workshop, July 8, 2004
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Revelation of Contexts
• Context 1 – Annotation 3004
• Context 2 – Annotation 5102
Watching TV
Sleep
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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
Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
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
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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
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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
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Brown – Zodiac
• Summary of Approaches
– Approach 1: No Chars
– Approach 2: 1 Char
– Approach 3: 2 Chars
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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
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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
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Brown – Zodiac – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .85 0 .8C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .716666667 0 .733333333
• Approach 2: 1 Char
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Brown – Zodiac – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .85 0 .8 0 .6236C 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 .716666667 0 .733333333 0 .5891
• Approach 2: 1 Char
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Brown – Zodiac – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .925C ontext 1 (w atc h ing T V ) 0 .6 0 .7C ontext 2 (s leep) 0 .7 0 .7O verall 0 .75 0 .775
• Approach 3: 2 Chars
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Brown – Zodiac – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .925 0 .6217C 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 .75 0 .775 0 .5885
• Approach 3: 2 Chars
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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
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Daimler Chrysler Research And Technology India
• Summary of Approaches
– Approach 1: Raw Data
– Approach 2: PCA
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Daimler Chrysler Research And Technology India – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .9 0 .9O verall 0 .766666667 0 .766666667
• Approach 1: Raw Data
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Daimler Chrysler Research And Technology India – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6 0 .4645C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6763C ontext 2 (s leep) 0 .9 0 .9 0 .8761O verall 0 .766666667 0 .766666667 0 .6723
• Approach 1: Raw Data
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Daimler Chrysler Research And Technology India – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6C ontext 1 (w atc h ing T V ) 0 .85 0 .85C ontext 2 (s leep) 0 .92 0 .92O verall 0 .79 0 .79
• Approach 2: PCA
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Daimler Chrysler Research And Technology India – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .6 0 .6 0 .4645C ontext 1 (w atc h ing T V ) 0 .85 0 .85 0 .6253C ontext 2 (s leep) 0 .92 0 .92 0 .7859O verall 0 .79 0 .79 0 .6252
• Approach 2: PCA
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
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
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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
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National Library of Medicine
• Summary of Approaches
– Approach 1: mDSB 1
– Approach 2: mDSB 2
– Approach 3: Simple Bayesian
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .8C ontext 2 (s leep) 0 .75 0 .8O verall 0 .8 0 .833333333
• Approach 1: mDSB 1
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .75 0 .8 0 .7049C ontext 2 (s leep) 0 .75 0 .8 0 .7130O verall 0 .8 0 .833333333 0 .6959
• Approach 1: mDSB 1
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .8C ontext 2 (s leep) 0 .75 0 .8O verall 0 .8 0 .833333333
• Approach 2: mDSB 2
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .75 0 .8 0 .7011C ontext 2 (s leep) 0 .75 0 .8 0 .7036O verall 0 .8 0 .833333333 0 .6915
• Approach 2: mDSB 2
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National Library of Medicine – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .75C ontext 2 (s leep) 0 .8 0 .75O verall 0 .833333333 0 .8
• Approach 3: Simple Bayesian
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National Library of Medicine – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6697C ontext 1 (w atc h ing T V ) 0 .8 0 .75 0 .7208C ontext 2 (s leep) 0 .8 0 .75 0 .8938O verall 0 .833333333 0 .8 0 .7614
• Approach 3: Simple Bayesian
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
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
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O v e ra ll S c o re
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
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University of Texas Austin – Saurabh Amin
• Summary of Approach
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University of Texas Austin – Saurabh Amin – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .51 0 .65C ontext 1 (w atc h ing T V ) 0 .689 0 .75C ontext 2 (s leep) 0 .689 0 .8O verall 0 .629333333 0 .733333333
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University of Texas Austin – Saurabh Amin – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .51 0 .65 0 .7591C ontext 1 (w atc h ing T V ) 0 .689 0 .75 0 .7000C ontext 2 (s leep) 0 .689 0 .8 0 .3065O verall 0 .629333333 0 .733333333 0 .5885
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re A m in
University of Texas Austin – Saurabh Amin – Summary
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O v e ra ll S c o re
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
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CMU – Informedia
• Summary of Approaches
– Approach 1: 50/50
– Approach 2: All Negative
– Approach 3: Semi-Supervised
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .75C ontext 2 (s leep) 0 .8711 0 .85O verall 0 .857033333 0 .833333333
• Approach 1: 50/50
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6342C ontext 1 (w atc h ing T V ) 0 .75 0 .75 0 .7314C ontext 2 (s leep) 0 .8711 0 .85 0 .9096O verall 0 .857033333 0 .833333333 0 .7584
• Approach 1: 50/50
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .7625 0 .75C ontext 2 (s leep) 0 .8834 0 .85O verall 0 .8653 0 .833333333
• Approach 2: All Negative
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6324C ontext 1 (w atc h ing T V ) 0 .7625 0 .75 0 .7310C ontext 2 (s leep) 0 .8834 0 .85 0 .8988O verall 0 .8653 0 .833333333 0 .7541
• Approach 2: All Negative
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CMU – Informedia – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9C ontext 1 (w atc h ing T V ) 0 .75 0 .75C ontext 2 (s leep) 0 .8711 0 .85O verall 0 .857033333 0 .833333333
• Approach 3: Semi-Supervised
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CMU – Informedia – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .95 0 .9 0 .6361C ontext 1 (w atc h ing T V ) 0 .75 0 .75 0 .7375C ontext 2 (s leep) 0 .8711 0 .85 0 .9125O verall 0 .857033333 0 .833333333 0 .7620
• Approach 3: Semi-Supervised
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
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
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O v e ra ll S c o re
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
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University of Porto – João Gama
• Summary of Approach
• Approach: Ultra Fast Forrest of Trees
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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
• Approach: Ultra Fast Forrest of Trees
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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
• Approach: Ultra Fast Forrest of Trees
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re G am a: U F F T
University of Porto – João Gama – Summary
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O v e ra ll S c o re
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
Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
Laboratoire de Recherche en Informatique
• Summary of Approaches
– Approach 1: Voting Procedure
– Approach 2: Weighted Voting Procedure
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Laboratoire de Recherche en Informatique – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .85 0 .85O verall 0 .85 0 .85
• Approach 1: Voting Procedure
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Laboratoire de Recherche en Informatique – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .6069C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6875C ontext 2 (s leep) 0 .85 0 .85 0 .8187O verall 0 .85 0 .85 0 .7044
• Approach 1: Voting Procedure
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Laboratoire de Recherche en Informatique – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9C ontext 1 (w atc h ing T V ) 0 .8 0 .8C ontext 2 (s leep) 0 .85 0 .85O verall 0 .85 0 .85
• Approach 2: Weighted Voting Procedure
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Laboratoire de Recherche en Informatique – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .9 0 .5825C ontext 1 (w atc h ing T V ) 0 .8 0 .8 0 .6854C ontext 2 (s leep) 0 .85 0 .85 0 .8275O verall 0 .85 0 .85 0 .6985
• Approach 2: Weighted Voting Procedure
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re LR I 1 : V ot ing
LR I 2 : W eighted V ot ing
Laboratoire de Recherche en Informatique – Summary
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O v e ra ll S c o re
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
Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
Smart Signal
• Summary of Approach
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Smart Signal – Predictions
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .925C ontext 1 (w atc h ing T V ) 0 .6 0 .825C ontext 2 (s leep) 0 .6 0 .85O verall 0 .7 0 .866666667
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Smart Signal – Results
S elf P red ic tion G roup P red ic tion A c tual S c oreG ender 0 .9 0 .925 0 .6614C ontext 1 (w atc h ing T V ) 0 .6 0 .825 0 .7498C ontext 2 (s leep) 0 .6 0 .85 0 .8684O verall 0 .7 0 .866666667 0 .7598
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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
c ontex t1 (TV ) c ontex t2 (s leep) gender A vg
Sco
re S m art S igna l
Smart Signal – Summary
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Leader Board
?
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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
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
Sco
re
Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
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
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
Sco
re
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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
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
Sco
re
Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary
O v e ra ll S c o 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
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
Sco
reSummary: Overall ResultsSummary: Overall Results
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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)
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