Transcript Smart Phone-Based Sensor Mining

Gary M. Weiss & Jeffrey W. Lockhart
Fordham University
{gweiss,lockhart}@cis.fordham.edu
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Biometrics concerns unique identification
based on physical or behavioral traits
 Hard biometrics relies on uniquely identifying traits
▪ Fingerprints, DNA, iris, etc.
 Soft biometric traits are not distinctive enough for
unique identification, but may help
▪ Physical traits: Sex, age, height, weight, etc.
▪ Behavioral traits: gait, clothes, travel patterns, etc.
8/21/2011
Gary M. Weiss
SensorKDD 2011
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In earlier work1 we showed that for 36 users we
were able to identify the correct user using
only accelerometer data:
 With a single 10 second walking sample: 84% - 91%
 With a 5-10 minute walking sample: 100%
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So if we can identify a user based on their
movements, maybe we can identify user traits
1
Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore. Cell Phone-Based Biometric Identification, Proceedings of the
IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems (BTAS-10), Washington DC.
8/21/2011
Gary M. Weiss
SensorKDD 2011
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To help identify a person (soft biometrics)
But do we have better uses for these “soft”
traits than for identification?
 As data miners, of course we do!
 We want to know everything we possibly can
about a person. Somehow we will exploit this.
8/21/2011
Gary M. Weiss
SensorKDD 2011
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Normally think about traits as being:
 Unchanging: race, skin color, eye color, etc.
 Slow changing: Height, weight, etc.
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But want to know everything about a person:
 What they wear, how they feel, if they are tired, etc.
 Our goal is to predict these too
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We have not seen this goal stated in context of
mobile sensor data mining.
8/21/2011
Gary M. Weiss
SensorKDD 2011
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Very little explicit work on this topic
 Some work related to biometrics but incidental
▪ Work on gait recognition mentions factors that
influence recognition, like weight of footwear & sex
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Other communities work in related areas
 Ergonomics & kinesiology study factors that
impact gait
▪ Texture of footwear, type of shoe, sex, age, heel height
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Gary M. Weiss
SensorKDD 2011
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Data collected from ~70 people
 Accelerometer data while walking
 Survey data includes anything we could think of
that might somehow be predictable:
▪
▪
▪
▪
Sex, height, weight, age, race, handedness, disability
Type of area grew up in {rural, suburban, urban}
Shoe size, footwear type, size of heels, type of clothing
# hours academic work , # hours exercise
 Too few subjects investigate all factors
▪ Many were not predictable (maybe with more data)
8/21/2011
Gary M. Weiss
SensorKDD 2011
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Accuracy
Male Female
71.2%
Male
31
7
Female
12
16
Accuracy Short
83.3%
Short
15
Tall
2
Tall
5
20
Accuracy
78.9%
Light
Heavy
Light
Heavy
13
2
7
17
Results for IB3 classifier. For height and weight middle categories removed.
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Gary M. Weiss
SensorKDD 2011
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Gary M. Weiss
SensorKDD 2011
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A wide open area for data mining research
 A marketers dream
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Clear privacy issues
Room for creativity & insight for finding traits
Probably many interesting commercial and
research applications
 Imagine diagnosing back problems via your
mobile phone via gait analysis …
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Gary M. Weiss
SensorKDD 2011
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Will collect data from hundreds of users
 Getting a diverse sample a bit difficult (on campus)
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Try to construct more useful features
Evaluate the ability to predict the dozens of
user traits that we track
 Have begun to track shoe type and heel size
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SensorKDD 2011
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