Why seated postures?

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(No Transcript)
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Why seated postures?
Automobile
Classroom
Wheelchair
Home
Office
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Using posture information
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Existing approaches

• KinestheticMotion-capture markers or conductive-
  elastomer-embedded fabrics

Pellegrini and Iocchi., 2006
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Existing approaches

• KinestheticMotion-capture markers or conductive-
  elastomer-embedded fabrics
• Vision-basedImage sequences from a single camera
  or multiple cameras

Tognetti et al., 2005
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Existing approaches

• KinestheticMotion-capture markers or conductive-
  elastomer-embedded fabrics
• Vision-basedImage sequences from a single camera
  or multiple cameras
• Pressure-sensing-basedPressure readings from the
  seating surfaces

Han et al., 2001
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Challenges

• Poor generalizationGood performance in
  classifying familiar subjects, poor performance
  with unfamiliar subjects due to high
  dimensionality.
• High costHigh-fidelity pressure sensors are
  expensive.
• Slow performanceProcessing high-fidelity sensor
  data demands computational power, which leads to
  slow processing.

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Our solution

• Robust generalizationUp to 87 accuracy in
  classifying 10 postures with new subjects.
• Low costUsing 19 pressure sensors instead of
  4032. Reducing sensor cost from 3K to 100.
• Near-real-time performance10Hz on a standard
  desktop computer
• Novel methodologyUsing domain knowledge and
  near-optimal sensor placement.

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Methodology
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Learning Algorithm

• Logistic RegressionSparse representation
• Cross-validation10-fold, gender-balanced
  training and testing samples from different
  subjects
• Separate setsTraining, testing, and reporting
  samples from 52 people in 5 trials
• Implementation in Java

?

• We would like to thank Hong Tan and Lynne
  Slivovsky for providing their data set for
  comparison.

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Understanding pressure data
Modeling
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Understanding pressure data
Modeling
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Understanding our data
Modeling
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Domain knowledge
Modeling
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Features
Modeling
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Features
Modeling
Classification accuracy
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Separability test
Modeling
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Feature elimination
Modeling
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Methodology
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Sensor granularity
Dimensionality Reduction
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Sensor granularity
Dimensionality Reduction
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How to place sensors?
Dimensionality Reduction
F

• F, feature variables
• V, locations and granularities
• A subset A of V that maximizes information gain
  about F where H is entropy
• NP-Hard optimization problem
• We use near-optimal approximation algorithm

V
A ? V
IG(AF) H(F) - H(F A)
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Near-optimal placement
Dimensionality Reduction
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Sensor placements
Dimensionality Reduction
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Near-optimal placement
Dimensionality Reduction
Classification accuracy
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Methodology
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Prototyping
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Evaluation of prototype

• 20 naive participants10-fold cross validation
  testing with 5 of the data
• 78 accuracyIn classifying 10 postures
• 10 Hz real-time performanceOn a standard desktop
  computer

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Methodology
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Conclusions

• GeneralizabilityUp to 87 (with a base rate of
  10) achieved with unfamiliar subjects.
• Low costHigher classification accuracy than
  existing systems using less than 1 of the
  sensors. 100 sensor cost compared to the
  commercial sensor for 3K (33 times reduction in
  price).
• Near-real-time performanceAt 10Hz on a standard
  desktop computer.

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Applications
Automobile
Classroom
Wheelchair
Home
Office
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Future challenges
Next Steps

• Transferring learning across chairsA
  transformation map could be created
• Only static posturesTemporal dimension needs to
  be considered
• The set of ten posturesThe set of postures
  should come from the activity

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Summary of Contributions

• A non-intrusive, robust, low-cost system that
  recognizes seated postures with generalizable,
  near-real-time performance.
• A novel methodology that uses domain-knowledge
  and near-optimal sensor placement strategy for
  classification.

This work was supported by NSF grants
IIS-0121426, DGE- 0333420, CNS-0509383, Intel
Corporation and Ford Motor Company.
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From Postures to Activities
Next Steps

• Reading the paper
• Watching TV
• Reading paperwork
• Watching TV eating
• Sleeping
• Talking on the phone
• Reading a book
• Craftwork
• Reading the paper watching TV
• Reading the paper eating