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Why seated postures?

Why seated postures?. Automobile. Classroom. Wheelchair. Home. Office. Today’s talk. Using posture information. Existing approaches. Kinesthetic Motion-capture markers or conductive- elastomer-embedded fabrics. Pellegrini and Iocchi., 2006. Existing approaches.

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Why seated postures?

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  1. Why seated postures? Automobile Classroom Wheelchair Home Office

  2. Today’s talk Using posture information

  3. Existing approaches • KinestheticMotion-capture markers or conductive- elastomer-embedded fabrics Pellegrini and Iocchi., 2006

  4. Existing approaches • KinestheticMotion-capture markers or conductive- elastomer-embedded fabrics • Vision-basedImage sequences from a single camera or multiple cameras Tognetti et al., 2005

  5. 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

  6. Robust generalization Low-cost Near-real-time performance 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.

  7. 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.

  8. Methodology

  9. 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.

  10. Modeling Understanding pressure data

  11. Modeling Understanding pressure data

  12. Modeling Understanding our data

  13. Modeling Domain knowledge

  14. Size and position of bounding boxes Distance and angle to between bounding boxes Parameters of the ellipses that fit the bottom area Pressure applied to the bottom area Distances to the edges of the seat Modeling Features

  15. Modeling Features Classification accuracy

  16. Modeling Separability test

  17. Modeling Feature elimination

  18. Methodology

  19. Dimensionality Reduction Sensor granularity

  20. Dimensionality Reduction Sensor granularity

  21. Dimensionality Reduction How to place sensors? 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(A;F) = H(F) - H(F | A)

  22. Dimensionality Reduction Near-optimal placement

  23. Dimensionality Reduction Sensor placements

  24. Dimensionality Reduction Near-optimal placement Classification accuracy

  25. Methodology

  26. Prototyping

  27. 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

  28. Methodology

  29. 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.

  30. Applications Automobile Classroom Wheelchair Home Office

  31. Next Steps Future challenges • 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

  32. 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.

  33. Next Steps From Postures to Activities • 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

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