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Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
Haptics   Final Project Presentation
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Haptics Final Project Presentation

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Transcript

  • 1. Haptics Final Project: Using a Sensor Glove to Write in the Air Paul Taele Spring 2008
  • 2. Goals
    • Write stuff in the air without a pen.
  • 3. Initial Gestures
  • 4. Original Posture Classifier Setup
    • Tools:
      • P5
      • CyberGlove
    • Posture Classifiers:
      • k-Nearest Neighbor
      • Naïve Bayes
      • Neural Network
  • 5. Postures - Results
    • P5
      • NB: 10%
      • kNN: 50%
      • NN: 70%
    • CyberGlove
      • NN: 75% (all 23 sensors)
      • NN: 100% (3 index finger sensors)
  • 6. Postures - Analysis
    • Desired 100% for posture classification.
    • Used CyberGlove device and Neural Network classifier for postures.
    • Used two easily separable gestures instead of four.
  • 7. Hand Gesture Segmentation
    • Simple for two very separable gestures.
    • Classify each time state in an instance using the trained NN.
  • 8. Final Project Setup
    • Tools: CyberGlove
    • Language: Java
    • Posture Classifier: Neural Network
    • Sketch Classifier: $1
    • # of Postures: 2
    • # of Gestures: 4
  • 9. Final Postures
  • 10. Final Gestures
  • 11. Training Data ($1)
    • Created templates from 3 users.
    • Each user gave 5 examples for each sketch gesture.
  • 12.  
  • 13.  
  • 14.  
  • 15.  
  • 16. Test Data ($1)
    • Data was tested on consecutively-inputted sketch gesture.
    • Postures first extracted from gesturing stream.
    • Time points of those postures used to classify sketch gestures.
  • 17. Target: Circle -> Triangle Actual: Rectangle -> Triangle
  • 18. Target: Rectangle -> Circle Actual: Rectangle -> Rectangle
  • 19. Target: Triangle -> X Actual: X -> X
  • 20. Target: X -> Rectangle Actual: Rectangle -> X
  • 21. Conclusion
    • $1 sucks.

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