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2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning
 

2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning

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2011 06 01

2011 06 01
(uned)
emadrid
aelsaddik
uottawa
tangible objects for e learning

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  • Pinching is also a form of posture, whereas sign language is a good example of a gesture

2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning 2011 06 01 (uned) emadrid aelsaddik uottawa tangible objects for e learning Presentation Transcript

  • Tangible Objects for E-Learning Abdulmotaleb El Saddik FIEEE, FCAE, FEIC
  •  
  • At a glance Creation of Advanced Multimedia LO Protection of LO Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
  • Introduction
    • Haptics
      • Derived from the Greek verb “haptesthai”
      • Refers to sensing and manipulation through touch
      • Comprises both “Tactile” and “Kinesthetic” senses
    • Applications
      • Education and Training
      • Medicine: Tele-surgery, rehabilitation, etc.
      • Entertainment
      • Arts and Design
  • Multimedia and Authoring
    • Multimedia contents
      • Graphic images
      • 3D models
      • Audio and video files
      • And recently haptic stimuli
    • Multimedia authoring tools
      • Integrate the disparate media elements into a cohesive multimedia application
  • Multimedia Authoring Tools
    • A major challenges in multimedia authoring is to make authoring complex multimedia titles as easy as using a word processor or drawing program
    • We propose a multimedia authoring tool for hapto-visual applications to make the development process a non-programming experience
  • HAMLAT
    • Blender:
      • Open source
      • Full-fledged 3D graphical renderer
      • Physics and game engine
      • Adaptive user-interface
    • Plug-ins incorporated in Blender source code
      • Haptic panel in Blender GUI
      • Haptic rendering extension
      • Implemented in C++ using OpenHaptics and OpenGL
  • HAMLAT Implementation (demo)
  • A Multimedia Handwriting Learning and Evaluation Tool
    • Learning sensorimotor skills is difficult with graphical or auditory feedback
    • Sense of touch will be a must for physical guidance
    • Examples of physical guidance learning:
      • Handwriting for visually impaired people
      • Medical procedures and rehabilitation training
      • Painting/sculpting techniques
  • Multimedia Learning Tool
    • Distinguished features
        • Language choice: Arabic, French, English, Japanese and Spanish
        • Multimodal: haptic, visual, and audio information for each letter
        • Learning mode choice: full or partial guidance, or test mode.
        • Evaluation Mode: quantitative evaluation using dynamic time warping
  • Multimedia Learning Tool
  • Learning Mode -1-
    • Language Repository
      • Including symbols images, pronunciation files, and the haptic stimuli of each letter (using XML-based schema)
  • Learning Mode -2-
    • Haptic player: Three modes of operation:
      • No guidance
      • Partial guidance
      • Full guidance
  • Evaluation Mode -1-
    • Test GUI
  • Evaluation Mode -2-
    • Dynamic Time Warping
      • Measure the similarity between test and reference handwritings
      • Tolerate both global and local shifts in the time domain
      • Startup position of the test pattern is shifted to that of the reference pattern
    Given two time series // x i is a point in the test trajectory X = x 0 , x 1 , …, x N-1 // y i is a point in the ref. trajectory Y = y 0 , y 1 , …, y T-1 D(x i ,y i ) = sqrt((abs(x i )-abs(y i ))^2 + (ord(x i )-ord(y i ))^2) DTW[0,0] = D(x 0 ,y 0 ) For i=1 to T-1 DTW[0,i] = D(x 0 ,y i ) + DTW[0,i-1] For i=1 to N-1 DTW[i,0] = D(x i ,y 0 ) + DTW[i-1,0] For i=1 to N-1 For j=1 to T-1 DTW[i,j]=D(x i ,y j )+min{DTW[i,j-1],DTW[i-1,j],DTW[i-1,j-1]} Return (DTW[N-1,T-1])
  • Performance Evaluation -1-
    • Experimental Setup
      • The Phantom Omni Device
      • Application is developed with .NET 2.0 Framework and programmed using C#.
      • Bipolar grading scheme (Pass/fail)
      • Threshold distance is computed based on subjective analysis
      • Three threshold values were estimated:
        • Difficult
        • Medium
        • Easy
  • Performance Evaluation -2-
    • The DTW distance for the “Tha” Arabic character
  • Performance Evaluation -3-
    • The DTW distance for the “i” Japanese character
  • Performance Evaluation -4- (Demo) The DTW distances for five Japanese characters Character Threshold Distance (x10 3 mm) Easy Medium Difficult Sa 5.5 2.5 1 Shi 4 1 0.5 Chi 5 2.5 1 Tsu 4 1.5 1 Na 10 6 1
  • At a glance Creation of Advanced Multimedia LO Protection of LO Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
  • Haptic signal prediction, compression, and watermarking Watermark haptic signals to protect copyright and authenticate content.
  • Case Study: Identifying Human Pattern with Haptics The Experiment Methodology Dynamic Time Warping : + Nelder-Mead non-linear minimization Spectral analysis: Fast Fourier Transform Unsupervised Method: K-Means Graphic Representation Data Trial Timestamp Position X Position Y … Force X ( N) 1 0 0.23344 0.56768 0.00456 1 … .. … … 1 0.0123090 0.37676 0.98976 0,03767
  • Verifying such feasibility ( demo ) Virtual Check Virtual Mobile Phone Performance Ambient Intelligent Engine
  • At a glance Creation of Advanced Multimedia LO Protection of LO by Digital Watermarking Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
  • Navigation
      • Tangible Objects: Demo1
      • Haptics: Demo 2
      • Mobile Phone: Demo 3
  • At a glance Creation of Advanced Multimedia LO Protection of LO by Digital Watermarking Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
    • E-Learning courses
    • Toddler Interfaces
  • Toddler Interfaces
    • Main idea
      • Whenever a child utters an object’s name or taps on an object, a set of appropriate multimedia representations is displayed (i.e. images, Multi-lingual texts, audio…)
    • Techniques investigated
      • Voice-based technique (Speech detection)
      • Magic Stick technique (RFID and Bluetooth technologies)
    • Evaluation
      • Several games were developed: i.e., Book game, Alphabet Learning, and Object Identification
  • Alphabet Learning Evaluation
    • 12 children, ages 4-5, evaluated the system
    • We Observed the following:
      • Children’s discussions
      • Their Writings
      • Their Questions
      • Simplicity of using the Magic Stick.
    Demo 1 Demo 2
  • At a glance Creation of Advanced Multimedia LO Protection of LO by Digital Watermarking Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
  • Clustering and 3D Mapping Semantic keyword metrics MST tree Overall clustering process design
  • Searching/Sharing Learning Objects
  • Sample Interface: Dynamic Traffic Layout
  • Multimodal Interaction ( demo )
  • At a glance Creation of Advanced Multimedia LO Protection of LO by Digital Watermarking Navigation of LOR by VR metaphors Adaptation of LO according to user context/profile & network QoS LO Delivery to user
  • Last thoughts
  • Acknowledgment
  • October 4, 2005 Dankie WAD MAHAD SAN TAHAY GADDA GUEY Ευχαριστώ Urakoze Asante متشکرم DMnvwd go raibh maith agaibh