Mobile Interaction Based on Human Gesture Analysis

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    Mobile Interaction Based on Human Gesture Analysis - Presentation Transcript

    1. Mobile Interaction Based on Human Gesture Analysis Ricardo Gamboa Tiago Guerreiro, Joaquim Jorge {rjssg,tjvg,jaj}@immi.inesc-id.pt Hugo Gamboa [email_address]
    2. Actual Interaction Successful & Appropriate?
    3. Screen and Keypad size are Limited
    4. Multi-Task Graphical-based devices
    5. Slow and Visually Demanding Interaction Mobility Issues!
    6. Possible solution Key Shortcuts
    7. Memory Issues – Where is each shortcut?
    8. Other Solution Voice shortcuts
    9. Voice Recognition Issues
    10. Low Social Acceptance
    11. Proposed Solution Gestural Interface!
    12. Gestures ease Communication
    13. Task Analysis - User observation Actual panorama on mobile usage and shortcutting habits. Results proved: Mobile Interaction is “keystroke consuming” Shortcuts are ineffective.
    14. Propposed Approach – Mnemonical Body Shortcuts “ Phone in Silence Mode”
    15. Propposed Approach – Mnemonical Body Shortcuts “ Phone your Girlfriend”
    16. Related Work RFID Accelerometers EMG Cameras Touch Screens
    17. RFID Prototype Pocket LOOX 720 ACG RF RFID reader RFID Tags
    18. Evaluation
    19. Mnemonical Body Shortcuts Evaluation Mouth Hand Chest Head Wrist Eye Finger Ear SMS 10 1 6 Call 3 1 12 Contacts 3 5 2 1 Clock 10 1 Photos 2 8 Calculator 3 Mp3 2 Agenda 1 3 1 Alarm-clock 2 2 2 3
    20. 20 Users 5 chosen Applications 20 Shortcuts Key Shortcuts Vs Mnemonics 94% Recognition Rate
    21. Accelerometer ADXL 330 MEMS Bioplux4 Device
    22. Why Accelerometers?
    23. Accelerometer Applicable in Contextual & Explicit Human Motion
    24. Contextual Interaction
    25. Stopped Holding Picked Walking Running Stopped Movement Analysis - Amplitude Holding Time (s) Acc (g) Amp = √( x^2+y^2+z^2 )
    26. Fall Detection
    27. Explicit Interaction Explicit Interaction
    28. Mnemonical Body Shortcuts X Y
    29. Mnemonical Body Shortcuts Position – Y axis Acceleration Calibrated Acceleration Thresholded
    30. Mnemonical Body Shortcuts Position – Y axis ∫ dt ∫ dt Velocity Position
    31. Mnemonical Body Shortcuts Position + Final Rotation
    32. Mnemonical Body Shortcuts Evaluation – Default Gestures TOTAL RECOGNITION 82% Mouth - 85% Chest – 85% Navel – 90% Shoulder - 75% Neck – 100% Ear - 60% Head – 85% Leg – 80% Wrist – 85% Eye – 75% 10 users – 5 Gestures – No training – 20 Recognitions each
    33. Mnemonical Body Shortcuts Evaluation – Trainable Gestures TOTAL RECOGNITION 71% 10 users – 5 Gestures – 5x Train for each gesture – 20 Recognitions Leg - 85% Mouth – 94% Navel – 64% Neck – 95% Ear - 55% 5 most common gestures
    34. Mnemonical Body Shortcuts Discussion
      • Default Gestures
        • Good Recognition Rate.
        • Limited to 10 pre-defined gestures.
        • Users have to learn the gestures.
      • Treinable gestures
        • Lower Recognition Rate – Similar gestures are choosen.
        • Position isn’t very effective to desambiguate gestures outside x,y plan.
        • One training error spoils the recognition – outlier detection is needed.
    35. Tilt – Angle Calculation Time (s/1000) Angle (degrees) X Y Z
    36. Tilt - Centralization and Joining Y & Z X Y + Z Angle (degrees) - start position variation Time (s/1000)
    37. Tilt - Thresholding X Y + Z Angle (degrees) - start position variation Time (s/1000)
    38. Tilt - Decision LEFT RIGHT UP DOWN X Y + Z Time (s/1000) Angle (degrees)
    39. Tilt Evaluation
    40. Tilt Results - Recognition LEFT TILT 85% RIGHT TILT 94% UP TILT 75% DOWN TILT 93% TOTAL RECOGNITION 86%
    41. Future Work
      • Mobility tests on Mnemonical Body Shortcuts
      • Try a Feature Based Algorithm
      • Design a full prototype
      • ( + feedback and shortcuts)

    + Tiago GuerreiroTiago Guerreiro, 2 years ago

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