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Enabling Always-Available Input with Muscle-Computer Interfaces

Enabling Always-Available Input with Muscle-Computer Interfaces. T. Scott Saponas University of Washington Desney S. Tan Microsoft Research Dan Morris Microsoft Research Ravin Balakrishnan University of Toronto Jim Turner Microsoft Corporation James A. Landay University of Washington.

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Enabling Always-Available Input with Muscle-Computer Interfaces

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  1. Enabling Always-Available Input with Muscle-Computer Interfaces T. Scott Saponas University of Washington Desney S. Tan Microsoft Research Dan Morris Microsoft Research RavinBalakrishnanUniversity of Toronto Jim Turner Microsoft Corporation James A. LandayUniversity of Washington

  2. Mobile Computing Enables…

  3. “How the computer sees us.” Igoe & O'Sullivan

  4. Hands Busy Physically Active

  5. Muscle-Computer Interfaces

  6. Muscles Activate via Electrical Signal

  7. Muscles Activate via Electrical Signal Electrical Signal can be sensed by Electromyography (EMG)

  8. EMG for Diagnostics, Prosthetics & HCI Jacobsen, et al. “Utah Arm”

  9. EMG for Diagnostics, Prosthetics & HCI Jacobsen, et al. “Utah Arm” Costanza, et al. “Intimate interfaces in action”

  10. EMG for Diagnostics, Prosthetics & HCI Naik, et al. “Hand gestures” Jacobsen, et al. “Utah Arm” Costanza, et al. “Intimate interfaces in action”

  11. EMG for Diagnostics, Prosthetics & HCI Naik, et al. “Hand gestures” Jacobsen, et al. “Utah Arm” Costanza, et al. “Intimate interfaces in action” Wheeler & Jorgensen “Neuroelectric joysticks”

  12. Finger Gestures Detected from Upper Forearm

  13. Detecting Finger Gestures Challenging

  14. Offline Classification of Finger Gestures on a Surface Saponas, et al. CHI 2008

  15. Real-Time Classification ofFree Space & Hands Busy Gestures Pinch Mug Bag

  16. Bimanual Gesture dominant hand gesture non-dominant hand squeeze +

  17. Sensor Placed on Upper Forearm

  18. Stimulus / Response Training

  19. Gesture Classification Technique • X 6 Sensors 30 millisecond sample labeled training data machine learning • Support Vector • Machine • user specific model

  20. Gesture Classification Technique • X 6 Sensors 30 millisecond sample labeled training data machine learning Features • Support Vector • Machine • Root Mean Square (RMS) • ratios between channels Frequency Energy 10 Hz bands • user specific model • Phase Coherence • ratios between channels

  21. Gesture Classification Technique • X 6 Sensors machine learning 30 millisecond sample • user specific model Features • Root Mean Square (RMS) • ratios between channels • Support Vector • Machine Frequency Energy 10 Hz bands • Phase Coherence • ratios between channels • gesture • classification

  22. 12 Person Experiment Pinch Mug Bag

  23. Training vs Testing in Several Postures

  24. Posture Independent Pinching

  25. Bag in Hand Better Recognized

  26. Worked Well for Those Who “got it”

  27. 80% Accurate with 70 Seconds Training

  28. Portable Music Player Menus Some participants navigated menus easily Other participants found interaction difficult

  29. Limitations of Current Technique Works best for SINGLE user SINGLE session Wired Sensors with Gel and Adhesive Sitting or Standing at a Desk in the Lab

  30. Ongoing & Future Work Wireless Armband, Dry Electrodes, Cross-Session Models

  31. Ongoing & Future Work Wireless Armband, Dry Electrodes, Cross-Session Models Walking & Jogging

  32. Ongoing & Future Work Wireless Armband, Dry Electrodes, Cross-Session Models Interactive Tabletops Walking & Jogging

  33. Thanks for Listening Enabling Always-Available Input with Muscle-Computer Interfaces T. Scott Saponas University of Washington Desney S. Tan Microsoft Research Dan Morris Microsoft Research RavinBalakrishnanUniversity of Toronto Jim Turner Microsoft Corporation James A. LandayUniversity of Washington

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