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Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces

Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces

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Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces

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  1. Demonstrating the Feasibility of Using Forearm Electromyography for Muscle-Computer Interfaces T. Scott Saponas University of Washington Desney S. Tan Microsoft Research Dan Morris Microsoft Research RavinBalakrishnanUniversity of Toronto

  2. Physical Transducers Leverage Human Expertise

  3. Need for Hands Free Input

  4. Advances in Muscle Sensing Enable Muscle-Computer Interfaces

  5. Muscles Activate via Electrical Signal

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

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

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

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

  11. Detecting Finger Gestures Challenging

  12. Sensors Placed on Upper Forearm

  13. Sensors Placed on Upper Forearm

  14. Gesture Sets

  15. Gesture Sets

  16. Lift

  17. Tap

  18. Position

  19. Pressure

  20. Lift Tap Position Pressure

  21. Gesture Classification Technique • X 8 Sensors machine learning 250 millisecond sample training data • Support Vector • Machine Features • user model test data evaluation

  22. Gesture Classification Technique • X 8 Sensors machine learning 250 millisecond sample training data • Support Vector • Machine Features • Root Mean Square (RMS) • 28 ratios between channels Frequency Energy 10 Hz bins • user model • Phase Coherence • 28 ratios between channels test data evaluation

  23. Randomized Block Design 1 2 3 4 random delay

  24. Randomized Block Design X 50 1 2 3 4 4 2 3 1 random delay random order random order

  25. 12 participants • aged 20 – 63 years (mean 46) • 8 female; 4 male • daily computer users • right-handed • 90 minutes

  26. Ten-Fold Cross-Validation Lift

  27. Ten-Fold Cross-Validation Tap

  28. Ten-Fold Cross-Validation Position

  29. Ten-Fold Cross-Validation Pressure

  30. How much training data?

  31. What are we really measuring? • Skin moving over muscle creates noise • Distant muscle contractions • Gestures are complex movements

  32. Limitations of Current Evaluation • Works best for SINGLE user SINGLE session • Offline Analysis • Approximation of sensor armband

  33. Forearm Electromyography for Muscle-Computer Interfaces Demonstrated possibility of gesture sets using pressure, position, & all five fingers Future: • Wireless & dry sensors • Dense auto-configurable band • Cross-user models • Quick compound gestures

  34. Interaction Possibilities • Virtual keyboards • Hands busy controls • 3D gestural interaction • Eye-free mobile interaction

  35. thanks! acknowledgements: SumitBasu, James Fogarty, Jon Froehlich, Kayur Patel, Meredith Skeels and our study participants … http://research.microsoft.com/users/dan/muci/ • T. Scott SaponasUniversity of Washington • Desney S. Tan Microsoft Research • Dan MorrisMicrosoft Research • RavinBalakrishnanUniversity of Toronto

  36. Lift Tap Position Pressure

  37. Labeling Training Data With Best Data stimulus rest label stimulus label

  38. Single Sample Classification stimulus stimulus Whole Trial Classification thumb 1 index 5 middle 1 winner ? ? ? ? ?

  39. Collect Pilot Data, Develop Classification Techniques, Evaluation define gesture sets collect pilot data develop classification techniques offline analysis collect test data experiment