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Toward Subtle Intimate Interfaces
for Mobile Devices
Using an EMG Controller
Enrico Costanza
Media Lab Europe
now at
MIT Media Lab
Samuel A. Inverso
Media Lab Europe
Rebecca Allen
Media Lab Europe
Outline
• Motivation: Subtle and hands-free interaction
• EMG as a solution
• An EMG-based controller
• Design approach
• Formal user study
• Conclusion
Importance of Subtlety
in Mobile Interfaces
• Mobile interaction is often in public spaces
• Subtle interfaces: do not disrupt the environment
• Intimate interaction: only for the user
• Ringing vs. vibrating alert
Importance of Subtlety
in Mobile Interfaces
• Speech recognition and evident gesturing
can be inappropriate
Design for Hands-free Interaction
Design for Hands-free Interaction
Eyeglass displays
Electromyogram (EMG)
as a Novel Solution
• Electrical signal from muscle activity
• Can measure isometric activity:
subtle or no movement
• Surface Electrodes (EKG-like)
• Non-contact sensing (future)
EMG in CHI
(Related Work)
• Prosthesis control
• Input devices for disabled users
• Affect sensing
• Music expression
EMG and Movement
Limitation or Advantage?
• EMG and movement are not always related
• Tanaka & Knapp report this as a limitation
• We think it is an advantage!
Motionless Gestures
• EMG greatest potential for mobile HCI
• Sense subtle gestures
• Example: brief contraction of the bicep
EMG-based Controller
• Self-contained in armband
• Integration with Bluetooth devices
(e.g. Phones and PDAs)
• No calibration for individual users
Design Process
The gesture should be:
• Natural to perform
• Different from normal muscle activity
User centered iterative approach:
1. Select muscle & generic gesture definition
(non-detailed description to subjects)
2. Definition refinement, model and algorithm
3. Tuning
Formal User Study
• Realistic controlled environment: subjects walked
around obstacles in trafficked walkway
• 10 subjects
• Audio stimuli and feedback
• Is training avoidable? (minimal feedback)
• Push the limit: short and long contractions
Results
• 96% correct recognition
• No false positives
• No training necessary in 7 out of 10 cases
• Cannot distinguish short and long contractions
across different subjects
Discussion
• EMG can be successfully used (96%)
• Generally no training required
• No calibration across users
Discussion
• Cannot distinguish short and long contractions
• Subjective definition of “short” and “long”
Future Work
• Test in more complex scenarios
• Measure subtleness
• Improve algorithm
• Use more muscles (alphabet definition)
Summary and Conclusion
• Subtle interaction for mobile devices
• New reason to use EMG in CHI
• Motionless gestures
• It works (96% correct recognition no false positives)