enhancing input on and above the interactive surface
TRANSCRIPT
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Enhancing Input On and Above the Interactive Surface with Muscle Sensing
Hrvoje Benko, T. Scott Saponas, Dan Morris, and Desney Tan
Summarized & Presented by:Reem Alattas
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Combining Muscle and Touch Sensing
• Touch-sensitive surfaces and EMG provide complementary streams of information.– Touch-sensitive surfaces provide precise location
and tracking information.– EMG can detect which muscle groups, and
consequently which fingers, are engaged in the current interaction.
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Hardware and Setup
• Microsoft Surface• BioSemi Active Two EMG device– Samples eight sensor channels at 2048 Hz– 6 sensors and two ground electrodes around the
upper forearm of the dominant hand– 2 sensors on the forearm of the non-dominant
hand
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Interpretation of Muscle Signals
• Level of pressure• Contact finger identification• “Pinch” and “Throw” gestures• “Flick” gesture
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Hybrid EMG-Surface Interactions
• Pressure-sensitive painting• Finger-aware painting • Finger-dependent pick and throw • Undo flick
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Exploratory System Evaluation
• Participants: 6 (3 females)• 90 minutes• $10 compensation
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Goals
• Feasibility Validation• Reliability Assessment
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Tasks• Task 1
– Copy an image from a given paper template using the pressure-sensitive painting technique
• Task 2– Copy an image from a given paper template using the finger-aware painting
technique• Task 3
– Make a series of vertical lines across the surface, changing color with each vertical line
• Task 4– Write the numbers from 1 to 10 on the surface, executing the “undo flick” gesture
after each even number, but not after odd numbers.• Task 5
– Presented with a pile of six images on a canvas, either copy or move each image to another canvas, depending on the image category.
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Tasks
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Results
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Results• Task 1 mean accuracy = 93.9%
– All participants were able to effectively manipulate pressure to control brush darkness.
• Task 2:– All six participants completed the target drawing. – One had some difficulty reliably selecting the finger color.
• Task 3 mean accuracy = 90.9%• Task 4:
– Five out of six participants were able to reliably execute and control the “undo flick” gesture without any false positives.
• Task 5:– Three perfect executions
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Conclusion
• The proposed approach enhances the existing tabletop paradigm and enables new interaction techniques not typically possible with standard interactive surfaces.
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Questions