Enhancing input on and above the interactive surface

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Transcript of Enhancing input on and above the interactive surface

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

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.

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

Interpretation of Muscle Signals

• Level of pressure• Contact finger identification• “Pinch” and “Throw” gestures• “Flick” gesture

Hybrid EMG-Surface Interactions

• Pressure-sensitive painting• Finger-aware painting • Finger-dependent pick and throw • Undo flick

Exploratory System Evaluation

• Participants: 6 (3 females)• 90 minutes• $10 compensation

Goals

• Feasibility Validation• Reliability Assessment

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.

Tasks

Results

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

Conclusion

• The proposed approach enhances the existing tabletop paradigm and enables new interaction techniques not typically possible with standard interactive surfaces.

Questions