Enhancing input on and above the interactive surface
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The document discusses a method of enhancing interactive surfaces through the use of touch-sensitive technologies and electromyography (EMG) for muscle sensing. It details experiments with various tasks, showing high accuracy in user interactions and the ability to detect pressure and finger engagement for complex gestures. The findings suggest potential for developing new interaction techniques beyond conventional surfaces.
Enhancing input on and above the interactive surface
- 1. 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
- 2. 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.
- 3. 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
- 4. Interpretation of Muscle Signals • Level of pressure • Contact finger identification • “Pinch” and “Throw” gestures • “Flick” gesture
- 5. Hybrid EMG-Surface Interactions • Pressure-sensitive painting • Finger-aware painting • Finger-dependent pick and throw • Undo flick
- 6. Exploratory System Evaluation • Participants: 6 (3 females) • 90 minutes • $10 compensation
- 7. Goals • Feasibility Validation • Reliability Assessment
- 8. 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.
- 9. Tasks
- 10. Results
- 11. 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
- 12. Conclusion • The proposed approach enhances the existing tabletop paradigm and enables new interaction techniques not typically possible with standard interactive surfaces.
- 13. Questions
Editor's Notes
- #2: http://research.microsoft.com/en-us/um/redmond/groups/cue/muci/ 2nd video
- #4: Non-dominant … for recognizing coarse muscle activation.
- #5: Our system uses the EMG signals to provide four primitives to applications on the interactive surface. identifying the fingers performing these gestures currently requires a two-minute training procedure identical to that described for contact finger identification
- #7: Each participant spent approximately 90 minutes interacting with our system
- #11: Pictures painted by participants in our exploratory system evaluation, where rows 1, 2, and 3 show the results of Tasks 1, 2, and 3 respectively. Task 1: copy the leftmost image using pressure-sensitive painting. Task 2: copy the leftmost image using index and middle fingers to paint in blue and green, respectively. Task 3: draw alternating blue and green lines using index and middle fingers, similar to task 2. The leftmost target images were provided to our participants on paper.