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Skinput

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A presentation on Skinput By Karan Sharma,(B.Tech - ECE 3rd Year), I.T.S. Engineering College.

Published in: Technology

Skinput

  1. 1. Appropriating Body as the Input SurfacePresented By- Karan SharmaECE – B.Tech III Year
  2. 2. Advances in ElectronicsMobile devices are becoming extra smallLimited user interaction SpaceRequirement of large user interaction spacewithout losing the primary benefit of small sizeAlternative approaches that enhanceinteractions with small mobile systems
  3. 3. Proprioception & Easily Accessible
  4. 4. Skinput is an input technology that uses Bio-acousticsensing to localize finger taps on the skin.Tap WithFingerBio-Acousticarray ofsensorsPico-ProjectorWhen augmented with aPico-Projector, the devicecan provide a directmanipulation , graphical userinterface on the body. Thetechnology was developedby Chris Harrison, Desney Tan& Dan Morris, at MicrosoftResearch’s ComputationalUser Experiences Group.
  5. 5. EEG and FNIR :Brain sensing Technologies such as Electroencephalography (EEG) andFunctional Near-Infrared Spectroscopy (FNIR) have been used by HCIresearchers to access cognitive and emotional state. This work alsoprimarily looked involuntary signals.BCI :Direct Brain Computer Interfaces (BCI’s) generally used for paralyzed stilllack the bandwidth required for everyday computing tasks and requirelevel of focus, training and concentration that are incompatible withtypical computer interaction.EMG :Electrical signals generated by muscle activation during normal handmovement through electromyography.
  6. 6. Transverse Wave Propagation :Finger impacts displace theskin, creating transverse waves(ripples). The sensor isactivated as the wave passesunderneath.Longitudinal Wave Propagation:Finger impacts createlongitudinal (compressive)waves that cause skeletalstructures to vibrate. This, inturn, creates longitudinal wavesthat emanate outwards from thebone (along it’s entire length)towards the skin.
  7. 7. Variations in bone density , size andmass and the soft tissue and jointscreate Acoustically different locations.
  8. 8. Playing Tetris :Using fingers ascontrol padConnect to anymobile devicelike iPod
  9. 9. Projection of adynamicgraphicalinterfaceFinger inputs areclassified andprocessed in RealTime
  10. 10. Sensing :A wearable Bio-Acoustic sensing array built into anarmband. Sensing elements detect vibrations transmittedthrough the body. The two sensor packages shown aboveeach contain five, Specially weighted, cantilevered piezofilms, responsive to a particular frequency range.
  11. 11. Ten channels of Acoustic data generated by three fingertaps on the forearm, followed by three taps on the wrist. Theexponential average of the channels is shown in red.Segmented input windows are highlighted in green. Notehow different sensing elements are actuated by the twolocations.
  12. 12. Designed software listens for impacts andclassifies them. Then different interactivecapabilities are bounded on differentregions.
  13. 13. Five Fingers :Classification accuracy remained high for the fivefinger condition, averaging 87.7% acrossparticipants.Whole Arm :The below-elbow placement performed thebest, posting a 95.5% average accuracy. This is notsurprising , as this condition placed the sensors closerto the input targets than the other conditions. Movingthe sensor above the elbow reduced accuracy to88.3% . The eyes free input condition yielded loweraccuracies than other conditions, averaging 85.0% .
  14. 14. Forearm :Classification accuracy for the Ten-locationforearm condition stood at 81.5% , a surprisinglystrong result.Higher accuracies can be achieved by collapsing the input locationsinto groups. A-E and G were created using a design-centric strategy. Fwas created following analysis of per-location accuracy data.
  15. 15. The testing phase took roughly 3 minutes tocomplete (Four trials total : Two participants, Twoconditions ). The male walked at 2.3 mph andjogged at 4.3 mph; the female at 1.9 mph and 3.1mph respectively.In both walking trials, the system never produced afalse positive input. Meanwhile true positiveaccuracy was 100 % . Classification accuracy was100% for male and 86% for female .In jogging this showed 100% true positive accuracy.Classification accuracy was 100% for male and83.4% for female .
  16. 16. The prevalence of fatty tissues and the density/mass ofbones tend to dampen or facilitate the transmission ofacoustic energy in the body.The participants with the three highest BMI’s (29.2, 29.6 and31.9 – representing borderline obese to obese) producedthe three lowest accuracies.
  17. 17. Skinput represents a novel, wearablebio-acoustic sensing array that wasbuilt in an armband in order to detectand localize finger taps on the forearmand hand. Results from theexperiments have shown that systemperforms very well for a series ofgestures, even when the body is inmotion which improves in further work.

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