Skinput technology

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Skinput technology

  1. 1. SKINPUT TECHNOLOGY TURNS HUMAN BODY INTO A TOUCH SCREEN INPUT INTERFACE Presented by :Neha Pevekar Prachiti Patil Nishal Shah
  2. 2. CONTENTS  Introduction  What is skinput  How it works  Principle of skinput  Advantages  Disadvantages  Application  Conclusion
  3. 3. Skinput is named because it uses the human skin. Skinput, is a sensor system. Developed by chris harrison(Mellon university),Microsoft research Reduce gizmo accessories and multiple gadgets.
  4. 4. What is skinput Giving input through skin It listen to vibrations in our body Skinput uses a series of sensors to track where a user taps on his arm. Provide an always available mobile input system Turns The body into a touch-screen interface. The arm is an instrument
  5. 5. How it works It needs bluetooth connection A microchip-sized pico projector An acoustic detector to detect sound vibrations
  6. 6. Working • When User tap on skin(Hand),the Bio-Acoustics & Sensors study the sound waves. • Variations in bone density,size and mass and the soft tissue and joints create Acoustically different locations • When a finger taps the skin ,several distinct forms of acoustic energy are produced • Londitudinal wave • Transverse wave •
  7. 7. When you tap your skin with your finger you generate transverse waves Tapping on soft regions of the arm create higher amplitude transverse wave than tapping on boney areas
  8. 8. Cause internal skeletal structure to vibrate These waves travel through the soft tissues of the arm Joints play an important role in making tapped locations acoustically distinct. This makes joints behave as acoustic filters.
  9. 9. Bio-Acoustics:sensing • Signal is sensed and worked upon • This is done by wearing sensor armband • The two sensor packages shown • Each contain five, specially weighted, cantilevered • piezo films, responsive to a particular frequency range.
  10. 10. Armband Prototype • Two arrays of five sensing elements incorporated into an armband • Two sensor packages focus on the arm of input • One package was located near the Radius other near the Ulna • Signals transmitted though denser bones
  11. 11. • Bio-Acoustics & Sensors are Connected to the mobile Bluetooth. a system use a tiny projector to display a screen onto your forearm or hand Then the menu displayed by the Pico-Projecter on user’s hand
  12. 12. • projector display image on arm finger tap on arm vibrations produced and passed through bones onto skin electronic signals produced armband in the form of music etc then detected by detector in
  13. 13. Processing Ten channels of acoustic data generated by three finger taps on the forearm, followed by three taps on the wrist. The exponential average of the channels is shown in red. Segmented input windows are highlighted in green
  14. 14. Participants: To evaluate the performance of our system, 13 participants (7 people) were recruited . These participants represented a diverse cross-section of potential ages and body types. Ages ranged from 20 to 56 (mean 38.3), and computed body mass indexes (BMIs) ranged from 20.5 (normal) to 31.9 (obese). Experimental Conditions: Three input groupings from the multitude of possible location combinations to test were selected These groupings, illustrated in Figure ,are of particular interest with respect to interface design, and at the same time, push the limits of our sensing capability. From these three groupings, five different experimental conditions are derived ,which are described as:
  15. 15. One set of gestures we tested had participants tapping on the tips of each of their five fingers. Provide clear, discrete interaction points, which are even already well-named (e.g., ring finger). In addition to five finger tips, there are 14 knuckles (five major, nine minor), which, taken together, could offer 19 readily identifiable input locations on the fingers alone.  The fingers are linearly ordered, which is potentially useful for interfaces acoustic information must cross as many as five (finger and wrist) joints to reach the forearm We decided to place the sensor arrays on the forearm, just below the elbow.
  16. 16. We selected these locations for two important reasons. First, they are distinct and named parts of the body (e.g., “wrist”). We used these locations in three different conditions. One condition placed the sensor above the elbow, while another placed it below. participants repeated the lower placement condition in an eyes-free context: participants were told to close their eyes and face forward, both for training and testing.
  17. 17. This experimental condition used 10 locations on just the forearm To maximize the surface area for input, we placed the sensor above the elbow Rather than naming the input locations, we employed small, colored stickers to mark input targets. we believe the forearm is ideal for projected interface elements; the stickers served as low-tech placeholders for projected buttons.
  18. 18. BMI Effects • Susceptible to variations in body composition • Prevalence of fatty tissues and the density/mass of bones • Accuracy was significantly lower for participants with BMIs above the 50th percentile
  19. 19. To maximise the surface area for input,we placed the sensor above the elbow,leaving the entire forearm free This increases input consistency Accuracy does drop when 10 or more locations are used The sensor can spot many different locations on the arm
  20. 20. Walking and jogging This experiment explored the accuracy of our system. Each participant trained and tested the system while walking and jogging on a treadmill. Three input locations were used to evaluate accuracy the rate of false positives and true positives was captured. In both walking trials, the system never produced a falsepositive input. In the jogging trials, the system had four false-positive input events (two per participant) over 6 min of continuous jogging. Accuracy, however, decreased to 83.3% and 60.0% for the male and female participants, respectively.
  21. 21. Single-Handed Gestures • It is a bimanual gestures • First had participants tap their index,middle, ring and pinky fingers against their thumb ten times each • Independent experiment that combined taps and flicks
  22. 22. Surface and Object Recognition • Ability to identify the Operating System • participants to tap their index finger against 1) a finger on their other hand 2) a paper pad 80 pages thick 3) an LCD screen.
  23. 23. Design and Setup • Participant performing tasks having five conditions in randomized order • One sensor package rested on the biceps • Right-handed participants had the armband placed on the left arm • Tightness of the armband was adjusted to be firm
  24. 24. PROCEDURE • Experimenter walked through the input locations to be tested • Participants practiced the motions for one minute with each gesture • To convey the appropriate tap force • To train the system, participants were instructed to tap each location ten times
  25. 25. Higher accuracies can be achieved by collapsing the ten input locations into groups.A-E and G were created using a designcentric strategy. F was created following analysis of perlocation accuracy data
  26. 26. RESULT • Classification accuracies for the test phases in the five different conditions • Rates were high, with an average accuracy across conditions of 87.6% • The correlation between classification accuracy and factors such as BMI, age, and sex
  27. 27. Research is going on to Make armband smaller  Incorporate more devices  Extend accuracy level
  28. 28. ADVANTAGES No need to interact with the gadget directly. Don’t have to worry about keypad. Skinput could also be used without a visual interface People with larger fingers get trouble in navigating tiny buttons and keyboards on mobile phones. With Skinput that problem disappears.
  29. 29. Disadvantages Individuals with visible disabilities cannot use this product. The arm band is currently bulky. the visibility of the projection of the buttons on the skin can be reduced if the user has a tattoo located on their arm If the user has more than a 30% Body Mass Index Skinput is reduced to 80% accuracy The easy accessibility will cause people to be more socially distracted
  30. 30. Applications • The Skinput system could display an image of a digital keyboard on a person's forearm. • Using Skinput, someone could send text messages by tapping his or her arm in certain places • while Walking and jogging, we can listen to music.
  31. 31. Conclusion This system performs very well even if the body is in motion in the future your hand could be your iPhone and your handset could be watchsized on your wrist.

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