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Chamakkavilamma saranam
PRESENTED BY:
ARAVIND.A.GHOS
       H
     S7CSE
    09431002

          Under the guidance of::
                           ROSHNI.R
CONTENTS
• Skinput is a technology that appropriates the
  human body for acoustic transmission.

• It allows the skin to be used as an input surface.

• It was developed by Chris Harrison, Densely
  Tan, and Dan Morris of the Microsoft Research's
  Computational User Experiences Group(MRCUEG)

• Its first public appearance was at Microsoft's Tech
  Fest 2010
CONTINUATION….

 Skinput allows the user to simply tap their skin in
 order to control audio devices, play games, make
 phone calls.

 It uses the sensors to determine where the user taps
  on their skin.
 It listens to vibrations in your body.


 Skinput also responds to the various hand gestures.


 The arm is an instrument.

  Arm band detects the acoustic signals and convert them
  to electronic signals which easily enable the users to
  perform simple tasks as browsing through a mobile
  phone menu, making calls, controlling portable music
  players, etc..
• Skinput, the system is a combination of two
  technologies:
1. the ability to detect the ultra low frequency sound
2. the ‘Pico’ projectors.



 Pico projector applies the use of projector in a hand
  held device.
 An acoustic detector        detects the ultra low
 frequency.
• A very small projector, basically used in gadgets.

•   The system comprises three main parts:

 The Laser light source
 The Combiner optics
 The Scanning mirror
 Study of sound waves inside living body.
 When a finger taps the skin, several distinct forms of
  acoustic energy are produced.
   Longitudinal Waves
   Transverse Waves


 These waves form the integral part of the whole
 concept of skinput.
Longitudinal Waves:

 These waves travel through the rigid tissues of
 the arm, exciting the waves, which can respond
 to the mechanical vibrations by tapping on a
 rigid body.



Transverse Waves:
 Tapping on soft regions of the arm creates higher
 amplitude transverse waves.
 These signals need to be sensed and worked
  upon.
 This is done by wearing the wave sensor arm
  band.
       OUTSIDE VIEW               INSIDE VIEW
• In Skinput, a keyboard, menu, or other
  graphics are beamed onto a user's palm and
  forearm from a pico projector embedded in an
  armband.
• An acoustic detector in the armband then
  determines which part of the display is
  activated by the user's touch.
• Their software matches sound frequencies to
  specific skin locations, allowing the system to
  determine which “skin button” the user
  pressed.
CONTINUATION….


• Currently, the acoustic detector can detect
  five skin locations with an accuracy of
  95.5%, which corresponds to a sufficient
  versatility for many mobile applications.
• The prototype system then uses wireless
  technology like Bluetooth to transmit the
  commands         to    the     device     being
  controlled, such as a phone, iPod, or computer.
Projector         Finger tap on arm
display image
on arm



                                      vibrations
                                      produced and
                                      passed
                                      through bones
                                      onto skin
Electronic
signals            then
produced in the    detected by
form of music      detector in
                   armband
Participants
    13-> 7 female, 6 male.
   Ages ranged from 20 to 56.
   Body mass indexes (BMIs) ranged
   from 20.5 (normal) to 31.9 (obese).

 Each participant was made to memorize the
 locations for a minute .
LOCATIONS
RESULTS


      Five Fingers


     When classification was incorrect, the
  system believed the input to be an adjacent
  finger 60.5% of the time.
     Ring finger constituted 63.3% percent of
  the misclassifications.
RESULTS

          Whole Arm


     Below elbow placed the
    sensors closer to the input
    targets     than the  other
    conditions.

     The margin of error got
    double or tripled when eyes
    were closed.
RESULTS



        Fore Arm


      Classification accuracy for
    the    ten-location    forearm
    condition stood at 81.5%.
B.M.I EFFECT



High BMI is correlated with
 decreased accuracies.

No direct relation with gender of
 the participant.
 Active also in Movable
 Playing Tetris: Using Fingers
                                    Environment
as Control Pad




Using Fingers, Palms, Arms as Control Any Computing Device
Can be run .Response is real time, robust & remains functional
while walking & Accuracy level is 99.5%()
 No need to interact with the gadget directly.
 Don’t have to worry about keypad.
 People with larger fingers get trouble in navigating
  tiny buttons and keypads on mobile phones. With
  skinput this problem disappears.
 The body is portable and always available, and
  fingers are a natural input device.
• Though the band seems easy enough to slip on,
  many people would not wear a very big band
  around their arm for the day.
• Not enough research has been conducted on
  this product to test the possible skin diseases or
  types of cancer, one can get from using this
  product.
• This technology might start up at very high cost
  which will not be affordable for the common
  man.
 Mobile
 Gaming
 I-pods
• An aid to paralyzed persons.
• The most profound achievement of Skinput is
  proving that the human body can be used as a
  sensor.
• A person might walk toward their home, tap
  their palm to unlock the door and then tap
  some virtual buttons on their arms to turn on
  the TV and start flipping through channels.
• Extensive Research is going on Currently on
  Skinput to
   make the armband more smaller.
   Incorporate More Devices with This System.
   Extend accuracy level.
• Skinput allows the human body as an input
  surface.
• It describes a novel, wearable bio-acoustic
  sensing array that we built into an armband in
  order to detect and localize finger taps on the
  forearm and hand.
• We conclude with descriptions of several
  prototype applications that demonstrate the
  rich design space we believe Skinput enables.
• http://research.microsoft.com/en-
  us/um/redmond/groups/cue/skin
  put Official Home Page of
  Skinput.
• http://www.chi2010.org Home
  Page of Computer & Human
  Interactions
  Conference, April,2010.
• http://www.chrisharrison.net/proj
  ects/skinput Personal Homepage
  of Chris Harrison.
• http://research.microsoft.com/en-
  us/um/people/dan Homepage of
  Dan Morris.
ITZ UR TURN…
Jai bijosh babu




                  RED SALUTE COMRADES

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1557700 634850482181137500

  • 2. PRESENTED BY: ARAVIND.A.GHOS H S7CSE 09431002 Under the guidance of:: ROSHNI.R
  • 3.
  • 4.
  • 5.
  • 7. • Skinput is a technology that appropriates the human body for acoustic transmission. • It allows the skin to be used as an input surface. • It was developed by Chris Harrison, Densely Tan, and Dan Morris of the Microsoft Research's Computational User Experiences Group(MRCUEG) • Its first public appearance was at Microsoft's Tech Fest 2010
  • 8. CONTINUATION….  Skinput allows the user to simply tap their skin in order to control audio devices, play games, make phone calls.  It uses the sensors to determine where the user taps on their skin.
  • 9.  It listens to vibrations in your body.  Skinput also responds to the various hand gestures.  The arm is an instrument. Arm band detects the acoustic signals and convert them to electronic signals which easily enable the users to perform simple tasks as browsing through a mobile phone menu, making calls, controlling portable music players, etc..
  • 10. • Skinput, the system is a combination of two technologies: 1. the ability to detect the ultra low frequency sound 2. the ‘Pico’ projectors.  Pico projector applies the use of projector in a hand held device.  An acoustic detector detects the ultra low frequency.
  • 11.
  • 12. • A very small projector, basically used in gadgets. • The system comprises three main parts:  The Laser light source  The Combiner optics  The Scanning mirror
  • 13.  Study of sound waves inside living body.  When a finger taps the skin, several distinct forms of acoustic energy are produced. Longitudinal Waves Transverse Waves These waves form the integral part of the whole concept of skinput.
  • 14.
  • 15. Longitudinal Waves: These waves travel through the rigid tissues of the arm, exciting the waves, which can respond to the mechanical vibrations by tapping on a rigid body. Transverse Waves: Tapping on soft regions of the arm creates higher amplitude transverse waves.
  • 16.  These signals need to be sensed and worked upon.  This is done by wearing the wave sensor arm band. OUTSIDE VIEW INSIDE VIEW
  • 17. • In Skinput, a keyboard, menu, or other graphics are beamed onto a user's palm and forearm from a pico projector embedded in an armband. • An acoustic detector in the armband then determines which part of the display is activated by the user's touch. • Their software matches sound frequencies to specific skin locations, allowing the system to determine which “skin button” the user pressed.
  • 18. CONTINUATION…. • Currently, the acoustic detector can detect five skin locations with an accuracy of 95.5%, which corresponds to a sufficient versatility for many mobile applications. • The prototype system then uses wireless technology like Bluetooth to transmit the commands to the device being controlled, such as a phone, iPod, or computer.
  • 19.
  • 20. Projector Finger tap on arm display image on arm vibrations produced and passed through bones onto skin Electronic signals then produced in the detected by form of music detector in armband
  • 21. Participants  13-> 7 female, 6 male.  Ages ranged from 20 to 56.  Body mass indexes (BMIs) ranged from 20.5 (normal) to 31.9 (obese).  Each participant was made to memorize the locations for a minute .
  • 23. RESULTS Five Fingers  When classification was incorrect, the system believed the input to be an adjacent finger 60.5% of the time.  Ring finger constituted 63.3% percent of the misclassifications.
  • 24. RESULTS Whole Arm  Below elbow placed the sensors closer to the input targets than the other conditions.  The margin of error got double or tripled when eyes were closed.
  • 25. RESULTS Fore Arm  Classification accuracy for the ten-location forearm condition stood at 81.5%.
  • 26. B.M.I EFFECT High BMI is correlated with decreased accuracies. No direct relation with gender of the participant.
  • 27.  Active also in Movable  Playing Tetris: Using Fingers Environment as Control Pad Using Fingers, Palms, Arms as Control Any Computing Device Can be run .Response is real time, robust & remains functional while walking & Accuracy level is 99.5%()
  • 28.  No need to interact with the gadget directly.  Don’t have to worry about keypad.  People with larger fingers get trouble in navigating tiny buttons and keypads on mobile phones. With skinput this problem disappears.  The body is portable and always available, and fingers are a natural input device.
  • 29. • Though the band seems easy enough to slip on, many people would not wear a very big band around their arm for the day. • Not enough research has been conducted on this product to test the possible skin diseases or types of cancer, one can get from using this product. • This technology might start up at very high cost which will not be affordable for the common man.
  • 30.  Mobile  Gaming  I-pods • An aid to paralyzed persons.
  • 31. • The most profound achievement of Skinput is proving that the human body can be used as a sensor. • A person might walk toward their home, tap their palm to unlock the door and then tap some virtual buttons on their arms to turn on the TV and start flipping through channels. • Extensive Research is going on Currently on Skinput to make the armband more smaller. Incorporate More Devices with This System. Extend accuracy level.
  • 32.
  • 33. • Skinput allows the human body as an input surface. • It describes a novel, wearable bio-acoustic sensing array that we built into an armband in order to detect and localize finger taps on the forearm and hand. • We conclude with descriptions of several prototype applications that demonstrate the rich design space we believe Skinput enables.
  • 34. • http://research.microsoft.com/en- us/um/redmond/groups/cue/skin put Official Home Page of Skinput. • http://www.chi2010.org Home Page of Computer & Human Interactions Conference, April,2010. • http://www.chrisharrison.net/proj ects/skinput Personal Homepage of Chris Harrison. • http://research.microsoft.com/en- us/um/people/dan Homepage of Dan Morris.
  • 35.
  • 37. Jai bijosh babu RED SALUTE COMRADES