Skinput is a technology developed by Microsoft researchers that allows a user's skin and body to serve as an input surface for controlling devices. It uses sensors in an armband to detect vibrations from taps on the skin to determine the location tapped and translate it into a command. In initial tests, Skinput demonstrated 95.5% accuracy in detecting taps on 5 locations on the arm. It has potential for use on mobile devices, gaming, and assisting disabled individuals by providing a new way to interact without direct contact. However, further research is still needed to address potential health issues and reduce costs before wide adoption.

1. Chamakkavilamma saranam

2. PRESENTED BY:
ARAVIND.A.GHOS
H
S7CSE
09431002
Under the guidance of::
ROSHNI.R

6. CONTENTS

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.

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.

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.

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 .

22. LOCATIONS

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.

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.

36. ITZ UR TURN…

37. Jai bijosh babu
RED SALUTE COMRADES