The document summarizes the Skinput technology, which turns the human body into a touch input surface. Skinput uses sensors in an armband to detect vibrations from taps on the skin. It can be used to control devices like phones and music players. The summary describes how Skinput works by sensing longitudinal and transverse waves from taps, using machine learning to classify inputs to different locations on the arm. It achieves over 80% accuracy but performance is affected by factors like BMI. Research is ongoing to improve Skinput for wider adoption.

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Nikitha Vidyadhar
NO : 32

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SKINPUTSKINPUT

4. STRUCTURE OF PRESENTATIOSTRUCTURE OF PRESENTATIO
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 Motivation
 Overview
 Principle of Skinput
 Mechanism and Processing model
 Experiment
 Advantages And Disadvantages
 Skinput in future
 Conclusion

5. MOTIVATIONMOTIVATION
Touch screens are familiar nowadays.
Certain drawbacks of them are :
 Limited interaction space
 Less accurate interactions
 Cannot make buttons and screens larger without
losing the primary benefits of small size
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6. OVERVIEWOVERVIEW
Giving input through skin.
 Skinput turns human body into touch screen input
surface.
Developed by Chris Harrison, Desney Tan, an
Dan Morris of the Microsoft Research's
Computational User Experiences Group.
 Its first public appearance was at Microsoft's
Tech Fest 2010
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7. WHAT SKINPUT DOESWHAT SKINPUT DOES??
Primary functions are
 Control audio devices.
 Make phone calls.
 Navigate browsing
systems.
 Play games.
Uses series of sensors to determine where user
tapped on skin
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8. PRINCIPLE OF SKINPUTPRINCIPLE OF SKINPUT
 It listens to vibrations in your
body.
 Skinput also responds to the
various hand gestures.
 The arm is an instrument
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9. WHAT MAKES IT WORKWHAT MAKES IT WORK??
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10. PICO PROJECTORPICO PROJECTOR
 It is also known as pocket projector or mobile
projector.
 Displays Menu.
 The system comprises three main parts:
 The Laser light source
 The Combiner optics
 The Scanning mirror
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11. BIO ACOUSTICSBIO ACOUSTICS
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
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12. TRANSVERSE WAVESTRANSVERSE WAVES
Created by the displacement of the skin from
a finger impact.
They are visible when we slow down it 14
times.
Tapping on soft regions of the arm create
higher amplitude transverse wave than
tapping on boney areas.
Sensors activated as wave passes under it.
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13. LONGITUDINAL WAVESLONGITUDINAL WAVES
Some of the energy is transmitted inwards
towards the Skelton.
Causes internal skeletal structure to vibrate.
These waves travel through the soft tissues of
the arms, exciting the bone .
It responds to mechanical excitation by rotating
rigid body.
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14. BIO ACOUSTICS : SENSINGBIO ACOUSTICS : SENSING
Each body part creates different type of vibrations
depending on:
Bones
Muscles
Tendons
These signal need to be sensed and worked upon.
This is done by wearing wave sensor armbands.
Sensing elements detect vibrations transmitted
through the body.
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15. ARMBANDARMBAND
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INSIDE VIEWINSIDE VIEW
OUTSIDE VIEWOUTSIDE VIEW

16. ARMBANDARMBAND
MECHANISMMECHANISM
 Detect vibrations transmitted through the body.
 Arm band device consists of two components:
1. Projector
2. Detector
 Armbands are placed in 2 ways.
Above the elbow
Below the elbow
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17. ARMBAND PROTYPEARMBAND PROTYPE
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 Two arrays of 5 sensing elements incorporated
into an armband.
 2 Sensor packages focus on the arm of armband
 One package was located near the Radius
other near the Ulna.

18. BLUETOOTHBLUETOOTH
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 Armband is connected to Mobile
Device or Computer via Bluetooth

19. MECHANISMMECHANISM
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.
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20. PROCESSINGPROCESSING MODELMODEL
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Projector
display image
on arm
Vibrations are
captured from
sensors
This is
connected to
Mobile Device
via Bluetooth.
A software to
match sound
frequencies to
specific skin
location is
used.
Corresponding
action is
implemented in
device
Finger taps on
arm

21. PROCESSING(PROCESSING(detaileddetailed))
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SKINPUT
21
Employ aMackieOnyx 1200F
audio interfaceto digitally
capturedatafrom ten sensors.
Connected via
FireWireto a
conventional computer
Each channel was
sampled at 5.5
KHZ(55KHZ total)by
ATmega168 processor
Datawassent to our primary
application written in JAVA

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This program perform three key functions:-
 Provides a live visualization of the data
 It segments inputs from the data stream into
independent instances(taps).
 Classified these input instances
The audio stream was segmented into individual
taps using an absolute exponential average of all 10
channels

23. SEGMENTATIONSEGMENTATION
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 After the input has been segmented ,the wave forms are
analyzed.
 Employing a brute force learning approach computing
186 features in total( includes combinationarily).
 186 features includes
 Average amplitude, SD and total energy of the waveforms in
each channel(30 features).
 Average amplitude ratios b/w channel pairs(45 features).
 Average of these ratios(1 feature).
 256 point FFT for all 10 channels (take only lower 10 values ie
(100 features).
 a rough estimation of the fundamental frequency of the signal
displacing each sensor (10 features).

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BRUTE FORCE LEARNING approach
computes186 featuresin total.
Support Vector Machine
Classify Input instancesusing Weka
MachineLearning Tool Kit.
Event associated with location is
instantiated. Interactivefeature
bounded to that event fired.

26. EXPERIMENTEXPERIMENT
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 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).
Three input groupings from the multitude of
possible location combinations to test .
From these groupings,5 experimental conditions
was derived.

27. LOCATIONSLOCATIONS
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28. TRAINING AND CLASSIFICATIONTRAINING AND CLASSIFICATION
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29. RESULTSRESULTS
FIVE FINGERSFIVE FINGERS
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Accuracy remained high for five finger condition
averaging 87.7%
When classification was incorrect, the system
believed the input to be an adjacent finger
60.5% of the time.
Ring finger constituted 63.3% of the
misclassifications.

30. WHOLE ARMWHOLE ARM
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 Below-elbow placement performed the best posting
at 95.5% accuracy.
 Moving the sensor above the elbow reduced
accuracy to 88.3 (7.2% drop).
 The eyes free input condition yielded lower
accuracies than other conditions averaging
85%(10.5% drop)

31. FOREARMFOREARM
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Classification accuracy for the 10 location forearm
condition stood at 81.5%.
Higher accuracies can be achieved by collapsing
the 10 locations into groups.

32. BMI EFFECTSBMI EFFECTS
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High BMI is correlated with decreased accuracies.
No direct relation with gender of the participants

33. WALKING AND JOGGINGWALKING AND JOGGING
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Each participant trained and tested the system while
walking and jogging on a treadmill.
3 input location were used to evaluate accuracy .
In walking trials system never produced a false input.
In jogging trials the system had 4 false-positive input
events over 6 min of continuous jogging.
Accuracy decreased to 83.3% and 60% for male
and female participants respectively.

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ADVANTAGESADVANTAGES
 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
skin put this problem disappears.
 The body is portable and always available, and
fingers are a natural input device.

36. DISADVANTAGESDISADVANTAGES
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 Band seems easy enough to slip on, people
found difficult to wear a very big band around
their arm for the day.
 Not enough research has been conducted
product to test the possible skin diseases get
from using this product.
 Very high cost which will not be affordable for
the common man.

37. SKINPUT IN FUTURESKINPUT IN FUTURE
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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.

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 With small sized Pico-projectors ,Skinput oriented
systems are emerging trend.
 Research carried out for smaller wrist band watch
sized sensor armband.
FUTURE IMPLICATIONS

39. CONCLUSIONCONCLUSION
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 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.

40. BIBLIOGRAPHYBIBLIOGRAPHY
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 http://research.microsoft.com/enus/u
m/redmond/groups/cue/skinput Official
Home Page of Skinput.
 http://www.chi2010.org Home Page of
Computer & Human Interactions
Conference, April,2010.
 http://www.chrisharrison.net/projects
/skinput Personal Homepage of Chris
Harrison.
 http://research.microsoft.com/en-
us/um/people/dan Homepage of Dan
Morris.
 SKINPUT: Appropriating the Body as
an Input Surface Chris Harrison ,
Desney Tan , Dan Morris

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