tanuj.skinput

7/29/2019 tanuj.skinput

1/20

EVOLUTION OF TOUCHSCREEN

WHATS NEXT?

Motion tracking

is the principleused in touchscreen


2/20

Presented To: Presented By:

Mr. Sushil Rai Tanuj Yadav

CS/IT Department 09EMCCS124


3/20

CONTENTSWhat is Skinput

Principle of Skinput

How it works Experiment

Result

AdvantagesVideo explanation

Conclusion


4/20

What is SkinputA novel input technique that allows the skin to be

used as a finger input surface.

To capture this acoustic information , theydeveloped a wearable armband that is non-invasive and easily removable

Was developed by Chris Harrison (Carnegie

Mellon University), Microsoft Research.


5/20

Principle of Skinput It "listens" to the vibrations in your body.

Skinput also responds to various hand gestures.

The arm is an instrument.


6/20

How it works It needs Bluetooth connection.

It uses a microchip-sized Pico projector to display

menu.An acoustic detector to detect sound vibrations.


7/20

HOW SKINPUT WORKS

Data was then sent from the client over alocal socket to our primary application,written in Java.

Key function of application are:

Live visualization.

Segmentation of data stream.Classification of Input instances.
http://www.eldergadget.com/news/skinput-turns-your-arm-into-a-touch-screen/attachment/skinput2


8/20

ENERGY THROUGH ARM

Transverse Wave Propagation

LongitudinalWavePropagation


9/20

ARM BAND

Two arrays of five sensing elements.

Bone conduction microphones.

Microphones Placed near:

Humerus

Radius

Ulna


10/20

EXPERIMENT 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 thelocations for a minute .


11/20

LOCATIONS


12/20

Five Fingers

When classification was incorrect, the systembelieved the input to be an adjacent finger 60.5%of the time.

Ring finger constituted 63.3% percent of the

misclassifications.

RESULTS


13/20

Whole Arm

Below elbow placed the sensorscloser to the input targets thanthe other conditions.

The margin of error got double

or tripled when eyes were closed.

RESULTS


14/20

Fore Arm

Classification accuracy for theten-location forearm conditionstood at 81.5%.

RESULTS


15/20

BMI EFFECTS

High BMI is correlated withdecreased accuracies.

No direct relation with gender of theparticipant.


16/20

Advantages No need to interact with the gadget directly.

Dont have to worry about keypad.

People with larger fingers get trouble in navigatingtiny buttons and keyboards on mobile phones.

With Skinput that problem disappears.

UI will appear much larger than on screen

Ideal for anyone with little to or no eyesight

Can be used without a visual screen


17/20

A VIDEO ON SKINPUT
http://localhost/var/www/apps/conversion/tmp/scratch_4/Microsoft%20Skinput.flv


18/20

CONCLUSION

Skin put's are not available yet, but

could be in the next few years.

Since we cannot simply make buttons andscreens larger it will be an alternative

approach


19/20


20/20

Documents

tanuj.skinput