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A Hands-On Approach to Android: Gesture Detection Using the Android Virtual Machine

Our entry for the Google Science Fair is called
A Hands-On Approach to Android Gesture Detection Using the Android Virtual
Machine By Amil Merchant and Prateek Shah The basis of our research as asking the question
of Can the Android Virtual Machine be efficiently
used to detect the hand, fingertips, and palm
in order to perform gesture detection on a live camera stream
on a mobile device For the hand to be detected by the Android
camera Significant amounts of background reduction
had to be done The image must be processed efficently and
The hand’s information must be correct If the hand can be properly detected by the
Android camera, then it is probable that gestures can be extracted
from the live feed We used the basic tools for coding and developing
on the Android phones As well as the Apache Math Commons and the
OpenCV API Incorporating all of these into the Android
Virtual Machine, with OpenCV Libraries,
Android’s API, and lots of Google-ing
We were able to create our application The procedure that our app follows is first
it gets an image from the live feed then it thresholds it so only the hand gets
detected and then it performs morphology, so that only
the hand gets truly detected The next phase of the app actually begins
to process the image in order to extract the contours and find
the angles of the hand then it procedes to name these fingers based
on their probable positions and angles and then uses those contours again to find
what the center of the hand, or the palm truly is The final phase of the application was actually
drawing this information onto the original camera frame. In addition,
it begins to process those results in order to see if gestures were actually
being done by the hand. In the case that it actually was performing
a gesture, an alert would be displayed indicating that a gesture had been found. Thank You!
This is our Google Science Fair Entry. A Hands-On Approach to Android!

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