Braille-Coded Gesture Patterns for Touch- Screens a Character Input Method for Differently Enabled Persons using Mobile Devices
M.shabnam and S.govindarajan. Article: Braille-Coded Gesture Patterns for Touch- Screens a Character Input Method for Differently Enabled Persons using Mobile Devices. IJCA Proceedings on International Conference on Communication, Computing and Information Technology ICCCMIT 2014(1):1-5, March 2015. Full text available. BibTeX
@article{key:article,
author = {M.shabnam and S.govindarajan},
title = {Article: Braille-Coded Gesture Patterns for Touch- Screens a Character Input Method for Differently Enabled Persons using Mobile Devices},
journal = {IJCA Proceedings on International Conference on Communication, Computing and Information Technology},
year = {2015},
volume = {ICCCMIT 2014},
number = {1},
pages = {1-5},
month = {March},
note = {Full text available}
}
Abstract
Mobile phones play an important role not only in a normal person's life but also in the lives of visually impaired and differently enabled people. This research emerges and grows gradually in the field of interaction systems between mobile phones and visually impaired persons. This paper describes the new Braille-coded gesture interaction method of communicating with a mobile device through touch screens by differently enabled people based on the concept of gesture computing and Braille Write. Using Braille codes to perform gestures on touch screen makes the visually challenged person(s) comfortable because Braille is the basis for communication. The prototype for the gesture input method was initially developed with the Android OS because Android has an open source operating system. Hence, the method is named the Eyedroid interaction technique. Essentially, Eyedroid is designed as a 'screen-layout-less system' to help the visually impaired without experiencing spatial problems. Eyedroid was developed and used by many visually challenged persons. Data were tested with a few post-hoc methods, and the random character input time was two seconds when using our Eyedroid method. The Kirkpatrick comprehensive gesture input method training evaluation models obtain a cent percent easiness from 50% of subjects, cent percent adaptability from 60% of subjects, cent percent accuracy from 65% of subjects.
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