Total views : 327
Gesture Recognition Algorithm: Braille-Coded Gesture Patterns for Touch Screens: Eyedroid
Objectives: The primary objective of this paper is to evaluate the correctness of the algorithm Gesture Recognition that is one of the parts of our novel method ‘Braille coded gesture pattern interaction method”; and to study the performance of Eyedroid system using the statistical methodologies. Methods/Statistical Analysis: The algorithm for gesture recognition was developed and implemented in C program to check the correctness of each step of algorithm. The gesture input parameters Swipe-Minimum-Distance, Swipe-Threshold-Velocity are defined with range of values. Varying gesture input range of values within domain and out of domain results the expected solution space. Our gesture recognition algorithm is developed for 2D touch surface. The Real time experiments were conducted for 12 blind fold subjects to observe the different versions Eyedroid-A(E-A), Eyedroid-B(E-B) of gesture recognition algorithm perceptions, being implemented in smart phones. The mean WPM for 10 trials was recorded for 12 subjects with E-A and E-B. The two tailed paired t test is performed for the mean dependent sample data of wpm. Findings: The existing 3D gesture recognition algorithm Derivative Dynamic Time Warping (DDTW) analyzed by Katarzyna Barczewska, Aleksandra Drozd, 2013 was proven to be efficient method for air fly environment. Our gesture recognition algorithm is developed for 2D touch surface found to be effective by the results of algorithm test. Gesture Recognition algorithm finds the mal gestures and the results proved that there is a significant difference in respect of blind subjects for two different Eyedroid versions. The observed data reveals E-B (mean WPM=14.95) is easy to use than E-A (mean WPM=12) as the number of words entered per minute by E-B is higher than E-A. Our Eyedroid with WPM is 14.95 is obtained to be better interactive system than the existing “Adaptive interaction Technique” system developed by Georgios Y fantidis Grigori Evreinov, 2005 confirms WPM is 12. Applications/Improvements: The implication is our Braille coded gesture pattern that enables visually challenged ones to enter more number of words than by the existing system. Our interaction method is deployable at all kinds of touchable devices.
Eyedroid-Braille Coded Gesture Pattern, Gesture Recognition Algorithm, t Test for Mobile Interaction.
- Shabnam M, Govindarajan S. Survey on the text entry methods used in touch screen mobile devices by visually challenged. International Journal of Advanced Intelligence Paradigms. In press.
- Shabnam M, Govindarajan S. Braille-coded gesture patterns for touch- screens a character input method for differently enabled persons using mobile devices. Proceedings on International Conference on Communication, Computing and Information Technology ICCCMIT. 2014; (1):1–5.
- Aslam SM, Swaminathan G. Framework of gesture for blind people usable at touch mobile phones. International Journal of Applied Engineering Reasearch. 2015; 10(17);37658–63.
- Kang H, Cho J, Kim H. Application study on android application prototyping method using app inventor. Indian Journal of Science and Technology. 2015 Aug; 8(18):1–5.
- Ulusoy M. A touch based finger-motion-adaptive control design for braille reading. Northeastern University; 2015. p. 157.
- Gallavata G, Ewerth R, Freisleben B. A robust algorithm for text detection in images. 2003 Proceedings of the 3rd International Symposium Image and Signal Processing and Analysis, ISPA. 2003; 2:611–16.
- Brownlee J. Clever algorithm: Nature inspired programming recepies. ACM Digital Library; 2011.
- Georgios Yfantidis, Evreinov G. Adaptive blind interaction technique for touchscreens. Universal Access in the Information Society. 2006 May; 4(4):328–37.
- Southern C, Clawson J, Frey B, Abowd G, Romero M. An evaluation of braille touch: Mobile touch screen text entry for the visually impaired. Proceedings of the 14thACM Conference on Hunan-Computers interactions with mobile devices and services, NY; 2012. p. 317–26.
- Silfverberg M, MacKenzie IS, Korhonen P. Predicting text entry speed on mobile phones. Proceeding of the ACM SIGCHI Conference on Human Factors in Computing, CHI’00, USA; 2000. p. 9–16.
- Prakash M, Gowshika U, Ravichandran T. A smart device integrated with an android for Alerting a Person’s health condition: Internet of things. Indian Journal of Science and Technology. 2016 Feb; 9(6):1–6.
- Hotelling S, et al. Gestures for touch sensitive input devices; 2013.
- Armitage P, Berry G, Mathews JNS. Statistical methods in medical research.
- Ott R L, Longnecker M. An introduction to statistical methods and data analysis. Nelson Education; 2015.
- Daniel WW, Wayne WD. Biostatistics: a foundation for analysis in the health sciences; 1995. p. 1–3.
- Prakash M, Gowshika U, Ravichandran T. A smart device integrated with an android for Alerting a Person’s health condition: Internet of things. Indian Journal of Science and Technology. 2016 Feb; 9(6):1–6. DOI: 10.17485/ijst/2016/ v9i6/69545.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.