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Idendifying Eye Movements using Neural Networks for Human Computer Interaction

by Hema.c.r, Ramkumar.s, Paulraj.m.p
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 8
Year of Publication: 2014
Authors: Hema.c.r, Ramkumar.s, Paulraj.m.p
10.5120/18397-9658

Hema.c.r, Ramkumar.s, Paulraj.m.p . Idendifying Eye Movements using Neural Networks for Human Computer Interaction. International Journal of Computer Applications. 105, 8 ( November 2014), 18-26. DOI=10.5120/18397-9658

@article{ 10.5120/18397-9658,
author = { Hema.c.r, Ramkumar.s, Paulraj.m.p },
title = { Idendifying Eye Movements using Neural Networks for Human Computer Interaction },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 8 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number8/18397-9658/ },
doi = { 10.5120/18397-9658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:10.422971+05:30
%A Hema.c.r
%A Ramkumar.s
%A Paulraj.m.p
%T Idendifying Eye Movements using Neural Networks for Human Computer Interaction
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 8
%P 18-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrooculography based bio signals have been used and applied as a control signal in several Human Computer Interactions. EOG is a technique of recording corneal- retinal potential associated with eye movement. An HCI captures and decodes EOG signals and transforms human eye movement into actions. This paper proposes algorithms for identifying eleven eye movement signals acquired from twenty subjects using static and dynamic networks. Convolution technique is used to extract the features. These features are trained and tested with two neural networks, namely time delay neural network and feed forward neural network. The results obtained are compared with Singular Value Decomposition features for same networks. Classification accuracies varied from 90. 99% and 90. 10% for convolution features and 90. 88% and 89. 92% for SVD features using time delay neural network and feed forward neural network respectively. From the results it is observed that Convolution features using Time Delay Neural Network has better classification rates in comparison with SVD features.

References
  1. Anwesha Banerjee. 2012 Electrooculogram based Control Drive for Wheelchair realized with Embedded Processors. ME Thesis, Jadavpur University, Kolkata.
  2. Yash Shaileshkumar Desai (2013) Natural Eye Movement & its application for paralyzed patients. International Journal of Engineering Trends and Technology , Vol. 4, pp. 679-686.
  3. A. Dix, J. Finlay, G. Abowd, and R. Beale (2003) Human-computer interaction, 3rd ed. ,Prentice Hall , New York.
  4. H. Sharp, Y. Rogers, and J. Preece (2007) Interaction design: Beyond human computer interaction, 2nd ed. , John Wiley & Sons, New York.
  5. Abdullah Khan, Muhammad Awais Memon, Yusra Jat, Ali Khan, (2012) Electro Occulogram Based Interactive Robotic Arm Interface for Partially Paralytic Patients, International Journal of Information Technology and Electrical Engineering, Vol. 1.
  6. Joao Cordovil Barcia. (2010) Human electrooculography interface, M. E thesis, university of Tecnica de Lisboa, Lisboa.
  7. Nathan D S, Vinod A P and Thomas P K, (2012) An Electrooculogram based Assistive Communication System with Improved Speed and Accuracy Using Multi-Directional Eye Movements", International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, pp. 554-558.
  8. Norris G, Wilson E, (1997) The eye mouse, an eye communication device, IEEE Trans Bio Eng, vol. 14, pp. 66-70.
  9. Simpson. T, Broughton. C, Gauthier MJA, Prochazka. A , (2008) Toothclick Control Of a Hands-Free Computer Interface, IEEETrans. Biomed. Eng, vol. 55, pp. 2050-2056.
  10. Coyle ED, (1995) Electronic Wheelchair Controller Designed for Operation by Hand-Operated Joystick, Ultrasonic Non-Contact Head Control and Utterance From a small Word-Command Vocabulary, in proc of the IEE Colloquium on New Developments in Electric Vehicles for Disabled Persons, pp. 31–34.
  11. Shaikh AA, Kumar DK, Gubbi J , (2011) Visual speech recognition using optical flow and support vector machines, International Journal of Comput. Intell. Appl. , vol. 10, pp. 167-187.
  12. Reale MJ, Canavan S, Yin L, Hu K, Hung T , (2011) A multi-gesture interaction system using a 3-D iris disk model for gaze estimation and an active appearance model for 3-D hand pointing , IEEE Trans. Multimedia, vol. 13, pp. 474-486.
  13. Panicker RC, Puthusserypady S, Sun Y, (2011) An Asynchronous P300 BCI with SSVEP-Based Control State Detection, IEEE Trans. Biomed. Eng, vol. 58, pp. 1781-1788.
  14. Evans DG, Drew R, Blenkhorn P , (2000) Controlling Mouse Pointer Position Using an Infrared Head-Operated JoyStick, IEEE Trans. Rehabil. Eng. , vol. 8, pp. 107-117.
  15. A. S. EI-Sherbeny, S. Badawy, (2012) Eye Computer Interface (ECI) and Human Machine interface Applications to help Handicapped Persons, The Online Journal of Electronics and Electrical Engineering, Vol. 5, pp. 549-553.
  16. A. B. Usakli, S. Gurkan, F. Aloise,G. Vecchiato and F. Babiloni, (2010) On The Use of Electrooculogram for Efficient Human Computer Interfaces, Published by Computational Intelligence and Neuroscience, pp. 1-5.
  17. Tsai JZ, Lee CK. , Wu CM. , Wu JJ, Kao KP,(2008) A feasibility study of an eye-writing system based on electro-oculography, J Med Biol. Eng,vol. 28, pp. 39-46.
  18. Barea R, Boquete L, Mazo M, Lopez E, (2002) System for Assisted Mobility Using Eye Movements Based on Electrooculography, IEEE Trans Neural System Rehabilitation Eng, pp. 209-18.
  19. Siriwadee Aungsakun,(2012) Development of Robust Electrooculography Based Human –Computer Interface Controlled by Eight Directional Eye movements, International Journal of Physical Sciences, vol. 7, pp. 2196-2208.
  20. Güven A, Kara S, (2006) Classification of Electro-Oculogram Signals Using Artificial Neural Network, Expert Syst. Appl, vol. 31, pp. 199-205.
  21. Arslan Qamar Malik and Jehanzeb Ahmad, (2007) Retina Based Mouse Control, Journal of Electrical and Computer Engineering, vol. 2, pp. 780-784.
  22. J. Hori, K,sakano, Y. Saitoh, (2004) Development of communication supporting device controlled by eye movements and voluntary eye blinks, IEEE proceedings of EMBS, USA, pp. 4302-05.
  23. Sunmee Park, Dong Woo Kim and Hee Chan Kim, (2005) Development of Human Computer Interface Device Using Electrooculogram for The ALS Patient, in proc of Third European Medical and Bilogical Engineering Conference",pp. 20-25.
  24. Andres Ubeda, Eduardo Ianez, and Jose M. Azor?n, (2011) Wireless and Portable EOG-Based Interface for Assisting Disabled People, IEEE/ASME Transactions on Mechatronics, vol. 16, pp. 870-873.
  25. Hari Singh, Jaswinder Singh, (2012) A Review on Electrooculography, International Journal of Advanced Engineering Technology, Vol. 3, pp. 115-122.
  26. Hema. C. R, Paulraj. M. P & Ramkumar. S, (2014) Classification of Eye Movements Using Electrooculography and Neural Networks, International Journal of Human Computer Interaction, Vol. 5, Issue. 4, pp. 51-63.
  27. Guanglin Li, (2011) Electromyography Pattern-Recognition-Based Control of Powered Multifunctional Upper-Limb Prostheses, Advances in Applied Electromyography, vol. 6, pp. 99-116.
  28. A. Nagoor Kani, (2012) Digital Signal processing: Discreate Time signals and system,Published by TataMcgraw Hill, Delhi, pp. 2. 51-2. 52.
  29. Hema C. R. , Paulraj M. P. , Nagarajan R. and Sazali Yaacob, (2007) Stereo Sensors-Based object Segmentation and Location for a Bin Picking Adept SCARA Robot, Journal of Engineering Research and Education, vol. 4, pp. 11-19.
  30. S. N. Sivanandam, M. Paulraj, (2003) Introduction to articial Neural networks, Vikas Publishing House.
  31. N. P Padhy, (2005) Artificial Neural network: Artificial Intelligence and Intelligent Systems, New Delhi: Oxford University press, pp. 412.
  32. S. N. Sivanandam, S. N. Deepa (2007) Principles of Soft Computing", published by Wiley India (p) Ltd.
  33. Ramkumar. S,Hema. C. R, (2013) Recognition Of Eye Movement Electrooculogram Signals Using Dynamic Neural Networks, KJCS, vol. 7, pp. 12-20.
Index Terms

Computer Science
Information Sciences

Keywords

Electrooculography Human Computer Interaction Convolution Features Singular Value Decomposition Feed Forward Neural Network Time Delay Neural Network Multi Layer Perceptron Fast Fourier Transform.

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