Hee-su KANG, Hyun-chool SHIN
Dept. of Electronic Engineering, School of IT, Soongsil Universit y, Seoul 156-743, Korea
Abstract-A smart Human-Computer Interface (HCI) replacing co nventional mouse interface is proposed. The interface is able to control cursor and command action with only hand. Four finger motions (left click, right click, hold, drag) are used to command the interface. Also the authors materialize cu rsor movement control using image processing. The measure what they use for inf erence is entropy of Electromyogram (EMG) signal, Gaussian modeling and maximum likelihood estimation. In image processing for cursor control, they use color r ecognition to get the center point of finger tip from marker, and map the point onto cursor. Accuracy of finger movement inference is over 95% and cursor contro l works naturally without delay. They materialize whole system to check its performance and utility.
Key words-EMG: vision: HCI: interface: mouse
Manuscript Number: 1674-8042(2011)02-0152-05
dio: 10.3969/j.issn.1674-8042.2011.02.13
References
[1]H.Serby, E.Yom-Tov, G.F.Inbar, 2005. An improved P300-based brain -computer interface. IEEE Transactions on Neural Systems and Rehabili tation Engineering, 13(1): 89-98.
[2]K. N. Kim, R. Ramakrishna, 1999. Vision-Based Eye-Gaze Tracking fo r Human Computer Interface. International Conference on Systems, Man, and Cybern etics, p.324-329.
[3]B.K.Ko, H.S.Yang, 1997. Finger mouse and gesture recognition system as a new Human computer interface. Computers & Graphics, 21(5 ): 555-561.
[4]E. Donchin, K. Spencer, R. Wijesinghe, 2000. The mental prosthesis: assessing the speed of a P300-basedbrain-computer interface. IEEE Tr ansactions on Rehabilitation Engineering, 8(2): 174-179.
[5]T.Yagi, 2006. Drifting and Blinking Compensation in Electro-O culography (EOG) Eye-Gaze Interface. IEEE International Conference on Systems, Man and Cybernetics, SMC′06.
[6]R.Bose, 2003. Information Theory, Coding and Cryptography. Tata McGr aw-Hill.
[7]A.Papoulis, 2002. Probability, Random Variables, and Stochastic Pro cesses. McGraw-Hill, New York.
[8]KJ.You, HC.Shin, 2009. Classifying finger flexing motions with surfa ce EMG using entropy and the Maximum likelihood method. Jounal of IEEK -SC, 46: 38-43.
[9]K.H.Moon, 2006. Hybrid Color Model for Robust Detection of Skin Colo r. KIISE Conference, p.98-101.
[10]M.S.Ko, 2009. An implementation of user interface using vision-bas ed gesture recognition. Journal of KIISE: Computer Systems and Theory , 35(1): 507-511.
[11]J.W.Son, 2009. Extraction of color information from images using gr id kernel. Journal of KIISE: Computer Systems and Theory, 34 (1): 182-187.
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