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3D Gesture Recognition for Human-Computer Interaction

Pankaj Bahekar, Nikhil Darekar, Tushar Thakur, Shamla Mantri

Abstract


The keyboard and mouse are currently the maininterfaces between man and computer. In other areas where 3Dinformation is required, such as computer games, robotics and design,other mechanical devices such as roller-balls, joysticks anddata-gloves are used. Humans communicate mainly by vision andsound, therefore, a man-machine interface would be more intuitive if itmade greater use of vision and audio recognition.Gesture recognition pertains to recognizing meaningful expressionsof motion by a human, involving the hands, arms, face, head, and/orbody. It is of utmost importance in designing an intelligent andefficient human-computer interface. The applications of gesturerecognition are manifold, ranging from sign language through medicalrehabilitation to virtual reality. The present invention provides asystem for recognizing gestures made by a moving subject. Thegesture recognizer recognizes a gesture of the human being based onmovement of the hand identified by the hand recognizer


Keywords


Hue, Saturation, Value Color Scheme (HSV), JMyron (JM), Red, Green, Blue Color Scheme (RGB).

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References


F. Axisa, C. Gehin, G. Delhomme, C. Collet, O. Robin, and A. Dittmar,

“Wrist ambulatory monitoring system and smart glove for real time

emotional, sensorial and physiological analysis,” in Proc. 26th Annu. Int.

Conf. IEEE Eng. Med. Biol. Soc. 2004 (IEMBS’04), pp. 2161–2164.

Y. B. Lee, S.W. Yoon, C. K. Lee, andM. H. Lee, “Wearable EDA sensor

gloves using conducting fabric and embedded system,” in Proc. IEEE

Conf. EMBS, 2006, pp. 6785–6788.

Y. Lee, B. Lee, C. Lee, and M. Lee, “Implementation of wearable sensor

glove using pulse-wave sensor, conducting fabric and embedded system,”

in Proc. Int. Summer School Med. Devices Biosensors, 2006, pp. 94–97.

A. Tognetti, F. Lorussi, M. Tesconi, R. Bartalesi, G. Zupone, and D. De

Rossi, “Wearable kinesthetic systems for capturing and classifying body

posture and gesture,” in Proc. IEEE EMBS, 2005, pp. 1012–1015.

A. Tognetti, N. Carbonaro, G. Zupone, and D. De Rossi,

“Characterization of a novel data glove based on textile integrated

sensors,” in Proc. IEEE Conf. EMBS, 2006, pp. 2510–2513.

L. K. Simone, E. Elovic, U. Kalambur, and D. Kamper, “A low-cost

method to measure finger flexion in individuals with reduced hand and

finger range of motion,” in Proc. IEEE Conf. EMBS, 2004, pp.

–4794.

S. Oda, M. Kyoso, A. Uchiyama, A. Takatsu, A. Hattori, and N. Suzuki,

“Development of a glove-type interface for data manipulation of the

virtual environment in the operating room,” in Proc. IEEE Int.

Annu.Conf. Eng. Med. Biol. Soc., 1998, vol. 20, pp. 1258–1259.

F. Iredale, T. Farrington, and M. Jaques, “Global, fine and hidden sports

data: Applications of 3D vision analysis and a specialised data glove for

an athlete biomechanical analysis system,” in Proc. Annu.

Conf.Mechatron. Mach. Vis. Practice, 1997, pp. 260–264.

H. H. Asada and M. Barbagelata, “Wireless fingernail sensor for

continuous long term health monitoring,” MIT Home Automation and

Healthcare Consortium, Cambridge, MA, Phase 3, Progr. Rep. 3-1, 2001.

R. Paradiso and D. De Rossi, “Advances in textile technologies for

unobtrusive monitoring of vital parameters and movements,” in

Proc.IEEE EMBS, 2006, pp. 392–395.

F. Lorussi, E. Scilingo, M. Tesconi, A. Tognetti, and D. De Rossi, “Strain

sensing fabric for hand posture and gesture monitoring,” IEEE Trans.Inf.

Technol. Biomed., vol. 9, no. 3, pp. 372–381, Sep. 2005.

M. A. Diftler, C. J. Culbert, R. O. Ambrose, R. Platt, Jr., and W. J.

Bluethmann, “Evolution of theNASA/DARPArobonaut control system,”

in Proc. IEEE Int. Conf. Robot. Autom., 2003, vol. 2, pp. 2543–2548.

Gonzalez, Rafael C. & Woods, Richard E. (2002). Thresholding. In

Digital Image Processing, pp. 595–611. Pearson Education. ISBN

-7808-629-8.

Mark S. Nixon and Alberto S. Aguado. Feature Extraction and Image

Processing. Academic Press, 2008, p. 88

T. Lindeberg (1993). "Detecting Salient Blob-Like Image Structures and

Their Scales with a Scale-Space Primal Sketch: A Method for

Focus-of-Attention"

L.R Rabiner, “A tutorial on Hidden Markov Models and Selected

Applications in Speech Recognition,” Proc IEEE, vol.77, pp.257-285,

H. Lee and J.H Kim, “An HMM-Based Threshold Model Approach for

Gesture Recognition,” IEEE Trans. Pattern Analysis and Machine

Intelligence, vol. 21, no. 10, pp. 961-973, 1999.


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