Recognition of human body poses and gesture sequences with gesture description language

• Autorzy: Hachaj T. , Ogiela M. R.
• Języki publikacji: EN

Abstrakty

EN

This paper presents our new proposition of human body poses and gesture description methodology for Natural User Interfaces. Our approach is based on forward chaining inferring schema performed on the set of rules that are defined with formal LALR grammar. The set of rules is called Gesture Description Language (GDL) script while automated reasoning module with heap-like memory is a GDL interpreter. We have also implemented and tested our initial GDL specification and we have obtained very promising early experiments results.

Słowa kluczowe

EN

biomathematical modelling; pose recognition; movement sequences recognition; syntactic description; natural user interface

PL

modelowanie biomatematyczne; naturalny interfejs użytkownika

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2012
• Tom: Vol. 20
• Strony: 129--135
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Hachaj T.

• Pedagogical University of Krakow, Institute of Computer Science and Computer Methods, 2 Podchorazych Ave, 30-084 Krakow, Poland

autor

Ogiela M. R.

Bibliografia

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• [3] MITRA S., ACHARYA T., Gesture recognition: A survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews , 2007, Vol. 37, No. 3.
• [4] YEASIN M., CHAUDHURI S., Visual understanding of dynamic hand gestures, Pattern Recognition, 2000, Vol. 33, pp. 1805–1817.
• [5] ROSENBLUM M., YACOOB Y., DAVIS L.S., Human expression recognition from motion using a radial basis function network architecture, IEEE Trans. Neural Netw., 1996, Vol. 7, No. 5, pp. 1121–1138.
• [6] OBDRŽÁLEK Š., KURILLO G., HAN J., ABRESCH T., BAJCSY R., Real-Time Human Pose Detection and Tracking for Tele-Rehabilitation in Virtual Reality, Studies in Health Technology and Informatics, 2012, Vol. 173, pp. 320 – 324.
• [7] OpenNI framework homepage http://www.openni.org/
• [8] Prime Sensor™ NITE 1.3 Algorithms notes, Version 1.0, PrimeSense Inc. 2010, http://pr.cs.cornell.edu/humanactivities/data/NITE.pdf
• [9] OGIELA M.R., JAIN L.C (Eds.), Computational Intelligence Paradigms in Advanced Pattern Classification, Springer-Verlag, Berlin Heidelberg, 2012
• [10] HACHAJ T., OGIELA M.R, A system for detecting and describing pathological changes using dynamic perfusion computer tomography brain maps, Computers in Biology and Medicine, 2011, Vol. 41, pp. 402-410.
• [11] OGIELA M.R., Visualization of perfusion abnormalities with GPU-based volume rendering, Computers & Graphics, 2012, Vol. 36, Issue 3, pp. 163–169.