Renata C. B. Madeo
ABSTRACT
Gesture analysis has been widely used for developing new methods of human-computer interaction. The advancement reached in the gesture analysis area is also motivating its application to automate tasks related to discourse analysis, such as the gesture phases segmentation task. In this paper, we present an initiative that aims at segmenting gestures, especially considering the "units" -- the larger grain involved in gesture phases segmentation. Thereunto, we have captured the gestures using a Xbox Kinect™ device, modeled the problem as a classification task, and applied Support Vector Machines. Moreover, aiming at taking advantage from the temporal aspects involved in the problem, we have used several types of data pre-processing in order to consider time domain and frequency domain features.
- R. Bryll, F. Quek, and A. Esposito. Automatic Hand Hold Detection in Natural Conversation. In IEEE Workshop on Cues in Communication, Kauai, Hawaii, pages 1--6, 2001.Google Scholar
- B. G. Gebre, P. Wittenburg, and P. Lenkiewicz. Towards automatic gesture stroke detection. In Proceedings of the Eighth International Conference on Language Resources and Evaluation, Istanbul, Turkey, May 2012.Google Scholar
- S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, 2nd edition, 1999. Google ScholarDigital Library
- M. Hermans and B. Schrauwen. Recurrent kernel machines: Computing with infinite echo state networks. Neural Computation, 24:104--133, jan. 2012. Google ScholarDigital Library
- A. Kendon. Gesture: Visible Action as Utterance. Cambridge University Press, 2005.Google Scholar
- S. Kita, I. van Gijn, and H. van der Hulst. Movement phases in signs and co-speech gestures, and their transcription by human coders. In I. Wachsmuth and M. Frohlich, editors, Gesture and Sign Language in Human-Computer Interaction, volume 1371 of Lecture Notes in Computer Science, pages 23--35. Springer Berlin/Heidelberg, 1998. Google ScholarDigital Library
- S. Levine, C. Theobalt, and V. Koltun. Real-time prosody-driven synthesis of body language. ACM Trans. Graph., 28(5):172:1--172:10, Dec. 2009. Google ScholarDigital Library
- C. A. M. Lima and A. L. V. Coelho. Kernel machines for epilepsy diagnosis via eeg signal classification: A comparative study. Artif. Intell. Med., 53(2):83--95, Oct. 2011. Google ScholarDigital Library
- A. Majkowska, V. B. Zordan, and P. Faloutsos. Automatic splicing for hand and body animations. In Proceedings of the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer animation, SCA '06, pages 309--316, 2006. Google ScholarDigital Library
- C. H. Martell and J. Kroll. Corpus-based gesture analysis: an extension of the form dataset for the automatic detection of phases in a gesture. International Journal of Semantic Computing, 1:521--536, 2007.Google ScholarCross Ref
- D. McNeill. Gesture and Thought. University of Chicago Press, 2005.Google Scholar
- A. Phinyomark, C. Limsakul, and P. Phukpattaranont. A novel feature extraction for robust emg pattern recognition. Journal of Computing, 1:71--80, 2009.Google Scholar
- A. S. Ramakrishnan. Segmentation of hand gestures using motion capture data. Master's thesis, University of California, 2011.Google Scholar
- Z. Shi and M. Han. Support vector echo-state machine for chaotic time-series prediction. IEEE Trans. on Neural Networks, 18(2):359--372, mar. 2007. Google ScholarDigital Library
- T.-H. Tsai and C.-Y. Lin. Visual hand gesture segmentation using three-phase model tracking technique for real-time gesture interpretation system. Journal of Information Hiding and Multimedia Signal Processing, 3(2):122--134, Apr. 2012.Google Scholar
- V. Vapnik. The nature of statistical learning theory. Springer-Verlag, 1995. Google ScholarDigital Library
- A. Wilson, A. Bobick, and J. Cassell. Recovering the temporal structure of natural gesture. In Automatic Face and Gesture Recognition, 1996., Proc. of the Second International Conference on, pages 66--71, oct 1996. Google ScholarDigital Library
Index Terms
- Gesture unit segmentation using support vector machines: segmenting gestures from rest positions
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