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Mobile Gesture Recognition using Dynamic Time Warping with Localized Template  

Choe, Bong-Whan (연세대학교 컴퓨터과학과)
Min, Jun-Ki (연세대학교 컴퓨터과학과)
Jo, Seong-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.4, 2010 , pp. 482-486 More about this Journal
Abstract
Recently, gesture recognition methods based on dynamic time warping (DTW) have been actively investigated as more mobile devices have equipped the accelerometer. DTW has no additional training step since it uses given samples as the matching templates. However, it is difficult to apply the DTW on mobile environments because of its computational complexity of matching step where the input pattern has to be compared with every templates. In order to address the problem, this paper proposes a gesture recognition method based on DTW that uses localized subset of templates. Here, the k-means clustering algorithm is used to divide each class into subclasses in which the most centered sample in each subclass is employed as the localized template. It increases the recognition speed by reducing the number of matches while it minimizes the errors by preserving the diversities of the training patterns. Experimental results showed that the proposed method was about five times faster than the DTW with all training samples, and more stable than the randomly selected templates.
Keywords
k-Means Clustering; Localized Template; Gesture Recognition; Dynamic Time Warping;
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1 R. S.-Urena, D. M.-Iglesias, A. G.-Antolin, C. P.-Moreno, and F. D.-de-Maria, "Robust ASR using support vector machines," Speech Communication, vol.49, no.4, pp.253-267, 2007.   DOI   ScienceOn
2 J. Rett and J.Dias, "Gesture recognition using a marionette model and dynamic bayesian networks (DBNs)," ICIAR 2006. LNCS, vol.4141, pp.69-80. 2006.
3 J. Liu, Z. Wang, L. Zhong, J. Wickramasuriya, and V. Vasudevan, "uWave: Accelerometer -based personalized gesture recognition and its applications," in Pervasive and Mobile Computing (PerCom), vol.5, no.6, pp.657-675, 2009.   DOI   ScienceOn
4 H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization for spoken word recognition," IEEE Transactions on Acoustics, Speech and Signal Processing, vol.26, no.1, pp.43-49, 1978.   DOI
5 S. Salvador and P. Chan, "Fast DTW: Toward Accurate Dynamic Time Warping in Linear Time and Space," Intelligent Data Analysis, vol.11, no.5, pp.561-580, 2007.
6 P. Capitani and P. Ciaccia, "Warping the time on data streams," Data & Knowledge Engineering, vol.62, no.3, pp.438-458, 2007.   DOI   ScienceOn
7 A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.
8 I. B. Ozer, T. Lu, and W. Wolf, "Design of a real-time gesture recognition system: high performance through algorithms and software," IEEE, Signal Processing Magazine, vol.22, no.3, pp. 57-64, 2005.   DOI