Feature-Strengthened Gesture Recognition Model based on Dynamic Time Warping


KIPS Transactions on Software and Data Engineering, Vol. 4, No. 3, pp. 143-150, Mar. 2015
10.3745/KTSDE.2015.4.3.143, Full Text:

Abstract

As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.


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Cite this article
[IEEE Style]
H. T. Kwon and S. K. Lee, "Feature-Strengthened Gesture Recognition Model based on Dynamic Time Warping," KIPS Transactions on Software and Data Engineering, vol. 4, no. 3, pp. 143-150, 2015. DOI: 10.3745/KTSDE.2015.4.3.143.

[ACM Style]
Hyuck Tae Kwon and Suk Kyoon Lee. 2015. Feature-Strengthened Gesture Recognition Model based on Dynamic Time Warping. KIPS Transactions on Software and Data Engineering, 4, 3, (2015), 143-150. DOI: 10.3745/KTSDE.2015.4.3.143.