Feature-Strengthened Gesture Recognition Model based on Dynamic Time Warping

Dynamic Time Warping 기반의 특징 강조형 제스처 인식 모델

  • 권혁태 (단국대학교 컴퓨터과학과) ;
  • 이석균 (단국대학교 소프트웨어학과)
  • Received : 2014.11.28
  • Accepted : 2015.02.13
  • Published : 2015.03.31


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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