Gesture recognition using skeleton data with weighted dynamic time warping

With Microsoft's launch of Kinect in 2010, and release of Kinect SDK in 2011, numerous applications and research projects exploring new ways in human-computer interaction have been enabled. Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is a template matching algorithm and is one of the techniques used in gesture recognition. To recognize a gesture, DTW warps a time sequence of joint positions to reference time sequences and produces a similarity value. However, all body joints are not equally important in computing the similarity of two sequences. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. Finally, we demonstrate the recognition performance of our proposed weighted DTW with respect to the conventional DTW and state-of-the- art